# Sucana — Full Content > Sucana fixes broken sales systems for founders with premium offers who generate leads but don't convert them into clients. Website: https://sucana.ai Founded: 2025 Founders: Virgil Brewster (Co-Founder, Strategy & Marketing), Victor Chazarra (Co-Founder, Agency Operations), Vinod Sharma (Co-Founder, Development & Engineering) --- ## How I Save 2 Hours a Day on LinkedIn DMs with Claude URL: https://sucana.ai/blog/how-i-save-2-hours-linkedin-dms-claude Published: 2026-04-15 Author: Virgil Brewster Tags: ai, automation, linkedin How do I save 2 hours a day on LinkedIn DMs with Claude? Claude reads every new DM, triages by urgency, drafts a reply in my voice, and sets the follow-up reminder. I approve before anything sends. Two hours back every morning, same quality DMs as before. The Morning Inbox That Broke Me 6am. Bali sun barely up. Coffee. I open LinkedIn. 10 new DMs from overnight. 9 of them start the same way. "Hi Virgil, I came across your profile and was impressed by your work at Sucana." Same opener. Same pitch three lines down. You can smell the template. 1 of them is a real human. She asked a question about Sucana's onboarding that nobody could ask without reading my latest post. Guess who got a reply. That's the whole game in one morning. Every marketer sending 500 DMs a week thinks they're clever. Their reply rate is probably under 1%. Quality DMs still win. But the problem with quality DMs is the admin around them. Finding the right prospect. Remembering what they said last month. Knowing when to follow up. Knowing what to follow up with. Two hours a day on LinkedIn isn't writing. It's tracking. Claude doesn't write my messages. Claude handles the tracking. That is the whole trick. Why I Did This For two years I did LinkedIn DMs the old way. Inbox open in one tab. Notion in another. Calendar in a third. Every morning I'd scroll, remember, copy, paste, schedule, repeat. By 8am I was drained and the writing had not started. Then I ran the math. 2 hours a day, 5 days a week, 50 weeks a year. 500 hours. A quarter of my working year gone to admin that nobody sees and nobody pays for. Victor, my co-founder, said it first. "You are the business. If you are stuck in DM admin, the business is stuck." So I built a stack where the admin disappears and my time goes back into the one thing that moves the needle. Real conversations with real people. What I Built A two-part stack. Kondo handles LinkedIn. It pulls every unread thread, tags it with a stage, keeps the full history searchable, and syncs in real time. No browser tab scraping. No Chrome extension surprises. Claude Code sits on top of Kondo. Every morning I tell it "pull the inbox." Claude reads every new thread. Sorts them by stage and urgency. Tells me which 5 to reply to first. Drafts each reply in my voice using the thread history. Sets the follow-up reminder for the ones that need one. I never send a DM Claude wrote without reading it. Claude drafts. I approve, rewrite, or kill. I hit send. That loop is the product. Before You Start You need four things before anything runs. A LinkedIn account with real thread history. Brand new accounts get rate-limited and shadow-flagged when you try to pull inbox data. Use the account you already post from. A Kondo account connected to that LinkedIn. Kondo is a LinkedIn DM tool that pulls your inbox, tags threads by stage, and exposes the data Claude reads. Free tier works for getting started. Claude Code installed on your laptop. Not the chat app. The CLI tool that runs inside your terminal and can read files. If you have never used it, the setup is one install command. ( Here is why I run my marketing on Claude instead of ChatGPT. ) A voice file. One markdown file with your banned words, cringe phrases, real example sentences, and the words you love. Without it, Claude drafts stay generic. With it, they sound like you wrote them tired on a Monday. Skip any of the four and the stack does not work. Step 1: Connect Kondo to Your Inbox Open Kondo. Sign in with LinkedIn. Let it pull your inbox. First sync takes 10 to 15 minutes depending on how many threads you have. Open Claude Code in your terminal. Point it at the folder where you want the DM workflow to live. I use ~/dm-workflow . Type the prompt: I want you to read my LinkedIn DM inbox from Kondo. Show me all unread threads with: - The person's name and headline - The last message they sent - Date of the last message - Current thread stage (if Kondo has one tagged) Sort by date, newest first. Claude pulls the inbox. You see your threads in the terminal. That is the entire integration. No code you write. Claude does it. Step 2: Triage Every Thread By Urgency Kondo lets you tag threads, but manually tagging 200 threads is the opposite of saving time. Let Claude do it. Triage is about urgency, not labels. Every thread is in one of four states. Is there a question waiting on me? Is the thread stale (no activity in 7+ days)? Is this a new lead I have not answered yet? Is this a follow-up I promised and missed? Type the prompt: For each thread in my Kondo inbox, read the last 5 messages. Label each thread with one of these states: - WAITING_ON_ME (they asked, I owe a reply) - STALE (no activity in 7+ days, was warming up) - NEW_LEAD (first message to me, not answered) - FOLLOWUP_DUE (I said I'd circle back and the date has passed) - PARKED (nothing to do right now) Update the Kondo tag on each thread so future-me can see the state at a glance. Then show me a count of threads in each state. Claude reads every thread. Tags every one. Shows you a count. The first time I ran this I had 214 threads. It took 4 minutes. The summary was brutal. 140 PARKED. 50 STALE. 18 WAITING_ON_ME. 6 NEW_LEAD. I had 24 threads that needed action. The other 190 were noise. That count in one screen replaced an hour of scrolling. Step 3: Pull the Morning Priority List Not every thread on that list needs a reply today. Claude sorts. Type the prompt: Show me the 5 threads I should reply to first this morning. Rules: - WAITING_ON_ME threads with a direct question: top of list - FOLLOWUP_DUE threads where I promised a specific date: next - NEW_LEAD threads from the last 48 hours: next - Anything older than 10 days in STALE: deprioritize (may be dead) For each thread, show me the person, the last message, and one sentence on why it's a priority today. Claude gives you 5 names. 5 reasons. You know exactly where to start. Everything else goes in a backlog you deal with after the 5 priority threads are done. Step 4: Draft the Replies This is where most people get nervous. Let AI write my DMs? Really? No. Claude drafts. I approve. Type the prompt: For each of the 5 priority threads, draft a reply. Rules: - Read my voice file at ~/dm-workflow/voice.md and match my tone - Reference something specific from the thread (their last message, a post they shared, a name they mentioned) - Maximum 3 short paragraphs - No "Hope you're well". No "I came across your profile". No template openers. - If I already answered their question once, do not answer it again. Move the thread forward with a new question or a specific next step. Output one draft per thread under the person's name. Claude drafts. You read. Most need 30 seconds of editing. Some need a full rewrite. One or two you approve as is. This is the part I kept for myself. Every word that goes out. Every send button. Every judgment call on whether this person is worth another message. Step 5: Approve, Rewrite, or Kill Look at each draft. Three choices. Approve. It sounds like you. Send it. Rewrite. It is close. Fix the parts that do not sound like you, add the specific detail Claude missed. Send it. Kill. The thread is dead or the draft is too generic to save. Archive the thread or mark it as PARKED. Track this. After a week you will see a pattern. If Claude's first drafts are getting killed more than 30% of the time, your voice file is too thin. Go back and add more examples of how you really talk. If approval rate is above 70%, your voice file is doing its job. Step 6: Set the Follow-Up Reminders Most DM stalls happen here. You replied. They replied. You forgot to reply back. Two weeks later the thread is cold. Type the prompt: For every thread where I just sent a reply, create a Kondo reminder: - If I asked a question: remind me in 3 days if they haven't replied - If I suggested a call: remind me in 5 days if they haven't booked - If I shared something useful: remind me in 7 days to follow up Add a note to each reminder so future-me knows what we were discussing. Claude writes the reminders into Kondo. Kondo nudges you when the time comes. You never forget a thread again. Step 7: End-of-Week Review Friday afternoon. Pull a week summary. Type the prompt: Summarize my LinkedIn DM activity from the last 7 days: - Total threads I replied to - New threads that became qualified conversations (question asked, call suggested) - Threads I killed and why - Any prospect who asked for a call - My approximate reply rate on cold first messages I sent Output it as a one-page summary I can paste into my weekly review. Claude writes the summary. Ten minutes of reading tells you what worked and what did not. That one screen replaces an hour of scrolling back through threads. What I'd Do Differently Build the voice file first, not last. The first week I ran this stack my voice file was four lines. Claude drafts sounded like a LinkedIn content coach. I rewrote every one. I finally sat down and wrote 3 pages of how I really talk. 20 real example sentences. The next week's drafts were 70% sendable. Start with Kondo before writing any Claude prompts. I spent two days trying to get Claude to scrape LinkedIn directly. Then I found Kondo. Kondo does the integration part. Claude does the thinking part. Do not try to make Claude do both. Keep the approval loop forever. My reply rate on AI-drafted DMs without human review would drop to zero in a month. Prospects know. The human in the loop is not slowing you down. It is the only reason the messages work. The Stack Tool What It Does Cost Kondo Pulls and tags the LinkedIn inbox, stores thread history, sets reminders Free tier to start Claude Code Reads the inbox, drafts replies, sorts priority, writes summaries $20/mo standard, $200/mo max LinkedIn The platform everything runs on Free Voice file One markdown file of your tone and examples Zero, you write it Total: $20 a month plus whatever Kondo tier you pick. I run on $20 Claude Code and Kondo free for the first three months. Files ~/dm-workflow/ voice.md (your tone, banned words, example sentences) prompts/ pull-inbox.md (Step 1 prompt) triage.md (Step 2 prompt) priority-5.md (Step 3 prompt) draft-replies.md (Step 4 prompt) set-reminders.md (Step 6 prompt) weekly-review.md (Step 7 prompt) logs/ weekly-YYYY-MM-DD.md (Friday summaries, one per week) Seven prompts. One voice file. One folder. That's it. Frequently Asked Questions How long does setup take? One afternoon if you already have Kondo and Claude Code installed. Two or three days if you are starting from zero. The longest step is writing the voice file. The rest is connecting accounts and saving the seven prompts. Can Claude send my DMs automatically? No, and it should not. Automated sending is what killed bulk DM campaigns. Reply rates tank the moment the recipient smells automation. The winning model is Claude drafts, you approve, you send. The human stays on the send button. Does this work for cold outreach? Not for the first message. Claude drafts to strangers are too smooth, and cold prospects can tell. For cold outreach I write the first message myself. Claude handles the follow-ups once there is thread history. What happens if LinkedIn changes their API? Kondo handles the LinkedIn side. If LinkedIn changes anything, Kondo updates. You do not touch code. That is the whole reason to use Kondo instead of scraping yourself. How do I write the voice file? Open a blank markdown file. Write three sections. Words I love. Words I hate. Banned phrases. 20 real sentences I have said or written. Three short paragraphs that sound exactly like me. Keep it under 2 pages. Add to it every time Claude drafts something that does not sound like you and tell Claude what it missed. Is Kondo the only way to do this? No, but it is the cleanest. You could use LinkedIn's own export, or a Chrome extension, or paste threads into Claude by hand. All of those break or eat more of your day than the tool was supposed to save. Kondo charges money in exchange for one less thing to worry about. That trade is worth it when your time is the bottleneck. What if Claude's drafts sound wrong? That is a voice file problem, not a Claude problem. Open your voice file. Add 10 more example sentences. Add the specific phrases Claude keeps using that you would never say. Re-run the draft prompt. Quality jumps. If you skip the voice file, the drafts will always sound like a generic LinkedIn ghostwriter. How many hours a week does this save? I save two hours every weekday. Ten hours a week. Before this, I was answering DMs after dinner because morning time was burned on admin. Now my DMs are done by 7am. Your number will be different. The ratio stays the same. Admin time drops to near zero. Writing time stays the same. Total time drops by around 75%. Will prospects know I used AI? If you keep the approval loop, no. Every message is something you wrote or rewrote. Claude is drafting, you are sending. If you skip the approval loop and send Claude's raw drafts, prospects will know within two messages. They can smell it. Does this work with other tools besides Kondo and Claude? The pattern works with any inbox tool that has an API and any LLM that can read files. I picked Kondo and Claude because they are what I use. If you already have Linear or Airtable or Notion wired to your LinkedIn, adapt the prompts to your setup. The only non-negotiable is the human-in-the-loop approval. Everything else is a tool swap. Want the Full Prompt Library Reply to my newsletter at sucana.ai/newsletter and I will send you the folder. Seven prompts, one voice file template, ready to paste. --- ## Is AI Replacing Marketers? What the Data Actually Shows URL: https://sucana.ai/blog/ai-replacing-marketers Published: 2026-04-03 Author: Virgil Brewster Tags: ai, analytics, agencies Is AI Replacing Marketers? If you ask will AI replace marketers, the short answer is no. But the job is changing fast. AI is doing the Monday data pulls, the weekly client reports, and the fifteenth ad copy variation. In PPC agencies, those tasks ate hours every week. Now they take minutes. The shift in AI for performance marketing is happening across every ad platform. The people who know what the numbers mean are more needed than ever. AI replaces the tasks. Not the thinking. Victor my co-founder still runs every campaign. AI pulls his numbers. He decides what to kill and what to scale. Meta Took the Media Buyer's Job Description In March 2026, Meta rolled out the latest Advantage+ updates. Creative sequencing, image-to-video, placement selection, brand consistency. All automatic now. I sat with Victor and we went through the list. "That used to be my Tuesday," he said. Picking placements, testing sequences, formatting creatives. But then I asked him something. "So what do you actually do now?" He did not hesitate. "I figure out why the campaign stopped working. I read the client's business. I decide when the algorithm is wrong." Meta can sequence your creatives. It cannot read the room when a client's launch flopped and the messaging needs to change by Friday. That part is still human. That part is still Victor. The job changed. It did not disappear. The data backs this up. It says something most agency owners do not expect. 60% Fired Based on a Guess Harvard Business Review surveyed 1,006 executives in January 2026 (HBR, "Companies Are Laying Off Because of AI's Potential, Not Its Performance"). 60% of companies that cut marketing staff did it because they expected AI to do the work. Only 2% cut because AI was already doing it. Read that again. Most of the layoffs are based on a guess. Klarna cut 40% of their team. Service quality dropped. They rehired staff months later (HBR, Bloomberg). Nobody wrote that headline. I see the same pattern in PPC agencies. The big holding companies cut 13,900 jobs in 2025 (Challenger, Gray & Christmas). Dentsu, WPP, IPG. But the agencies I talk to, the small ones with 2 to 8 people, are not cutting heads. They are just not hiring new ones. That is the quiet shift nobody tracks. Juniors Got Hit First Stanford's Digital Economy Lab studied millions of payroll records ("Canaries in the Coal Mine," August 2025). Workers aged 22 to 25 in AI-exposed roles saw a 16% drop. Workers over 30 in the same roles grew 6 to 12%. The jobs are not gone. They moved up the experience ladder. I see it at Sucana. The junior work Victor used to hand off to a freelancer? AI does it now. Junior PPC roles are shrinking. The ones where you pull search term reports and pause bad keywords. AI does that now. But the people who know why a search term matters? More in demand than before. Anthropic measured how much of marketing work AI can touch ("Labor Market Impacts of AI," March 2026). Marketing scored 64.8 percent. Fifth highest out of 800 jobs. Touching the work is not replacing the person. It means the job description changed. What a PPC Agency Looks Like Now I told Victor and Vinod my co-founders: I want Sucana to run with 10 people maximum. I wrote about how we are building an AI-powered marketing agency around that idea. I do not think we even need that. The rest is automation. Every inch. Two years ago a PPC agency doing $500K in managed spend needed five to seven people. From what Victor and I saw, that was the standard. Account manager, two media buyers, a reporting person, a junior for data entry. Maybe a creative coordinator. Now the same agency runs with three. One senior who talks to clients. One media buyer who runs campaigns with AI for data and copy. One person who manages the tools. The work did not shrink. The team did. Victor sees it with every agency he talks to. "The ones who kept the right three people are printing money." "The ones who fired everyone and let AI run it are losing clients." That is the split I see every day. Smart humans with AI versus no humans at all. Ad Copywriters Got Hit First Bloomberry analyzed 180 million job postings ("I Analyzed 180M Jobs," 2025). Writing roles dropped 22 to 28 percent. Graphic design dropped 33 percent. In PPC, the specific tasks that disappeared: Ad copy variations: AI writes 15 headline variations in seconds. No PPC manager misses this work. I used to watch Victor spend an hour on this. Weekly client reports: I built an agent that automates client reporting with AI by pulling from three platforms and formatting a client deck. It does in 40 minutes what took Victor 10 hours per week. Search term cleanup: AI scans the report and flags the garbage keywords. The junior who used to do this manually is gone at most agencies I talk to. First-pass budget moves: The algorithm handles basic budget shifts better than a junior ever did. Victor still overrides it when the data looks wrong. That is the difference. These were tasks, not careers. The people who only did these tasks got replaced. The people who did these tasks plus the thinking behind them still have their jobs. Victor's Job Got Harder and Better I built Sucana around a bet: the tools get smarter, but the people who know what to do with them get more valuable. So far that bet is paying off. PPC strategists who understand the client's business are in higher demand. AI cannot sit in a meeting and read the room. Media buyers who override the algorithm are worth more. Victor does this every week. Advantage+ suggests something, he says no. Client managers who turn numbers into decisions. The report is automated. The conversation about what to do next is not. AI ops people who build and fix the systems. This role did not exist three years ago. Now every agency with five clients needs one. PwC found that AI skills command a 56% wage premium (2025 Global AI Jobs Barometer, ~1 billion job ads analyzed). Up from 25% the year before. If you want to know which ones matter most, I broke down the AI skills every marketer needs in 2026 . The people who learned the tools are getting paid more. Not less. What I Tell Agency Owners I tell every agency owner the same thing. Do not fire anyone. Look at what your team does every week. Which tasks can AI do faster and cheaper? The answer is usually reporting, data pulls, copy variations, and basic QA. I wrote a full guide on AI automation for agencies that walks through this step by step. That is 10 to 15 hours per week per person. Those hours come back. Then your three-person team does what seven people used to do. I run Sucana with three people. Victor runs campaigns. Vinod my co-founder builds the product. I handle marketing and strategy. AI does the rest. Vinod said it best when we were looking at our analytics tool. "Even though I build this, I would never be able to use it like Victor does." "Victor knows what to ask for, what to look for." That is domain knowledge. AI does not have it. The person who spent ten years in PPC has it. Keep the key players and give them better tools. The headcount stays small on its own. Frequently Asked Questions Will AI completely replace marketers? No. AI replaces specific tasks inside marketing roles, not the roles themselves. The thinking, client relationships, and business reading still need humans. What changes is how many people you need for the execution work. Which marketing jobs are most at risk from AI? Entry-level execution roles took the biggest hit. Junior copywriters, report builders, data entry people, and basic QA roles. Any task that follows the same steps every week is at risk. Roles that require judgment and client communication are growing. Are PPC agencies laying off staff because of AI? Large holding companies cut 13,900 jobs in 2025 (Challenger, Gray & Christmas). Small agencies are not firing, but they stopped hiring for junior roles. The quiet shift: fewer people, higher revenue per head. What PPC skills are safe from AI? Client strategy, creative direction, and the ability to override the algorithm when it is wrong. Victor my co-founder overrides Advantage+ every week. That judgment call is the job now. How are PPC managers adapting to AI? Most use AI for data pulls, report formatting, and copy variations. The ones who adapted fastest built their own workflows instead of waiting for tools. CoSchedule found 74% of marketers use AI daily (State of AI in Marketing 2025, n=1,005). Is AI better than humans at writing ad copy? AI writes faster. Humans write better when it matters. For the fifteenth headline variation, AI wins. For the first headline that defines the angle, a human who knows the client writes it. Victor feeds AI his best ads and gets variations. He still picks the winners. Will entry-level marketing jobs disappear? They are shrinking, not disappearing. Stanford found a 16% employment drop for workers aged 22 to 25 in AI-exposed roles. The path into marketing is changing. Learning AI tools early is now part of the entry requirement. How much time do PPC marketers save using AI? Most save 10 to 15 hours per week. Reporting alone used to take Victor 10 hours weekly for five clients. Now it takes 40 minutes. CoSchedule found 83% of marketers report increased productivity (State of AI in Marketing 2025, n=1,005). Are companies rehiring after AI layoffs? Some are. Klarna cut 40% of their team, then rehired staff when quality dropped. HBR found only 2% of companies cut staff based on actual AI results. The rest guessed. Some guessed wrong. What does an AI-native PPC agency look like? Three to five people running what used to need ten. One strategist, one senior media buyer, one AI ops person. Revenue per person is higher. Client count per person is higher. The tools do the repetitive work. The humans do the thinking. --- ## AI Automation for Marketing Agencies: Where to Start URL: https://sucana.ai/blog/ai-automation-agency Published: 2026-04-01 Author: Virgil Brewster Tags: ai, agencies, automation Where Should Agencies Start With AI Automation? Marketing agencies should start AI automation with the single repeatable task that eats the most hours every week. For most agencies, that is client reporting or campaign data pulls. The best first task is data-driven, low-judgment, and takes 2 to 4 hours per cycle. Automating the right first task in an AI automation agency setup pays for itself in under 30 days. I spent three months automating the wrong things before I figured this out. The boring work is where AI delivers. Strategy, creative, client calls, those still need a human. But that weekly report your team dreads? That is your starting point. Victor's MacBook Air Couldn't Keep Up Victor, my co-founder, was running AI workflows on his MacBook Air. Pulling campaign data, generating reports, running Claude in the background. The Air kept choking. One day he called me. "I'm buying a MacBook Pro. The Air can't handle it." Not a fancier tool. Not a new subscription. A more powerful machine. That conversation changed how I thought about AI automation. Everyone talks about which software to use. Nobody talks about whether your hardware can run it. Victor needed his AI workflows running while he was on client calls. The Air froze every time he opened more than two things. He bought the Pro. The workflows stopped crashing. His Monday mornings got three hours shorter. That is the part nobody writes about. AI automation is not just software. It is compute, infrastructure, and the boring decisions that keep everything running. If your machine can't run the tools, nothing else matters. Here is what I learned building this, step by step. The Real Cost Nobody Talks About The first question every agency owner asks is "how much does this cost?" The honest answer: more than the tool costs and less than you think. Here is what we spend at Sucana. API costs for Claude run between $20 and $80 per month depending on usage. That covers the AI processing for reporting, email triage, and content workflows. A VPS for running always-on agents costs $20 to $50 per month. The tools themselves are cheap. The time to build them is not. I spent two full weeks setting up our first reporting agent. I wrote the full story in my guide on building an AI-powered marketing agency . That is two weeks of founder time not spent on sales or product. If you bill yourself at $150 per hour, that is $12,000 in opportunity cost for one agent. The hidden cost is maintenance. An API changes, your agent breaks. A prompt that worked last month drifts and starts giving worse output. I spend about 3 hours per week maintaining our agents. That number is averaged over 8 weeks of running 5 production agents across Victor's agency. That is the number nobody includes when they talk about AI automation. Budget for it or get surprised by it. The First Task Worth Automating I wasted time automating things that did not need it. Here is what I look for now. I look for tasks that repeat. Same thing, same way, every week. Client reporting is a perfect example. Same data sources, same format, same delivery day. The inputs need to be clean and organized. Campaign metrics, CRM records, analytics dashboards. If the input is messy, the output is worse. And it needs to eat real time. Fifteen minutes? Leave it. Go after the ones that eat 2 to 4 hours per cycle. That is where automation pays for itself in weeks. Last thing: the decisions are routine, not strategic. "Pull last week's spend and format it" works. "Should we shift budget from Meta to Google?" does not. At Sucana, the first thing I automated was weekly client reporting. I documented the full process in my guide on building AI workflows for your marketing team . Victor, my co-founder, spent every Tuesday pulling data from four platforms, combining it in a spreadsheet, and formatting a PDF. Four hours, every single week. Now an agent pulls the data, formats the report, and sends a draft for Victor to review. He spends 20 minutes instead of four hours. Start there. The task everyone does but nobody enjoys. Building Your First Agent You do not need to write code. I built our first agents in Claude Code without traditional programming. But you do need to think about architecture. Pick one task from the previous section. Just one. Do not try to automate five things at once. I cover the full rollout approach in my marketing team AI adoption strategy . Then map the loop. Every agent runs one. Something kicks it off, maybe a schedule, maybe you pressing a button. The AI does its thing. It spits out a result. Then you check it. That last step is the one people skip. Here is what our reporting agent looks like in practice. Every Tuesday at 8am, a cron job triggers the agent. It pulls spend and conversion data from Google Ads, Meta Ads, and the CRM. Then it runs this prompt: Read the attached campaign data for [client name]. Compare this week to last week. Flag anything where spend changed more than 15% or conversions dropped. Format as a one-page summary with a recommendations section. The agent writes the report and drops it in a shared folder. Victor reviews it in 20 minutes, adds his notes, and sends it to the client. That last part is critical. Never let an agent run without human review in the beginning. I learned this when our email triage agent started marking client invoices as spam. Took two days to notice. We measured these numbers across Victor's agency over four weeks, comparing manual task completion times against the same tasks run through our AI workflows. For tools, here is what we use. Claude handles the AI processing. A database stores data and agent memory so the agent remembers previous runs. Connectors link everything together so the agent can pull from one tool and push to another. You do not need all of that on day one. Start with Claude and a simple prompt that does one thing well. Add infrastructure as the task gets more complex. The Risks That Kill Margins AI automation is not set-and-forget. That is the biggest lie in the space right now. API changes break things overnight Our reporting agent stopped pulling cost data one morning. The Google Ads API had updated and our connection format no longer matched. Took a full day to diagnose and fix. Prompt drift kills quality I noticed our client summaries getting blander over two months. Same prompt, same structure, but the output kept getting more generic. Traced it back to a model update that shifted how Claude handled the formatting instructions. The prompt that was tuned for one context slowly stops working in another. You do not get an error message. You just get worse results. Silent failures are the worst The agent looks like it is working. Reports go out on time. But the numbers are wrong because a data source changed format. I caught one of these when a client asked why their ROAS looked different from their own dashboard. The agent had been pulling stale data for two weeks. Scope creep makes maintenance worse I went from one agent to five in three weeks. The maintenance almost ate the time I saved. Each one needs monitoring, updating, and fixing when something breaks. How I protect against all of this: pin API versions so updates do not break things silently. Review agent output weekly, not monthly. Set up alerts for when output changes shape. And keep a log of every agent, what it does, and when it last broke. How We Structured the Architecture Most people treat AI as one chatbot that does everything. That does not scale. At Sucana, I built separate agents for separate jobs . One handles my calendar, email, and messaging. Another handles deployments, automation pipelines, and system monitoring. A third one is just memory: it stores what the team knows so any agent can look it up. Each agent has its lane. Same idea as not making one person do every job. When the personal agent needs campaign data, it pulls from the memory agent. When the infrastructure agent deploys something, it logs the change. They do not step on each other. All agents connect through a shared protocol and read from the same database. Not scattered across ten different tools. One source of truth. One AI assistant that does everything breaks in unpredictable ways. Small agents with clear jobs let you fix the one that broke without touching the rest. Start with one agent. Name it. One job. When it runs clean, build the next one. Frequently Asked Questions How much does AI automation cost for a marketing agency? Expect $50 to $150 per month in direct costs for a small agency. That range is based on our actual monthly invoices: Claude API runs $20 to $80 per month for 3 agents, Vercel hosting is $20 per month, and miscellaneous services cover the rest. That covers API usage for one or two AI models plus a basic server to run agents. The real cost is the time to build and maintain them, which can run 10 to 20 hours for your first agent. We broke even within 30 days on our first agent because the task it replaced was eating 4+ hours per week. Do I need a developer to build AI agents? No. I built our agents in Claude Code without traditional programming. The important part is knowing what the agent should do, what data it needs, and how to check its output. Those are process skills, not coding skills. If your workflows are complex or you need custom integrations, a developer speeds things up. But the first agent does not require one. What marketing tasks should I automate first? Weekly client reporting is the best starting point for most agencies. It is repeatable, data-driven, and takes hours every week. After that, look at email triage, meeting summaries, and campaign data pulls. Stay away from strategy, creative work, and client communication. AI is bad at judgment calls and worse at relationships. How long does it take to see ROI from AI automation? For a well-scoped agent handling a 4-hour weekly task, I saw time savings in the first week. The setup took about two weeks of focused work. Payback on that setup time came within 30 days. The caveat: if you pick the wrong task, you spend weeks building something nobody needed. What is the difference between AI automation and regular marketing automation? Regular marketing automation follows rules you set. "If someone opens three emails, move them to this list." It does exactly what you told it. AI automation makes decisions within boundaries. "Read this campaign data and write a summary highlighting anything unusual." The AI interprets, not just executes. What are the biggest risks of AI automation for agencies? Silent failures are the worst. The agent runs, the output looks fine, but the data is wrong. Nobody catches it until a client complains. Other risks: API changes breaking your setup overnight, prompt drift degrading output quality over months, and maintenance hours eating into the time you saved. Budget 2 to 4 hours per week for upkeep per active agent. Can a solo marketer build AI agents without a team? Yes. I started as a solo founder before Victor joined. One person can build and maintain 2 to 3 agents comfortably. Beyond that, maintenance starts competing with your actual work. The key is starting with one agent, getting it stable, and only adding the next one when the first runs without daily attention. How do I measure the ROI of AI automation? Track two numbers: hours saved per week and errors caught during review. Hours saved is straightforward. If the task took 4 hours manually and now takes 30 minutes of review, you saved 3.5 hours. Errors caught tells you how much trust to put in the agent. If you catch issues every week, the agent needs tuning. If review is clean for a month straight, you can reduce oversight. What tools do I need to start with AI automation? Start with Claude or ChatGPT for the AI processing. Add a database like Airtable or Google Sheets if your agent needs to remember previous runs. Use webhooks or simple scripts to connect tools together. You do not need all of this on day one. Your first agent can be a Claude prompt that you run manually with copy-pasted data. Automate the connections later. How many agents should an agency run? Start with one. Get it stable. Then add a second. Most small agencies run 3 to 5 agents comfortably. Each one handles a specific task: reporting, data pulls, email triage, content scheduling. The mistake is building too many too fast. Every agent needs monitoring and maintenance. Five agents with 2 hours of weekly maintenance each is 10 hours, half of the time they were supposed to save. --- ## Claude vs ChatGPT: What I Use for Marketing URL: https://sucana.ai/blog/claude-vs-chatgpt-marketing Published: 2026-03-31 Author: Virgil Brewster Tags: ai, automation, agencies Is Claude Better Than ChatGPT for Marketing? In my case, Claude is a clear winner. I run my marketing on it. Ad copy, headlines, strategy, client reports, content writing. It follows brand voice rules and sticks to instructions better than any other tool I have tested. ChatGPT is my Google now. I ask it a question, get an answer, move on. For images I use Gemini. For deep research I use Perplexity. For quick facts I use Grok. Victor Called Me on a Tuesday Morning Victor my co-founder opened the call buzzing. He had just used Claude Code for the first time on his own that morning. Messy CSVs that used to take hours of formulas and copy-paste, done in minutes. He kept saying "now I know that I don't know." Like he had seen something he could not unsee. Victor runs ad campaigns for a living. He had been using ChatGPT for months. Never once did he sound like that after a ChatGPT session. Not once. That morning I knew the comparison articles people write about Claude vs ChatGPT are missing the point. They compare features. They list context windows and pricing tiers. Nobody talks about what happens when you hand both tools real marketing work and see which one finishes it. The tool that changes how you work is the one that wins. Here is what I found after running both on real marketing tasks for months. Where Claude Wins for Marketing I write ad copy in Claude . Headlines, descriptions, landing page text. I give it my brand voice file, the campaign brief, and the target audience. It follows the instructions. ChatGPT used to do this fine. Then something changed. The writing got long-winded. It started ending every response with a teaser: "there is one more thing I can tell you if you want." Like a salesperson who will not let you leave the store. Claude writes what I ask. Stops when it is done. For client reports, Claude reads a CSV export from Meta or Google, runs the analysis, writes the narrative, and gives me two HTML reports. One for the team, one for the client. I built this as a skill inside Claude Code and documented the entire process in my guide on automating client reporting with Claude . It runs every Monday. Victor my co-founder went from eight hours of reporting to fifteen minutes. We timed this over three consecutive Mondays across five client accounts, comparing his old manual process against the Claude skill output plus review time. For content strategy, I upload the brand guide, three months of performance data, and the keyword research. Claude holds the full context in one conversation. ChatGPT on the Plus plan runs out of room before I finish uploading. Where ChatGPT Still Wins I am not going to pretend Claude does everything. I needed a blog cover image last week. Claude cannot make images. It reads them, analyzes them, but it will not draw you one. I opened Gemini, generated three variants in two minutes. If you want one tool that writes and draws, ChatGPT is the only option. I just prefer Gemini's output. I tried Claude's deep research mode on a competitor analysis last month. It came back shallow. Same query in Perplexity gave me twelve sources I could cite. ChatGPT's deep research sits between the two. Better than Claude, not as good as Perplexity. I never use voice mode, but if you work by talking to your tools, ChatGPT lets you have a conversation with your camera on. Claude does not do this. I scrolled through the GPT marketplace looking for a reporting template. Found nothing I could not build myself in a Claude Project in ten minutes. But if you want pre-built tools without building anything, it is there. Shopping, health tracking, location recommendations. I do not need any of that. ChatGPT is building a consumer app. Claude is building a work tool. That is the difference. What People Get Wrong Most comparison articles line up features side by side. Context windows. Pricing. Model names. None of that told me anything useful. I wanted to know what happens when I ask both to write 20 Google Ads headlines with a 30-character limit. I care about three things: Does it follow instructions? Does the output sound like a human wrote it? Does it push back when my brief is bad? I gave both the same brand voice file with 12 specific rules. Three responses in, Claude was still following all 12. ChatGPT had dropped back to its default tone by response two. I tested this across five separate conversations over two weeks, each with a different client brief. Same result every time. Claude's writing sounds more human. ChatGPT has a pattern I spot from across the room. Long paragraphs that say nothing. Engagement bait at the end. Those "curious" and "wondering" words that make everything sound like a press release. I gave Claude a campaign brief where I said a lead is worth $2,000 because my product costs $2,000. Claude told me that is not how lead value works. Not every lead becomes a customer. I need to factor in close rate. ChatGPT took my $2,000 number and built the entire analysis on it. Wrong math, confident output. When ad budget depends on that analysis, the tool that corrects you is worth more than the one that agrees with you. The Real Problems With Claude The problems I run into most: No image generation. I solved this with Gemini, but it means one more tab open. Rate limits on the $20 plan. I hit the wall mid-article three days in a row before upgrading. Claude gives you the minimum if you are not specific. You have to spell out what you want. No voice mode. If you work by talking to your tools, Claude is not there yet. I asked it to add a delete button to a page. It gave me a button that deletes. Nothing else. No confirmation, no styling. ChatGPT would have added all of that without being asked. With Claude, you have to spell out what you want. That is a strength once you learn it and a wall when you are starting out. How I Set Up My AI Stack This is the setup I use every day. Four tools. Each one does one thing well. I wrote a deeper breakdown of the AI marketing tools that actually stuck after a year of testing. Claude: This is where 80% of my work happens. Ad copy, headlines, content, client reports, data analysis, and building automations with Claude Code . It also runs my LinkedIn DM workflow that saves two hours a day . Gemini: Image generation. I use Nano Banana for blog cover images and ad creatives. Photorealistic output without the AI look. Perplexity: Deep research. Market analysis, competitor breakdowns, finding data I can cite. Perplexity shows its sources. I can verify before I publish. Grok: Quick facts. Fast answers to simple questions. My Google replacement for anything that does not need deep research. ChatGPT sits open in a browser tab. I use it the way I used to use Google. Type a question, read the answer, close the tab. How I Tested Both on Real Work I picked client reporting. One task I run every Monday. I exported a week of Meta campaign data as a CSV. Uploaded it to Claude with this prompt: "Read this CSV. Calculate spend, CTR (click-through rate), CPC (cost per click), CPL (cost per lead), and ROAS (return on ad spend) per campaign. Flag any campaign where spend jumped 20% without a lift in results. Flag zero-conversion campaigns. Write a client-facing summary with specific numbers behind every recommendation." Claude came back with a clean breakdown. Every campaign sorted into scale, kill, or investigate. Numbers matched the Ads Manager dashboard. The narrative sounded like something Victor my co-founder would send. Same CSV, same prompt, into ChatGPT. ChatGPT returned a longer report. The structure looked professional. But two numbers were wrong. CPL on one campaign was off by 40% because ChatGPT misread a column header. This was a single-CSV test using one week of Meta Ads data with 12 campaigns, same file and same prompt given to both tools. The narrative was vague: "Campaign A showed strong performance." No numbers behind it. I fixed Claude's output in three minutes. Mostly formatting. I spent twenty minutes fixing ChatGPT's output. Recalculating the wrong numbers, adding specifics to every vague sentence. That is the test. Not features. Not pricing. How much time do you spend fixing the output? I did this with one week of Meta data. Same CSV, same prompt, both tools. Counted the edits. That told me everything I needed to know. Try it with your own export and see which one makes you work less. I Almost Quit Claude Over the Pricing Claude Pro and ChatGPT Plus both cost $20. Same sticker price, completely different experience. I hit Claude's usage limit three days in a row during a heavy writing week. Mid-article, locked out. No fallback model. Just a message telling me to wait. ChatGPT handles this differently. It bumps you to a cheaper, dumber model. You keep working, but the quality drops without warning. I upgraded to Claude Max at $100 a month. Five times the usage. That solved it. ChatGPT Pro costs $200 for unlimited access to their best model. More expensive, no ceiling. Here is what I pay: $20 for Claude Pro (enough for writing, strategy, and light analysis), $20 for ChatGPT Plus (quick lookups and the occasional image). $40 a month total. Less than one hour of a freelancer's time. When I have a heavy week, I bump Claude to Max for that month. Run Your Own Test I stopped reading comparison articles months ago. They never answer the question. The answer is: run your actual work through both and count the edits. The one you fix less is the one worth paying for. For me, that is Claude. For you, it might be different. Test it. Frequently Asked Questions Is Claude better than ChatGPT for writing ad copy? I write all my ad copy in Claude. It follows brand voice rules better and sticks to character limits. ChatGPT tends to drift from the brief after a few responses. Can Claude generate images? No. Claude cannot create images. It can read and analyze images you upload, but it will not generate new ones. I use Gemini for image creation. Is Claude or ChatGPT better for data analysis? For structured data like CSV exports from ad platforms, Claude is more reliable. I tested both on the same Meta export. Claude matched the Ads Manager dashboard. ChatGPT misread a column and gave me a CPL that was off by 40%. For client work, that kind of error kills trust. Which one is cheaper? Both have a $20 plan. Claude gives you higher quality but stricter usage limits. ChatGPT gives you more features but lower quality on the default model. Running both costs $40 a month total. Does Claude have web search? Yes. Claude added web search in 2026. It works but feels basic compared to ChatGPT's browsing, which has been around longer. For serious research I use Perplexity instead of either. Can I use Claude for SEO content? I write all my SEO articles in Claude. This article was written in Claude. It handles keyword placement, heading structure, and FAQ sections well because it follows detailed instructions without drifting. What is Claude Code? Claude Code runs in your terminal. It reads your files, writes code, runs commands. I use it to build reporting skills, content pipelines, and marketing automations. If you want to see what that looks like in practice, I documented how to turn repeatable tasks into AI SOPs . ChatGPT has Codex, which does something similar but works differently. Is ChatGPT getting worse? A lot of people on Reddit say ChatGPT got worse after the GPT-5 update. OpenAI auto-selects cheaper models behind the scenes to save costs. One user wrote: "I basically had to go Karen on it and say stop with this nonsense." The writing got more verbose and the tool started using tricks like fake cliffhangers to keep you engaged. Should I use both? That is what I do. Claude for real work. ChatGPT for quick lookups. Gemini for images. Perplexity for research. Four tools, each doing one thing well. What about Gemini and Perplexity? Gemini is strong for images and has a natural advantage for Google Ads because it knows Google's platform deeply. Perplexity is the best research tool because it shows sources and lets you verify everything. Both are part of my daily stack alongside Claude. Can Claude replace ChatGPT entirely? Not yet. Claude cannot generate images, and its deep research is weaker. I keep ChatGPT for quick lookups and the occasional visual. But for the work that makes money, writing and analysis, I use Claude. Is Claude vs ChatGPT different for writing than for business tasks? For writing, Claude wins clearly. It holds tone, follows brand rules, and sounds more human. For business tasks like scheduling, app integrations, and voice conversations, ChatGPT has more built-in features. It depends on what your work looks like day to day. Which is better for email marketing? I draft email sequences in Claude. It keeps the tone consistent across a full sequence without drifting. ChatGPT is faster for subject line brainstorming when I need 20 variations in a minute. --- ## How to Run a Marketing Agency With AI (Claude Code Workflow) URL: https://sucana.ai/blog/how-to-use-claude-code-agency-brain Published: 2026-03-24 Author: Virgil Brewster Tags: ai, automation, agencies How I Used Claude Code to Build an AI Brain That Runs My Entire Agency Can Claude Code Run Your Entire Agency? Yes, if we build the right system around it. I built a project folder that holds everything my agency knows: client briefs, ad frameworks, reporting templates, and onboarding checklists. Then I built AI skills that use that knowledge to do real work. Claude Code is the tool that builds it all. Plain language. No coding required. I built a system where Claude manages every client, writes every ad, generates every report, and onboards every new account. The goal was simple: walk away from day to day operations completely, and have the output be better than when I did everything manually. Why I Built This with Claude Code I run a performance marketing agency called Fan Convert. I manage paid ads for 8 clients across Meta and Google. For years, I was the bottleneck. Every report went through me. Every new client setup went through me. I was looking at hiring media buyers to scale. I had already seen how Claude compares to ChatGPT on real marketing tasks , so I knew it could handle the work. Then I realized something. What if I built a system where Claude does the work, and my project manager just oversees it? Not a chatbot. Not a simple assistant. A full operating system for the agency. The foundation is the same as any AI workflow for a marketing team — start with the tasks that waste the most time. So I stopped hiring. And I started building. Three days later, I had a working system. One person, my project manager, manages the entire business. Claude does the heavy lifting. The output is better than what I was producing manually. What I Built with Claude Code A project folder called Fan Convert Brain, stored on GitHub. Think of GitHub as a shared hard drive that tracks every change, like a permanent version history. It holds everything the agency knows: client data, campaign strategies, processes , and six AI skills. Ad copywriting. Campaign reporting . Client onboarding. Image generation. Landing page design. Plus five production tools: a Facebook Ads publisher that creates ads from a spreadsheet, a client reporting portal, automated daily cron jobs for pulling ad metrics, a server side tag manager, and an auto sync system that backs everything up every 15 minutes. Before You Start with Claude Code Claude Code installed on your computer. This is Anthropic's CLI tool. You type instructions in natural language and Claude writes code, creates files, and builds things for you. A GitHub account. GitHub is where I store the finished project so it's always backed up and accessible. Think of it as a shared folder on the internet. Your agency's information: client list, services, pricing, processes, campaign data. API keys for the platforms you use ( Meta Ads , Google Ads ). An API is how two tools talk to each other. When I say "Meta Ads API," it means Claude can create ads without you logging into Ads Manager. A Vercel account for deploying web tools. Vercel is the service that puts websites on the internet. When you push a file, Vercel sees the change and updates the site automatically. The Steps Stage 1: Create the Brain This is the prompt: In Claude Code, type: "I want to build a knowledge base for my marketing agency. Create a git repository called Fan-Convert-Brain with this structure: identity/ for who we are, processes/ for SOPs, strategy/ for our frameworks, benchmarks/ for performance data, tech-stack/ for tool docs, admin/ for services and billing, team/ for team priorities, clients/ for client workspaces, skills/ for AI capabilities, and tools/ for production software. Create a CLAUDE.md at the root that acts as a router, telling you which folder to check depending on the type of question." Claude builds the entire structure in under two minutes: every folder, every file, and the CLAUDE.md router that tells it where to look for anything. CLAUDE.md file: This is your project's brain. It's the first file Claude reads when it opens the project. It tells Claude what folders exist and what's in each one. Think of it as a map that Claude follows to find the right information. I confirmed it worked by opening the folder and checking every directory existed. Then I opened CLAUDE.md and verified it listed every folder with the right reading priority: strategy first for methodology questions, processes for "how do we do X," benchmarks for performance questions. Without a clear structure, Claude doesn't know where to look and gives generic answers instead of answers based on your agency's actual data. Stage 2: Build the Client Template Paste this in: In Claude Code, type: "Create a client template at clients/_template/ with this structure: a CLAUDE.md as the client brief, a context/ folder with files for client-info, offer, buyer-personas, funnel, accounts, brand-guidelines, and competitors. An admin/ folder with contract, services, pricing, invoicing, onboarding checklist, and notes. A strategy/ folder with current-plan, roadmap, and decision-log. A campaigns/ folder with a change-log. Plus folders for copy, data, reports, meetings, and tasks." Before this template existed, every client folder was different. One had buyer personas. Another didn't. I was the system. If I wasn't around, nothing was findable. I read every file Claude created. The CLAUDE.md had a file index pointing to every context file. The decision-log and change-log were set up as append-only files. You only add to them, never delete. Permanent record of every decision. Now every client gets the exact same workspace. Nothing gets missed during onboarding. Anyone on the team can find anything in the same place. Stage 3: Create the Context Routing Skill The prompt: In Claude Code, type: "Build a context routing skill at skills/context-routing/. The core idea is lazy loading: only load the information Claude needs for the current task, not everything at once. Define 7 principles: light files only (under 80 lines each), explicit authority hierarchy (entity level overrides domain, domain overrides global), one concern per file, conditional routing by task type, explicit human approval for context changes, mandatory append-only logging, and platform agnostic design. Include reference docs for how to build a router file, how to structure a context file, scaling playbook, routing rules catalog, audit checklist, and anti-patterns." SKILL.md file: This is the instruction file that tells Claude what to do. Think of it as a recipe card. Claude reads it and follows the steps. Every skill in the Brain has one. The core principle here is lazy loading: Claude reads only what it needs for the current task. Not everything at once. I had six reference files, one for each area, how to build a router, how to structure a context file, scaling rules, routing catalog, audit checklist, and common mistakes. I verified the authority hierarchy was explicit: a client's CLAUDE.md overrides the agency's CLAUDE.md, which overrides a skill's defaults. Every reference file stayed under 200 lines. The anti-patterns file listed real mistakes , "putting everything in one giant file" and "loading all context upfront." This is the architecture that makes everything else work. Without routing, Claude either knows too little or tries to read too much and gets confused. Stage 4: Build the Andromeda Copywriting Skill Your instruction: In Claude Code, type: "Build a copywriting skill at skills/andromeda-copywriter/ based on Eugene Schwartz's Breakthrough Advertising, adapted for Meta's algorithm. The key insight is that on Meta, the creative itself segments the audience, not the targeting. Build a 4-phase process: Phase 1 gathers all client data (offer, personas, funnel). Phase 2 creates detailed buyer avatars. Phase 3 generates hooks (the first line people see). Phase 4 writes full ad bodies with CTAs. The output should be 10 ads organized in 2 thematic groups of 5. Include reference files for each phase." CTA: CTA means Call to Action. It's the line that tells people what to do next, like "Book a call" or "Download this guide." I ran it on a real client immediately: a high-ticket mentorship at 2,000 euros. The skill went through all 4 phases in order. Each hook was different from the others. The ads spoke to specific pain points from the buyer persona, not generic marketing language. Ad copy was the most time consuming task I did every week, and quality depended entirely on my energy level that day. Stage 5: Build the Ad Reporting Skill What to type: In Claude Code, type: "Build an ad reporting skill at skills/ad-reporting/. It should analyze campaign data from Meta Ads, Google Ads, YouTube Ads, and TikTok Ads. Create a mandatory 5-question workflow: first summarize found data, then ask campaign type, then ask target metric, then tag campaigns as Launch or Evergreen, then ask if the user wants an HTML report. Always generate two versions: a detailed agency report and a simplified client report. Chart colors are always Google in red and Meta in blue. Priority metrics are CPL and cost per appointment, never CTR or CPC first. Include reference files for each platform with benchmarks and metric definitions." CPL: Cost Per Lead. How much money you spend to get one person to fill out a form or sign up. If you spent 100 euros and got 10 leads, your CPL is 10 euros. Lower is better. Then I said: "The reports need to prioritize CPL and cost per appointment. I don't want CTR or CPC showing up first. Those metrics distract clients." What happened instead: Claude reorganized the metric hierarchy in the skill so CPL and cost per appointment always appear at the top of every report, and CTR/CPC only show up in the detailed agency version. I exported a real CSV from Meta Ads Manager and dropped it into Claude Code. The 5-question workflow triggered in order. It detected Meta as the platform, set chart colors to blue, and put CPL front and center in the HTML report. I also tested with a Spanish-language CSV. Comma decimals, multi-row headers. It handled both. Reporting took 2 to 3 hours per client per week, and half that time was formatting, not analysis. I covered the full reporting automation process in how to automate client reporting with AI . Stage 6: Build the Client Onboarding Skill Feed this to Claude Code: In Claude Code, type: "Build an onboarding skill at skills/onboarding/ that automates the entire new client setup. 5 phases: Phase 1 gathers admin info (contract, billing, contacts). Phase 2 understands the market (offer, personas, funnel). Phase 3 audits advertising setup (accounts, pixels, tracking). Phase 4 generates strategy (decision log, roadmap, current plan). Phase 5 validates and launches. The skill should auto-generate every file in the client template, fully populated with real data from the conversation. Include a question bank with every question mapped to the exact field it fills." New client setup used to take a full day of back and forth. Emails about ad accounts. Follow-ups about the contract. Hunting down pixel IDs alone took hours. The onboarding skill runs one conversation and does all of it. I onboarded a real client through all 5 phases. After the conversation ended, I checked every file. The offer was there. The funnel setup was there. The Meta pixel ID and Google Ads account ID were there. The strategy file had the correct target CPL and daily budget. Everything populated. Nothing missing. One conversation. Full client workspace. Ready for Claude to start working. Stage 7: Build the Ad Image Generator Skill Open Claude Code and type this: In Claude Code, type: "Build an ad image generator skill at skills/ad-image-generator/. It should generate ad creatives across 4 sizes: square, feed, story, and landscape. Use the Nano Banana MCP for image generation. Give the user 3 paths to start: provide a hook and angle, describe an idea, or upload a reference image. Include a reference file with the JSON prompt schema for Nano Banana, a gallery of great ad images as examples, and a style framework document." MCP: MCP is how Claude connects to external tools like image generators or ad platforms. Think of it as plugging in a cable between Claude and another app. Clients need fresh creatives every week. Designing 4 size variants per concept used to take hours. The skill does all four from one prompt. I tested it on a real estate client. I gave it a hook about luxury properties and checked that 4 size variants came out. The images matched the style framework and the generated prompt followed the Nano Banana schema exactly. Fresh creatives, four sizes, one prompt. That's the shift. Stage 8: Build the Pen-to-Web Skill Prompt for this stage: In Claude Code, type: "Build a pen-to-web skill at skills/pen-to-web/ that converts Pencil design files (.pen) into Next.js 15 landing pages with Tailwind CSS v4. The process: read the .pen file using Pencil MCP tools, map each design node to a React component, extract design tokens and variables, build the component tree, write the Next.js code, test it, and deploy to Vercel. Include reference files for responsive strategy, Tailwind v4 setup, Pencil node-to-JSX mapping, and Next.js project structure." Next.js: Next.js is the tool that builds the website. Think of it as the engine behind the page. You don't need to understand it to follow the steps. Vercel: Vercel is the service that puts the website on the internet. When Claude pushes a file, Vercel sees the change and updates the website automatically. Going from a design file to a live landing page used to require a developer. This skill removes that dependency entirely. I designed a landing page for a personal brand site in Pencil and ran it through the skill. Responsive strategy worked on mobile and desktop. Tailwind v4 classes matched the design tokens. The component mapping preserved the visual hierarchy from the design file. From design file to live URL, one conversation. Stage 9: Build the Facebook Ads Publisher Type this exactly: In Claude Code, type: "Build a tool at tools/fb-ads-publisher/ that creates Facebook Ads in bulk from a Google Sheet. The sheet has tabs for config (account IDs), testing groups, hooks, bodies, CTAs, and creatives. The tool reads the sheet, generates every combination (hook x body x CTA x creative), downloads the creative images from Google Drive, and publishes each combination as a Facebook Ad via the Meta API. Ads should be created PAUSED by default. Include a dry-run mode that validates everything without publishing. Build it in Python with a CLI interface." .env file: This file stores your passwords and API keys. It stays on your computer and never gets shared. Every time the script runs, it reads this file so you don't have to paste your keys again. Three hooks. Two bodies. Two CTAs. Four creatives. That is 48 combinations. Creating them manually in Ads Manager takes an afternoon. The publisher does it in two minutes. I ran the dry-run first to verify the math: 3 x 2 x 2 x 4 = 48. Then I ran it live. All 48 ads appeared in Meta Ads Manager, paused, with the correct copy and images matched to the right combinations. Victor messaged me after I showed him the publisher running. "How long did that actually take?" Two minutes, I told him. He stared at the number. "We need to start doing this for every client." Stage 10: Build the Reporting Portal Copy and paste this: In Claude Code, type: "Build a client-facing reporting portal at tools/reporting-portal/ using Next.js 15. It should have a login page and a dashboard that shows campaign reports per client. Build the API routes for authentication and data fetching. Deploy it to Vercel." Clients used to email me asking for the PDF. Now they log in and see everything, any time, without involving me. I deployed to Vercel and logged in with test credentials. The dashboard loaded. Client data was isolated. One client cannot see another's reports. The report formatting matched my agency style with the correct chart colors. A portal gives clients 24/7 access. Sending PDF reports by email is unprofessional and clients lose them. Stage 11: Build the Automated Cron Jobs The exact prompt: In Claude Code, type: "Build automated cron jobs at tools/agency-crons/ that run daily on Vercel. They should pull ad metrics from Google Ads and Meta Ads APIs automatically so I don't need manual CSV exports anymore. Deploy as serverless functions on Vercel." API route: An API route is a small piece of code on the website that runs when triggered. In this case, it runs on a schedule (daily) and pulls data from ad platforms automatically. Exporting CSVs from ad platforms every week is the most boring task in the agency. This cron runs on its own, every day, without anyone touching it. I ran the Google Ads cron manually with a backfill parameter to pull the last 85 days of data. The numbers matched what I saw in the Google Ads dashboard. The next day, the cron ran on schedule with no intervention. Hours saved per month, just from not manually exporting data. Stage 12: Set Up Git Auto-Sync Run this: In Claude Code, type: "Set up automatic git sync at tools/git-auto-sync/. It should commit and push all changes to GitHub every 15 minutes. Create a shell script that runs as a cron job on my machine. Include error logging." GitHub: GitHub is where I store the finished project so anyone with access can download it. Think of it as a shared folder on the internet that keeps a history of every change. Before this was running, I lost changes twice. A crash, a lost file. Since auto-sync started: nothing lost. Every 15 minutes, everything backs up automatically. I made a small change to a file and waited 15 minutes. Checked GitHub. The change appeared with the auto-sync commit message. The sync.log showed the script ran without errors. If the laptop dies, nothing is lost. When my PM gets her access set up, she'll see every change in near real time. Stage 13: Onboard Every Client Your prompt: In Claude Code, type: "Use the onboarding skill to set up [client name]. Here's everything I know about them: [paste client information: name, contact, offer, price, target audience, ad accounts, current campaigns, funnel URLs]." The test is simple: after onboarding, can Claude answer a question about the client without me telling it anything? I onboarded my most mature client and then asked cold: "What's the target CPL?" Claude answered correctly from the strategy file: under 12 euros. "What's the daily budget?" Correct. "What CRM do they use?" Correct. Every answer came from the files, not from me. Every client needs a complete workspace for Claude to do its job well. Garbage in, garbage out. Stage 14: Set Up Team Management Paste this into Claude Code: In Claude Code, type: "Create a team management system at team/. Each person gets their own folder with a CLAUDE.md defining their role and a priorities.md for their weekly tasks. Create a CLAUDE.md at the team root with rules: priorities.md files are the source of truth (not ClickUp), max 15 items per week, tag tasks as [Agencia] or [Personal]. Create an agency-priorities.md for cross-team items. Set up the founder as transitioning from ops to strategy, and the PM as taking over operations." Then I said: "I was using ClickUp before but it's too heavy. Let's move to just markdown files. During the transition, Claude can still read ClickUp via MCP but the markdown priorities are the source of truth." What happened instead: Claude set up the team CLAUDE.md with a transition rule: ClickUp is read-only reference, priorities.md is the source of truth. Clean cut. I opened the founder's priorities.md and confirmed it had the current week tasks tagged correctly. The PM's CLAUDE.md defined her role clearly. The team CLAUDE.md enforced the 15-item limit and the tagging convention. If the PM is going to manage everything, she needs a clear system that shows what's priority this week without logging into another tool. Stage 15: Save the PRODUCT.md Use this prompt: In Claude Code, type: "Update the CLAUDE.md with everything I built. List every folder, every skill, every tool, and the reading priority. Make sure anyone opening this project for the first time knows exactly where to find everything." PRODUCT.md file: This is your project's memory. It documents what you built, what decisions you made, and where everything lives. When you come back to this project in three weeks and forgot what you did, this file tells you everything. In my case, CLAUDE.md serves this purpose. I tested this the best way I know how: opened a fresh Claude Code session in the project directory and asked questions cold. "Where do I find Client X's target CPL?" Strategy folder. "How do I create a new ad?" Andromeda skill. "What's the onboarding process?" Onboarding skill. Claude answered all three without any context from me. The routing works. When you come back to this project, or when your PM opens it for the first time, this file tells you everything. What I'd Do Differently Start with the context routing skill. I built some things before formalizing the routing framework. If you build routing first, everything else follows the same architecture from day one. The broader picture of what an AI-powered agency looks like helped me understand how these pieces fit together before I built them. Onboard clients before building skills. I built the reporting skill, then realized I didn't have client data structured properly to feed it. The onboarding skill should come before everything else. Set up git sync first. I lost a few changes before the auto-sync was running. Version control on day one saves headaches. When I showed Vinod the finished system, his first question was: "How long did this actually take?" Three days, I told him. He looked at me and said: "That is three days that just changed how we run this company." The Claude Code Stack Tool What it does Claude Code The AI that builds and runs everything GitHub Stores the Brain, backs up every change Vercel Hosts the reporting portal and cron jobs Meta Ads API Creates and reads Facebook/Instagram ads Google Ads API Pulls campaign metrics Google Sheets API Reads ad testing structures Google Drive API Downloads ad creative images Pencil MCP Reads design files for landing pages Nano Banana MCP Generates ad images Next.js 15 Builds the web tools and landing pages Tailwind CSS v4 Styles everything Python Powers the Facebook Ads publisher Docker Runs the server-side tag manager Files Fan-Convert-Brain/ CLAUDE.md - The brain's router agency/ - Agency-level knowledge clients/ - 8 client workspaces + template skills/andromeda-copywriter/ - Ad copy generation skills/context-routing/ - Information architecture skills/ad-reporting/ - Campaign analysis skills/ad-image-generator/ - Creative generation skills/pen-to-web/ - Design to code skills/onboarding/ - Client setup automation tools/fb-ads-publisher/ - Bulk ad creation tools/reporting-portal/ - Client dashboard tools/agency-crons/ - Automated data pulling tools/git-auto-sync/ - 15-minute backups tools/sgtm-server/ - Server-side tracking team/ - Team priorities and roles processes/ - Standard operating procedures strategy/ - Frameworks and playbooks benchmarks/ - Performance benchmarks tech-stack/ - Tool documentation templates/ - Reusable templates Frequently Asked Questions What can you build with Claude Code? Claude Code can build complete software systems, not just individual scripts. In this project alone it built six AI skills, five production tools, a client reporting portal, a bulk Facebook Ads publisher, and an automated data pipeline, all from plain language instructions. The range is wide: web apps, API integrations, automation scripts, knowledge bases, and anything that involves creating and organizing files. How do I start a project in Claude Code? Open your terminal, navigate to the folder you want to work in, and type claude . That opens the Claude Code interface. From there you give it instructions in plain English and it writes code, creates files, and builds things for you. The most important first step is creating a CLAUDE.md file that tells Claude what the project is and where everything lives. Is Claude Code hard to learn if you don't code? The prompts in this article are copy-pasteable. You do not need to understand the code Claude writes, you need to understand what you want to build and describe it clearly. The learning curve is in structuring your instructions. The more specific you are about the output you want, the better the result. What is a CLAUDE.md file and how does it work? CLAUDE.md is a plain text file that sits at the root of your project. Every time Claude Code opens the project, it reads this file first. It tells Claude what folders exist, what each one contains, and which files to read depending on the type of task. Think of it as an index card that gives Claude a map of your project before it does anything. How do you give Claude Code long-term memory? Claude Code has no memory between sessions by default. The solution is a folder structure where all context lives in files, not in conversation history. Every time you open the project, Claude reads the CLAUDE.md and loads the right context files for the task at hand. The files are the memory. That is exactly how this agency brain works. How much does Claude Code cost? Claude Code runs on your Anthropic account. Pricing depends on the Claude model you use and how much you build in a session. For a project like this one, expect to spend between $20 and $60 over a 3-day build. After the build is complete, day-to-day usage, running skills, generating reports, writing ad copy, costs a few dollars per session depending on the volume. Can you use Claude Code to automate a marketing agency? Yes. This article documents exactly that. Six AI skills handle copywriting, reporting, client onboarding, image generation, and landing page design. Five production tools handle bulk ad creation, client portals, automated data pulls, and version control. One project manager oversees the whole system. Claude does the execution. What is the difference between Claude Code and other AI coding tools? Claude Code runs in your terminal and operates directly on your file system. It reads, writes, and edits files without you copying and pasting between a chat interface and your editor. The key difference for non-developers is that you describe what you want in plain language and Claude figures out the implementation. You stay in charge of the logic. Claude handles the code. How long does it take to build something useful with Claude Code? The agency brain in this article took three days. That includes the full folder structure, six AI skills, five production tools, and onboarding eight existing clients. A single useful tool, like a reporting script or a bulk uploader, can be built in a few hours on a first session. What APIs does Claude Code work with? Any API that has documentation. In this project: Meta Ads API, Google Ads API, Google Sheets API, Google Drive API, and Vercel for deployment. Claude reads the API documentation, writes the integration code, handles authentication, and tests the connection. You provide the API keys in a .env file. Claude does not touch the keys directly. --- ## How I Automated Client Reporting With Claude (8h to 15min) URL: https://sucana.ai/blog/automated-client-reporting-claude Published: 2026-03-23 Author: Virgil Brewster Tags: ai, automation, agencies How Do You Stop Wasting Mondays on Reports? You build a skill in Claude that reads your CSV exports, runs the full analysis, writes the narrative, and gives you two client-ready reports. One for your team. One for the client. What took my co-founder a full Monday now takes fifteen minutes. Here is how I built it. Victor Was Staring at Three Monitors Monday morning. One screen has Meta Ads Manager open. One has a Google Sheet with last week's numbers. The third has a PowerPoint deck from two weeks ago that he is about to copy-paste into a new file and update by hand. He has twelve clients. Each one gets a report. Same ritual every single Monday. Pull the data. Copy it into slides. Write the narrative. "Campaign X performed well." "We recommend increasing budget on Y." Send. Next client. Repeat. Eight hours later he looks up from his desk and the day is gone. Not a single ad was optimized. Not one creative was tested. Not one client call was made. He spent the entire day telling people what already happened. I sat across from him one Monday and watched. Same tabs. Same clicking. Same face. That look you get when you know you are doing something a machine should be doing but you cannot stop because the client expects it by noon. That was the moment I decided to build the reporting skill. CSV In, Two Client Reports Out A skill inside Claude that reads your CSV export, runs the full analysis, writes the narrative, and outputs two client-ready HTML reports. One for your team. One for the client. This is not a tool you download. It is something you build yourself, inside Claude, fitted to the way your agency actually does reporting. Your sections. Your metrics. Your voice. Three Things Before You Start Claude installed. A CSV export from Meta, Google, YouTube, or TikTok. And the knowledge of what a good report looks like at your agency. That last one is the important one. The sections, the metrics, the narrative structure your clients expect. Every agency has a version of this floating around in somebody's head or buried in a PowerPoint template from 2019. Step 1: Define Your Reporting Standard I opened Claude and described what Victor puts in every report. "I want to build a reporting skill. Here is what we include in every client report: executive summary, per-campaign breakdown with spend, CTR, CPC, CPL, and ROAS, creative performance ranking, anomaly flags, recommended actions for next week. The tone is direct. Every recommendation backed by numbers. Turn this into a PPC report template I can reuse." Claude came back with a structured template. Sections, metric definitions, narrative guidelines. I showed it to Victor. He looked at it for about ten seconds, then said: "The executive summary should come after the campaign breakdown, not before. And I always add a key decisions block at the end." Two changes. Both went into the template. The reporting standard has to come from whoever actually sends the reports. Not from a generic template. Not from me. From the person who knows what clients complain about when the format changes. Step 2: Build the CSV Parser This is where it gets ugly if you rush it. "When I upload a CSV, read it, identify all columns, normalize the names. Meta and Google both rename columns depending on the export type. Calculate per-campaign totals: spend, impressions, clicks, CTR, CPC, conversions, CPL, ROAS, frequency. If a metric is missing, derive it. If it genuinely cannot be derived, flag it. Never fabricate it." We tested this against a real Meta export and cross-checked every number against the Ads Manager dashboard. This is one of the reasons I now use Claude over ChatGPT for marketing work . Matched perfectly until we tried a monthly export instead of weekly. Meta names one column differently depending on the date range. Nobody tells you this. You find out when your CPL is suddenly off by 40% and you have no idea why. That is the kind of silent failure that ruins trust with a client. The report looks right. The numbers look reasonable. But they are wrong, and you will not know until someone checks. We added both column variants and tested again. Test on real exports, not sample data. Sample data does not have the naming inconsistencies that will break your parser at 9 AM on a Monday when you are trying to send reports. Step 3: Build the Analysis Engine Data without interpretation is a spreadsheet. The client already has a spreadsheet. What they need is somebody telling them what to do next week. "Flag top campaigns by ROAS and conversion volume. Flag bottom campaigns by cost efficiency. Flag any campaign where spend jumped 20% or more without a lift in results. Flag zero-conversion campaigns that burned through twice the CPL target. Flag frequency fatigue. One sentence per flag explaining what happened and the numbers behind it." The skill splits every campaign into three buckets. Scale it. Kill it. Investigate it. A new campaign burning cash to find winners has different thresholds than a six-month evergreen campaign. A 3x frequency on a launch campaign is Tuesday. The same number on an evergreen campaign is creative fatigue and your audience is tuning out. The skill separates launch mode from evergreen mode. Cold audiences above 3x frequency on launch campaigns get flagged. On evergreen, the threshold drops to 2x. Small difference. Huge impact on the recommendation. We ran it against a week where one campaign had a genuine spend spike with zero performance lift. The skill caught it. The explanation it wrote matched what Victor would have written himself. That was the first time I thought: this is going to work. Step 4: Build the Narrative Layer This is the step that separates a reporting tool from a reporting skill. Most PPC analytics tools give you a dashboard. Charts. Tables. Data. Nobody reads dashboards. Clients open them, scroll for three seconds, close them, and ask you on the next call: "So how are things going?" They want the story. What happened. Why it happened. What you are doing about it. "Take the analysis and write a client-facing summary following the report template. Direct tone, not corporate. Every section three to five sentences. Every recommendation must include the specific numbers that support it." "Campaign X is underperforming" is an opinion. You can write that about anything. "Campaign X spent EUR450 with zero conversions, three times above the EUR15 CPL target" is evidence. The client reads that and understands exactly what happened without asking a follow-up question. The skill enforces this on every recommendation. If there is no number, there is no recommendation. I showed the output to Victor without telling him the skill wrote it. He read the account summary and said it sounded like his writing. Two campaign sections were too vague. We added the "include specific numbers" rule and reran. Passed. Step 5: Build the HTML Output "Format the narrative as a clean HTML report. Campaign sections in cards. Winners in green. Attention items in orange. Anomalies in red. Two versions: agency version with all metrics and technical detail, client version with plain-language insights and no raw tables." Two versions because the internal team needs the full breakdown. Quality Score, impression share, hook rates, the lot. The client does not care about hook rates. The client cares about whether the campaign is working and what you are going to do next. Color coding is the fastest way to comprehension. Client opens the report, sees green and orange at a glance, knows the account health before reading a single word. First version had a table that broke on mobile. Fixed the CSS, reran, confirmed it passed the only test that matters: would I actually attach this to a client email? Step 6: Add Calibration Questions Without this step, the skill applies the same standard to every account. A EUR10 CPL target produces a very different report than a EUR40 CPL target on the same data. "Before running the analysis, ask six questions: reporting period, campaign type, the client's target metric with ranges, CPC target for Google Ads, launch or evergreen mode per campaign, and whether to generate the HTML report." Takes thirty seconds to answer. Changes everything about the analysis. The skill also auto-detects market codes in ad set names. If you are running campaigns across US, UK, and EU, it groups the analysis by market and asks whether the goal is the same or different per region. Step 7: Package the Skill "Package everything into one skill file. Include the main instruction file, platform reference files for Meta, Google, YouTube, and TikTok, and the HTML report template." Now it is a single file. Load the skill. Drop in a CSV. Answer six questions. Get two reports. The platform reference files matter because each platform has different metrics. Meta has hook rate and hold rate. Google has Quality Score and impression share. TikTok has 2-second and 6-second hook rates. YouTube has skip rate and completion rate quartiles. The skill loads the right reference file based on which platform it detects in the CSV columns. Victor still uses it every Monday. The difference is he is done by 9:15. Full Flow From CSV to Client Email The full flow from CSV to client-ready report: Upload CSV exports from Meta, Google, YouTube, or TikTok Auto-detection of platform from column headers Six calibration questions for period, campaign type, goals, and mode Column normalization and metric calculation across all naming variants Per-platform analysis with scale, kill, or investigate verdicts backed by numbers Creative scoring per ad with a 0-10 score and adaptive weights Cross-platform comparison if two or more platforms present Full narrative report following your template Two HTML reports for agency and client audiences Edge cases that will save you debugging time: multi-market grouping from ad set naming conventions. Missing metrics derived where possible, flagged where not, never fabricated. Budget pacing only checked on three or more days of data. Cross-platform comparison only runs if date ranges actually overlap. Launch vs evergreen thresholds applied per campaign, not globally. Two Mistakes That Cost Me Extra Hours Test with actual client CSV formats before building the parser. Meta and Google change their export columns regularly. I found three naming variations after I thought I was done. More aggressive normalization on day one would have saved two debugging sessions. Separate metric calculation from narrative generation earlier. When both break at the same time, you cannot tell which step failed. Build each step on its own, test it, then connect them. Frequently Asked Questions What ad platforms does this work with? Meta Ads Manager, Google Ads, YouTube Ads, and TikTok Ads. The skill detects the platform from column headers in your CSV. No API keys needed. Do I need to know how to code? Not at all. Every step in this article is a plain language prompt you type into Claude. Copy it, paste it, adjust it for your agency. How long does the full build take? About 30 minutes for the basic version with analysis and narrative. Another 30 if you want the HTML output with color coding and dual versions. Can I use my own report template? That is the entire point. Step 1 is defining your template from whoever actually sends reports at your agency. What if my CSV is missing columns? The skill derives metrics where possible. Frequency from impressions and reach. CTR from clicks and impressions. If a metric genuinely cannot be calculated, it flags it. It does not invent numbers. Does it work with multiple clients? Victor runs it for 12 clients every week. Each one gets calibrated separately through the six questions. Different goals, different thresholds, different reports. How accurate is the analysis compared to manual? I cross-checked every metric against the Ads Manager dashboard during testing. The numbers matched. The narrative quality depends on your template. If your template is vague, the output is vague. Can I compare multiple platforms in one report? If you upload CSVs from two or more platforms, it runs a cross-platform comparison. CPL and ROAS side by side with a budget reallocation recommendation. It validates that date ranges overlap first. What is launch mode versus evergreen mode? Launch mode assumes the campaign is new and spending aggressively. Higher frequency thresholds, volume-focused benchmarks. Evergreen mode assumes the campaign is mature. Tighter limits, efficiency-focused benchmarks. Can I change the HTML report design? The report uses HTML and CSS. Changing the template prompt changes the output. Colors, fonts, layout, which sections appear. All adjustable. --- ## Lead Magnet Ideas: How I Built One With AI That People Actually Use URL: https://sucana.ai/blog/how-to-build-ai-lead-magnet Published: 2026-03-17 Author: Virgil Brewster Tags: ai, automation, agencies What Is an AI Lead Magnet? An AI lead magnet is a downloadable AI tool that subscribers run themselves to get a result. Not a PDF. Not a checklist. A file they drag into Claude or ChatGPT , and it does something useful for them on the spot. I built one for Sucana . It takes any meeting transcript and pulls out content stories, action items, and client notes. One file. One command. Three outputs. The difference between this and a traditional lead magnet is simple. People use it. They don't save it to a folder and forget about it. Why I Stopped Making PDFs Every marketer I know has the same graveyard. A Google Drive folder full of ebooks nobody finished reading. Checklists that got downloaded 500 times and opened twice. Victor my co-founder ran an agency for years . He told me once that his best-performing lead magnet, a 30-page media buying guide, had a 2% completion rate. Two percent. That means 98 out of 100 people downloaded it and never looked past page three. The problem is not the content. The problem is the format. A PDF asks you to read. An AI lead magnet asks you to paste something in and get a result back. One is homework. The other is a tool. How I Built the Lead Meeting Agent I did not start from scratch. That is the part most people miss about building an AI lead magnet. You do not sit down and invent something new. You take a workflow you already run internally and strip it down until anyone can use it. Here is exactly how I did it in Claude Code, step by step. Step 1: Pick a Workflow You Already Use We run a morning routine skill every day at Sucana . It processes our Fireflies meeting transcripts and does six things: pulls content stories, extracts action items, organizes client notes, fetches PPC news, updates the product roadmap, and generates content ideas . I only needed three of those six. The content stories, the action items, and the client notes. If you run any repeatable AI workflow in your business , you already have the raw material. Look at what you do every week that gives you a clear output. Step 2: Open Claude Code and Start a Conversation I did not open a blank file and start writing instructions from scratch. I opened Claude Code and told it what I wanted. "I have a morning routine skill that processes meeting transcripts. I want to strip it down to a single markdown file that anyone can drag into Claude and use. It should extract three things: content stories, action items, and client notes." Claude Code read our existing skill file. It understood the structure, the prompts, the output format. Then it asked me questions. What should the trigger word be? Should the agent ask for the transcript or wait for it? How should action items be formatted? That conversation took about 20 minutes. By the end, Claude Code had a plan for the new file before writing a single line. Step 3: Let Claude Code Build the First Draft I told Claude Code to build it. One command: "Create the markdown agent file based on what we just discussed." It generated the full file in under a minute. The structure had three sections: a system prompt that tells Claude what to do, an input handler that waits for the transcript, and output templates for each of the three sections. The first draft was 90% there. It already had the right tone, the right structure, and the right output format because it pulled from our existing skill. That is the power of building in Claude Code. You do not start from zero. You start from a conversation where the AI already understands your workflow. Step 4: Strip Out Everything Internal The first draft still had our team's fingerprints all over it. Claude Code used our internal references because that is what it learned from. I went through line by line with Claude Code. "Change 'extract Victor's ads knowledge' to 'extract any participant's insights.' Remove the Fireflies API call. Remove the file path references. Remove our category names." The same precision you need when writing prompts for PPC campaigns applies here: the clearer the instruction, the better the output. Claude Code made every change in real time. I could see the file update, test it, and ask for another change in the same conversation. No switching between apps. No copy-pasting between tools. What was left fit in a single markdown file. The instructions, the structure, and the output format. That is it. Step 5: Test Each Output Separately I ran the stripped-down version on a real 30-minute call. Pasted the transcript into Claude with the agent file and typed the trigger word. Then I graded each section. Content stories: Seven postable angles came back. All usable. Passed. Action items: The first run pulled priorities as tasks. "Focus on retention" is not an action item. I went back to Claude Code and added a filter: only extract items with a clear owner and a clear next step. Claude Code updated the file. Second run was clean. Client notes: Worked first try. Organized by topic, easy to scan. Every time something was off, I told Claude Code what was wrong. It adjusted the instructions in the agent file. I tested again. This loop, tell Claude Code what is broken, let it fix the file, test again, is how you build fast in Claude Code. Three rounds and the agent was solid. Step 6: Make It Work for Anyone This is the step that separates an internal tool from a lead magnet. The file cannot assume anything about the person using it. I asked Claude Code to review the file as if it had never seen our company. "Read this agent file. Flag anything that only makes sense if you know Sucana." It found four references I missed. Team names buried in example outputs. A mention of our Slack channel. A formatting preference that only made sense for our content calendar. I removed all of them. The test: could someone who has never heard of Sucana paste in their own meeting transcript and get useful results? If yes, it is ready. Step 7: Host It and Build the Landing Page I put the markdown file in the public folder of our Next.js site. One button downloads it directly. No GitHub repo. No terminal commands. No npm install. We built a page at sucana.ai/gift . One headline, one description, one button. That is it. The page shows exactly what to do in seven steps. Each step is one sentence. If someone needs a tutorial to use your lead magnet, it is too complicated. Download, drag, paste, type one word. Two minutes and they have their result. What Makes This Convert Better Than a PDF Three things happen when someone uses an AI lead magnet instead of reading a document. They get a result immediately. No reading required. Paste, run, done. They see their own meeting broken into content stories, action items, and client notes in under two minutes. They experience your brand inside the tool. At the end of every output, one line appears: "Want a custom AI workflow built for your team? Email virgil@virgilbrewster.com ." The CTA lives inside the experience, not on a landing page they already closed. They learn a new skill without trying. Every run teaches them how to use Claude with a structured file. They did not sign up for a Claude tutorial. But now they know how it works. That is the kind of value that sticks. The Hidden Marketing Layer Here is something I learned from Victor my co-founder about low-friction funnels. Do not qualify at the top. Let people in easy. Qualify when they want your time. The lead magnet is free. No form fields beyond email. No company size dropdown. No budget range. But inside the welcome email sequence, one message says: "If you want a custom AI workflow built for your team, reply to this email." That message shows once. It is not a drip campaign hammering the same CTA. One message, one offer, one reply button. For our actual target market, PPC agencies, every download gets a tag: "Gift downloaded." Our sales team can see who downloaded it and start a real conversation about Sucana when the timing is right. Low friction at the top. Qualification happens naturally when they engage. Why This Works as a SaaS Lead Magnet Traditional SaaS lead magnets have a shelf life problem. An ebook about "PPC trends in 2025" is garbage by 2026. A template gets used once. An AI lead magnet works every time someone has a new meeting. It is not a one-time read. It is a tool they come back to. Every time they use it, they see your brand. Every run reinforces that you build useful AI tools. When they eventually need something bigger, custom, or team-wide, they already know who to call. The best lead magnet is one that keeps working after the download. What I Would Do Differently I would test the landing page copy before building the agent. We spent two days on the markdown file and 30 minutes on the landing page. The agent worked perfectly, but the first version of the page did not explain clearly enough what it does. The page needs to answer one question: "What will I get when I use this?" If that answer takes more than two sentences, simplify the agent, not the page. How to Build Your Own AI Lead Magnet You do not need to be technical. If you can describe a workflow in plain English, you can build one. Start with something you already do. A report you generate weekly . A process you walk clients through. An analysis you run on every new account. Strip it to one file. Remove your internal references. Test it on someone who does not work with you. Host it somewhere simple. One download button. Clear instructions. Seven steps or fewer. The format is not the innovation. The shift is giving people a tool instead of a document. Something they run, not something they read. That one change turned our lead magnet from a PDF graveyard into something people actually talk about. Frequently Asked Questions How long does it take to build an AI lead magnet from scratch? The agent file took about four hours over two days. Two hours stripping down the internal workflow, one hour testing each output, one hour rewriting instructions for a general audience. The landing page and hosting took another two hours. Total: six hours from internal tool to live lead magnet. Do I need coding skills to build one? Not at all. The file is plain markdown, which is just text with simple formatting. If you can write a Google Doc, you can write a markdown file. The hosting part depends on your website, but most platforms let you upload a file and link a button to it. Will this work with ChatGPT or does it only work with Claude? The markdown agent approach works with both. Claude handles structured instructions in markdown files especially well, but ChatGPT can read them too. I recommend Claude because the file-drag-and-drop experience is smoother. But test with both and see which gives your audience better results. What kind of workflows make good AI lead magnets? Anything with a clear input and a clear output. Meeting transcripts in, organized notes out. Raw campaign data in, performance summary out. Client brief in, project plan out. Stay away from workflows that need live API access or login credentials. The file has to work with just a paste. How do I track downloads and conversions? We track the download button click as an event in our analytics. The email capture happens through our newsletter signup, which tags the subscriber. Inside the welcome sequence, the "want a custom build" email tracks replies. That is our conversion event for this funnel. What if my audience does not use AI tools yet? That is actually the best scenario. Your lead magnet becomes their first AI experience. The seven-step instructions walk them through it. Once they see it work on their own meeting transcript, the "AI is complicated" barrier disappears. You just gave them a reason to start. How often should I update the agent file? Test it every month with a fresh transcript. AI models improve, and sometimes your instructions need adjusting. I update ours roughly every six weeks. If something stops working, it is usually because the model got better at following instructions and your old workarounds are now causing issues. Can I put a CTA inside the AI agent itself? Yes, and you should. I include one line at the end of every output: "Want a custom AI workflow built for your team? Email virgil@virgilbrewster.com ." It appears once, at the bottom, after they already got their result. It is not intrusive because they just received value. How does this compare to a free trial as a lead magnet? A free trial requires signup, onboarding, and a time commitment. Most people sign up and never log in. An AI lead magnet takes two minutes. Drag, paste, run. They get value before they even decide if they like you. Can I use this approach for B2B and B2C? The approach works for both. B2B lead magnets tend to process business data, like meeting notes, campaign reports, or client briefs. B2C lead magnets work better with personal inputs, like meal plans, workout routines, or budget analysis. The principle is the same: give them a tool, not a document. --- ## I Vibe Coded a Newsletter Signup Without Writing Code URL: https://sucana.ai/blog/vibe-coding-newsletter-signup Published: 2026-03-17 Author: Virgil Brewster Tags: ai, automation, agencies How Do You Vibe Code a Website Feature Without Writing Code? I open Claude Code, describe what I want in plain English, and let it build. I added a newsletter signup component to all 24 blog posts on sucana.ai in 20 minutes. I typed zero lines of code. Claude Code handled the component, the auto-injection logic, the styling, and the Beehiiv API connection. I just talked and made decisions. The Jarvis Thing I grew up watching Iron Man. Tony Stark talks to Jarvis, tells it what he wants, and it builds the thing. I always thought that was the coolest part. Not the suit. The conversation. For years I kept thinking about that. Every time I opened a code editor or hired a developer for something that should take five minutes, I thought: where is my Jarvis? I do not have the glass lab overlooking the mountains. I am sitting at my desk in Bali with a cup of coffee. But I have something Tony Stark would recognize. I talk to Claude Code and it builds what I describe. Not tomorrow. Right now, while I am talking. That is vibe coding. You describe the vibe. The AI writes the code. It is the same approach I use for everything from content automation to reporting at Sucana. Here is what happened today. What I Wanted to Build Victor my co-founder looked at our blog analytics and said: "People read the first half and leave. Nobody scrolls to the bottom." I said: "So if we want newsletter signups, the form needs to be in the middle, not at the end." He nodded. "But you have 24 articles. You are not going to edit all of them manually, right?" I was not. That is exactly the kind of task I refuse to do manually. Step 1: First I Needed Data I typed: "Research where newsletter signups convert best on blog posts in 2026." Claude pulled data from Wisepops, OptinMonster , and Sumo. Mid-article inline forms convert 5-10%. End-of-post forms convert 1-2%. Popups convert higher but annoy people. Sticky bars sit around 3%. That made the decision for me. Middle of the article. Where people are still reading. The same kind of data-first decision making Victor applies when he reads campaign performance numbers . Step 2: One Function for 24 Articles I was not going to open 24 files and paste the same component into each one. So I told Claude: "Auto-inject it at the midpoint of every post. I do not want to edit 24 files." Claude wrote a function that finds the halfway point of each article, splits it at a clean paragraph break, and drops the signup box in between. One function. All 24 articles. I did not touch a single file. That is the whole point of vibe coding. You describe the outcome. The AI handles the labor. Step 3: Dark Charcoal, Not Purple The site already has a lot of purple. I told Claude: "Make it dark charcoal, not purple." Three options came back with trade-offs. I picked dark charcoal. It built the component with a dark card, white email field, and a purple button that matches the Sucana brand. Then I looked at the button. "Make the button round on both sides, not square on one end." One sentence. Fixed. No mockups. No Figma file. No designer explaining border radius values. Step 4: The Copy Was Mine This is the part the AI should not decide. I dictated: "Sign up for The Lead. The AI newsletter for marketing agencies. One AI experiment per week. We break it down so you can steal it." Claude put the words in. Make the button say "Join hundreds of marketing agencies." Updated. Make the mobile text bigger. Bumped up. Three rounds, each one a single sentence from me. About two minutes total. Nobody knows my audience better than I do. The AI handles the code. I handle the words. Step 5: The Beehiiv Tag Almost Did Not Exist Victor asked me: "How do we know who signed up from the blog versus LinkedIn?" I had not thought about that. I asked Claude: "Can you check in Beehiiv that everybody coming through the blog gets a tag blog-signup?" Claude looked at the API code and found there was no tagging at all. Beehiiv needs a separate API call for tags. So it added one. I said "test the connection." Claude hit the Beehiiv API from the terminal. Created a test subscriber. I watched it show up in Beehiiv with the right tag. Confirmed. Deleted the test. All without opening a browser. One question saved months of messy data. Step 6: Push It Two words. One command. Vercel auto-deployed. Live on all 24 articles within 60 seconds. Twenty minutes from idea to live on the website. What Vibe Coding Actually Feels Like I sent Victor the live link. He clicked through three articles. The signup was there, in the middle, on every single one. "You did this in 20 minutes?" he asked. "Twenty minutes." "At the agency, this would have been a ticket. Then a meeting about the ticket. Then a designer. Then a developer. Then QA. Then a release window." Tony Stark never had to open Jira, write a ticket, wait for a sprint, or explain to a developer what "round on both sides" means. He just said it and Jarvis made it happen. That is what this felt like. I talked. It built. I decided. It shipped. The thing most people miss about vibe coding is that it is not about AI writing code. It is about a founder making decisions at the speed of conversation. Every prompt I typed was a decision. Where to place it, what color, what copy, how to tag subscribers. The AI just executed faster than any developer could. That is the skill every marketer actually needs in 2026 : knowing what you want and telling the machine clearly. I am not a developer. I do not want to be one. I want to describe what I see in my head and have it exist on the internet 20 minutes later. The glass lab and the mountain view would be nice. But honestly, Jarvis was the part that mattered. And that part is real now. What I Would Do Differently Victor messaged me the next morning. "We got 11 signups overnight from the blog. But I cannot tell which articles convert best." He was right. I would have asked Claude to build a conversion counter from day one. Right now I can see total signups in Beehiiv, but not the conversion rate per article. I would also have asked Claude to A/B test the copy from the start. Three rounds of copy changes, but no way to measure which version actually converts better. Next time, I build the test before I push. Frequently Asked Questions Do I need coding experience to vibe code? Not at all. I have zero coding experience. I describe what I want in plain English. Claude Code handles the implementation. If something looks wrong, I describe what is wrong and it fixes it. The skill you need is not coding. It is knowing what you want. Clear instructions get clear results. What is the difference between vibe coding and using a no-code tool? No-code tools give you templates and drag-and-drop builders. You work within their constraints. Vibe coding has no constraints. You describe exactly what you want and the AI builds it from scratch. I could not have auto-injected a component at the midpoint of 24 articles with any no-code tool. That required custom code. I just did not have to write it. How long did this actually take? Twenty minutes from first prompt to live on all 24 articles. The longest part was the copy. I spent about five minutes going back and forth on the button text and mobile font size. The technical build, the component, the auto-injection, the Beehiiv integration, took maybe eight minutes total. What tools do I need? Claude Code (the CLI version of Claude), a code editor like VS Code or Cursor, and a website you can deploy to. I use Next.js on Vercel, but the approach works with any stack. You also need a Beehiiv account if you want newsletter signups, but that part is specific to my setup. Can I vibe code anything or just simple features? I have vibe coded full blog deployments , LinkedIn content automation , and an AI analytics dashboard. The newsletter signup was a simple one. But the principle scales. The more specific you are about what you want, the better the output. Vague prompts get vague results. What if Claude Code builds something wrong? You tell it what is wrong. "The button is square on one end, make it round on both sides." That is it. The feedback loop is instant. You do not file a bug report. You just say what you see and it fixes it. In my 20-minute session, I made four corrections. Each one took less than 30 seconds to describe and fix. Is vibe coding just for developers who want to code faster? The opposite. Vibe coding is most powerful for people who cannot code at all. Developers already know how to build. Founders and marketers who know what they want but cannot implement it, those are the people who get the most value. I went from "I need a developer for this" to "I will just tell Claude" in one afternoon. That shift changed everything about how fast we ship at Sucana . How do I know the code Claude writes is good? I do not read the code. I test the output. Does the signup form appear in the middle of every article? Yes. Does the Beehiiv tag show up when someone subscribes? Yes. Does it look right on mobile? Yes. If the result works, the code is good enough. I am not trying to win a coding award. I am trying to ship features. What was the conversion rate after adding the signup? Too early to share real numbers. The signup went live today. I will update this article once I have 30 days of data. What I can say is that mid-article placement consistently outperforms end-of-post placement by 3-5x based on the research Claude pulled. Can I use this approach with ChatGPT instead of Claude Code? ChatGPT can help you plan and write code snippets, but it cannot execute commands, push to GitHub, or test API connections directly. Claude Code runs in your terminal and does all of that. For vibe coding where you want to go from idea to deployed in one session, Claude Code is the tool that makes it work end to end. --- ## How to Run a Marketing Agency With AI URL: https://sucana.ai/blog/ai-powered-agency-playbook Published: 2026-03-15 Author: Virgil Brewster Tags: ai, agencies, automation How Do You Run a Marketing Agency? I run a marketing agency by identifying the repetitive tasks that burn the most hours and automating them first. Victor taught me this after years of running his own agency. We build AI skills for each workflow, test them on real client work, then scale what works. Start with the bottleneck. Find the task your team does every week that follows the same steps and eats the most hours. Then automate that one task. Not five. Not an entire agency OS because some kid on YouTube built one in five minutes. One task. Test it. Fix what breaks. Get your team used to it. Then move to the next one. Three Guys Beat the Enterprise Vinod my co-founder works at a large company during the day. His team wanted to use Claude for a project but could not. A Microsoft enterprise agreement locks them in. To switch to Anthropic they would need a master agreement. That takes months. Victor my co-founder heard this and laughed. "That's why we can pack big companies now. They're so slow and so complex." "Three guys can outpace them." He was not bragging. That is what we live every day at Sucana. Three founders building an AI analytics platform for lead gen agencies. No 50-person team, no procurement department. When we want to try a new AI model, we try it that afternoon. The speed difference is not about talent. It is about permission. Big companies need six meetings to approve a tool. A Slack message is all we need. That is the real advantage of building an AI-powered agency. Not the AI itself. The speed at which you can move when you are small and willing to change. I wrote a full guide on how to build an AI-powered marketing agency from the ground up. This article is different. This is about the order that works, the mistakes that waste money, and what I learned building Sucana. Where AI Fits (And Where It Fails) The first mistake I see agencies make is looking at what AI can do and trying to find places to use it. I made that mistake myself. What works is the opposite. Look at where your team wastes time. Then ask if AI can take that off their plate. The best tasks for AI share four things. Weekly cadence. Same steps every time. Data inputs, not judgment calls. And output the client sees. Reporting fits all four. Creative strategy fits zero. I started with reporting. Victor my co-founder spent two hours per client every Monday pulling numbers from Facebook Ads Manager into Google Sheets. Five clients. Ten hours. Every single week. That is the kind of task AI was built for. I wrote the full system for automating client reporting with AI . It cut the time to about 40 minutes per client. The key was not replacing Victor. It was giving him back the hours he was wasting on data entry. That distinction matters -- the data on whether AI is replacing marketers shows it is the tasks that disappear, not the people. The Order That Works I have watched agencies add AI in every possible order. Most do it wrong. Five tools on the same day. Performance Max turned on. All copy from ChatGPT. Automated bidding across everything. Then they wonder why results got worse. The order matters more than the tools. Data first. Before AI can analyze anything, your data needs to be clean. Consistent naming in your ad accounts. UTM parameters that match your CRM. One source of truth for leads. I spent two weeks cleaning up our naming before I let any AI touch the data. It felt like wasted time. It saved me months of wrong answers. Analysis second. Once your data is clean, feed it to AI for analysis. Not execution. Analysis. Let the AI read your campaign data and tell you what happened. Phase transitions. Geographic spending patterns. Creative fatigue signals. The kind of detail that takes hours to find manually. This is where AI earns its keep. It reads a full quarter of data in seconds. Victor saw this when our AI found a geographic scaling trap he missed. Spain's budget grew 37 times but leads only increased 9 times. Eight years of experience and he did not catch it. Execution last. Only after your data and analysis layers work do you add AI to execution. Ad copy and bid adjustments. Creative testing at scale comes last. The agencies that skip straight to execution are on Reddit complaining that "nothing is reliable." They are right. AI execution without clean data is a liability. What It Costs (Honest Numbers) Most articles about AI agencies skip the cost conversation. I will not. The tools are cheap. Claude costs $20 a month, ChatGPT is the same. Most automation platforms have free tiers. The expensive part is setup time. That is the cost nobody talks about. I spent four weeks building our first AI reporting workflow. Hitting walls, rebuilding prompts, fixing data formats, starting over. The frustration was the education. After those four weeks, the workflow saves ten hours a week. The math works out fast. But the setup period is real. The second hidden cost is human review. AI does not produce finished work. It produces drafts that need a human eye. Every AI report still needs someone to check the numbers. Context the AI cannot know has to come from a person. I budget about 30% of the time savings for review. If AI saves an hour, the review takes 20 minutes. Net savings are real but not as dramatic as the marketing says. How to Pitch AI to Clients This is the part most agencies get wrong. They lead with the technology. "We use AI to run your campaigns." That sentence makes clients nervous. They picture a chatbot replacing their account manager. I wrote a full guide on how to pitch AI services to agency clients . The short version: lead with the result, not the tool. "We catch campaign problems in three days instead of two weeks." That is the pitch. How you do it is the backstory. Victor showed one of our AI reports to his client Pablo. The reaction was not about AI. Pablo asked when the next report was coming. Depth of insight sold itself. The agencies losing client trust are the ones explaining AI before showing results. Show the report first. How to Get Your Team to Use AI Buying AI tools is easy. Getting your team to use them is the hard part. I wrote a full guide on marketing team AI adoption strategy . The biggest lesson: do not start with training. Start with one person and one task. Pick the team member most frustrated with their workload. Then identify the task they complain about every week. Build one AI workflow for that task. Let them use it for 30 days. When they tell the rest of the team it saved five hours a week, adoption happens on its own. Nobody needs a training deck when a colleague leaves the office two hours earlier. The mistake is rolling out AI to everyone at once. That creates resistance. One person, one task, one win. Expand from there. How to Measure Whether AI Is Working Most agencies add AI and never track the difference. They feel faster, think results improved. Nobody puts a number on it. I track three things. First, time per task. Before AI versus after. Put those numbers on a spreadsheet and track monthly. Second, error rate. Not "slightly off" but wrong enough that a client would notice. If that number is climbing, your prompts need work. Third, client feedback. Not surveys, direct reactions. Pablo asked for his next report unprompted. That was the signal. If your AI tools do not move at least one of those three numbers, they are adding complexity without value. I built a full system for proving marketing ROI with AI . The same principles apply to measuring AI inside your agency. The Part Nobody Talks About AI breaks. Often. We opened Sucana for testing on March 2. Full team call. Victor typed a message and the screen reset. Nothing. Vinod checked the backend. Same error. It was not us. Claude's API was down everywhere. Every business built on that tool lost an hour of their day. That moment taught me something. Building on someone else's technology is a dependency. You need a plan for when the AI goes down. For us, Victor can still run his reports manually. The old process is slower, but it works. We parked the boats nearby instead of burning them. The agencies that go all-in on AI with no manual fallback are one outage away from missing a client deadline. What an AI-Powered Agency Looks Like in Practice The daily reality is less dramatic than the articles make it sound. Monday morning, I check what the AI analyzed over the weekend. Campaign performance, geographic spend patterns, creative fatigue signals. Fifteen minutes instead of two hours. Then I look at what the AI flagged. A CPL spike in one market, a creative running three weeks without a refresh. Budget that shifted to Display when the client wanted Search. The AI surfaces things I might miss. I make the calls. That is the model. A layer of AI doing the heavy data work so humans can focus on judgment and client relationships. Victor put it well. The AI does not just recommend the best-performing number. It recommends the one you can iterate on without breaking your production budget. That kind of thinking separates a useful AI workflow from a tool that sounds good in a demo. Where to Start If I had to start over today, I would do four things in the first month. Week one: find the bottleneck. List every task your team does weekly. Pick the one that takes the most hours, follows the same steps, and produces output clients see. I walk through this exact process in my guide on where agencies should start with AI automation . Week two: automate that one task. Use Claude, ChatGPT, or whatever tool fits. Build a simple workflow. It will not be perfect. That is fine. Week three: test and fix. Run it alongside the old process. Compare the output. Fix what breaks. Let your team get comfortable with it. Week four: measure. Put the old time and the new time side by side. If the numbers work, roll it out for real. If not, fix it before moving on. One month. One task. One win. Do not build an agency OS. Do not automate five things at once. Do not follow the YouTube kid who says he built it all in a weekend. Start small. Get your people used to it. Then expand one task at a time. When you are ready to go further, I documented how I built a full AI brain for the agency using Claude Code — the architecture, the skills, and how it all connects. Frequently Asked Questions How do you build an AI-powered marketing agency from scratch? Start with one repeatable task, not with AI tools. Identify the workflow that eats the most hours every week, usually reporting or data analysis. Build one AI workflow for that task. Test it with one client for 30 days. Measure the savings, then expand. What AI tools do marketing agencies actually use? Most agencies use Claude or ChatGPT for campaign analysis and ad copy. Automation platforms like n8n or Make handle data connections between ad platforms and reporting tools. The tools matter less than how you use them. A $20 Claude subscription with good prompts beats a $500 platform with generic ones. How do you pitch AI services to agency clients without scaring them? Never lead with the technology. Lead with the result. "We catch campaign problems in three days instead of two weeks" is a pitch. Show clients a report with depth they have never seen. The reaction will be about the insight, not about what produced it. How much does it cost to add AI to a marketing agency? The tools cost $20 to $40 per month. Automation platforms often have free tiers for small volumes. The real cost is setup time. Expect four to six weeks of building and testing your first workflow. Budget 30% of time savings for ongoing human review. Will AI replace marketing agencies? AI will replace tasks that involve pulling data, formatting reports, and generating ad copy at scale. Those are already faster with AI. Strategy, client relationships, and judgment calls about what the data means are still human. The role changes shape, but agencies that add AI get stronger. How do you automate client reporting with AI? Pull campaign data into a structured format. Feed it to AI asking three questions: what happened, why, and what to do next. The process takes about 40 minutes per client instead of four hours. AI handles data reading while you add context and judgment. What tasks should agencies automate with AI first? Start with reporting. It runs every week, follows the same format, and the output is visible to clients. Stay away from creative strategy and client communication early on. AI needs patterns and structured inputs, and strategy gives it neither. How do you train a marketing team to use AI? Do not start with training. Start with one person and one task. Pick the team member who complains most about their workload. Build one workflow. Let them use it for 30 days. When they share the time savings, the rest of the team follows. How do you price AI-powered agency services? Do not discount because AI does some of the work. The value to the client stays the same no matter what produced it. Price based on the outcome. Deeper analysis, faster problem detection, better recommendations. Catching a budget leak in three days instead of fourteen is worth more, not less. What is the ROI of AI for marketing agencies? AI cuts reporting time by about 75% in our experience. What took two hours per client now takes about 40 minutes. The real ROI is what you do with the saved time. Deeper analysis, faster problem detection, and catching issues before clients notice them. What happens when the AI gives wrong answers? It happens regularly. AI produces drafts, not finished work. Every output needs a human check. Track error rates monthly. If wrong answers climb, your prompts need better context. Feed the AI your client's business details, not just campaign data. Can a small agency compete with large agencies using AI? Small agencies have the advantage. New tools get adopted the same day. Large agencies need months of procurement and approvals. Three people with AI and clean data can produce analysis that matches a ten-person team doing everything by hand. How long does it take to see results from AI in an agency? Reporting improvements show up in the first week. Your first AI report will be faster and more detailed than your manual process. Campaign performance improvements take 60 to 90 days as the AI spots patterns across enough data. What is the biggest mistake agencies make when adding AI? Automating everything at once. AI bidding, AI copy, AI reporting, and AI creative testing in the same week. Impossible to tell what is working. Add one AI layer at a time. Start with reporting, measure the result. Then add the next layer. Do clients care whether their agency uses AI? Most clients care about the result, not the tool. When they see their own data explained at a depth they have never experienced, the AI question disappears. Results first, always. --- ## AI for Agencies: How to Adapt to AI-Powered Search URL: https://sucana.ai/blog/agencies-adapting-ai-search Published: 2026-03-14 Author: Virgil Brewster Tags: ai, automation, agencies How Are Digital Marketing Agencies Adapting to AI Search? We see agencies adapting to AI search in three ways: restructuring content for AI overviews, building answer-first blog strategies, and tracking AI citation metrics. Victor works with agencies daily and most are still behind. The ones winning treat AI search as a new channel, not a tweak to old SEO. Most are not. That's the honest answer. I've been watching this since AI Overviews started cutting organic clicks in 2025. The agencies moving fast made one call: stop treating Google as the only game. They started building content that ChatGPT and Perplexity actually cite. The ones waiting to see how it plays out? Down 20-40% in organic traffic and wondering what happened. What I Saw When I Searched for Sucana in ChatGPT When we started building Sucana, I typed our five core questions into ChatGPT and Perplexity. The questions our future clients would actually search for. I wanted to see where we stood. Nothing came back. Not one mention. Not a single citation. We were invisible to the AI layer before we had even launched. That was clarifying. Not discouraging. It told me exactly what the problem was and where to start. Here is the uncomfortable part: I know exactly what needs to happen. I have the full system mapped. But I am building a product right now, not running a content machine. Sucana is not ready to show people yet and I am sitting on a strategy I cannot fully execute. So I ran the individual steps on what content we did have. I tested each move separately. I tracked what shifted. The results were consistent enough that I am confident in the system. Structure matters more than volume. One well-structured page with the answer in the first 200 words will get cited before a well-written page that buries the answer at the bottom. The same lesson applies when you create AI SOPs for your agency : clarity beats complexity every time. Here is the process, in order. Step 1: Pull Your Traffic Data and Find What AI Is Eating Open Google Search Console . Filter by the last 6 months and sort by impressions drop. You are looking for pages where impressions stayed flat or grew but clicks dropped. That gap is AI Overviews answering the question before anyone clicks through. I found three pages like this in the first ten minutes. All informational pages, all ranking well. All getting fewer clicks than six months ago because Google was now answering the question at the top of the page. Step 2: Search Your Core Topics in ChatGPT and Perplexity Before changing anything, do this: type your five most important client questions into ChatGPT and Perplexity. The questions your clients actually ask you. See whose content comes up as a source. If it is not yours, read what does come up. That is your benchmark. I did this for Sucana's core topics. None of our content appeared in the first round. That told me exactly what needed to change. Step 3: Move the Answer to the First 200 Words Open each page that is losing clicks. Read the first 200 words out loud. If you hit 100 words before actually answering the question, that page is invisible to AI. ChatGPT and Perplexity skip past all that context. They pull from wherever the direct answer starts. I moved the answer to line one on five pages. No other changes. Three of those pages showed up in Perplexity answers within six weeks. Step 4: Change Every Section Heading to a Question Go through your page headings. Every H2 and H3 that reads like a label needs to become a question. "Our Approach to AI Search" becomes "How do agencies get cited in AI Overviews?" "Key Benefits" becomes "What does GEO actually do for your organic traffic?" AI systems match content to queries. When your heading is the question someone typed, the match is immediate. When it is a generic label, the system has to guess. I renamed seven headings across three pages. The language felt more natural to write too, because it matched how people actually talk. Step 5: Add a Real FAQ Section to Every Important Page Not a fake FAQ with questions nobody asks. Real questions from the people who find your page. Go to Google. Type your main keyword and look at "People Also Ask." Write down every question that appears. Those are the exact queries triggering AI Overviews on your topic. Write 10 of them into a proper FAQ section at the bottom of the page. Two to four sentences per answer. Direct and specific. No padding. By 2025, 73% of B2B websites saw major traffic losses with an average drop of 34% year-over-year. The sites holding ground all had structured FAQ content that AI could extract. Step 6: Add Structured Data Markup This one is technical but it takes 20 minutes with a plugin or a developer. If you want to see how AI handles the technical work, I walked through exactly how vibe coding builds real features without writing code. Add Article schema and FAQPage schema to every page you updated. This tells Google and AI crawlers exactly what kind of content they are looking at and which parts are questions with answers. AI Overviews strongly favor pages with structured data. Without it, a well-written page can still get skipped for a worse one that is properly marked up. Step 7: Build Your Entity Presence Across Multiple Sources An entity is how AI systems recognize you as a real, trustworthy source. Your name, your agency, your product need to appear consistently across more than just your own website. Write one guest post per month on a relevant publication. Appear on one podcast per quarter. Answer questions in Reddit threads in your space. Get your name into real conversations outside your own domain. One study analyzed over 30 million AI citations. Reddit accounts for 21% of all sources in Google AI Overviews . AI pulls from wherever the clearest, most trusted answer lives. Your agency blog can compete. But it needs to be one of several places your voice shows up. Step 8: Track AI Citations Manually Every Week No tool needed for this. 15 minutes, once a week. Open ChatGPT and Perplexity. Type the five questions your clients ask most. Write down whether your content appears as a source. If something else appears, note what it is and why it might be getting cited instead. I keep a simple spreadsheet for this. Date, question, source cited, notes. After eight weeks you will see patterns. You will know exactly which pages are working and which ones need another pass. Step 9: Change What You Measure Rankings in Google alone will not tell you if this is working. You need two more numbers. First: track direct traffic as a percentage of total. When AI answers bypass your site, direct traffic and newsletter sign-ups become the real measure of brand strength. If that number is growing, AI search is working for you even when organic clicks stay flat. Second: track brand mention share. Ask ChatGPT "who are the best agencies for X" every month. Log whether your name appears and how high. This is the new version of keyword ranking. I wrote about proving marketing ROI to clients with these exact metrics. Step 10: Repeat on the Next Batch Once citations start appearing on your first five pages, move to the next five. Do not do everything at once. The feedback loop is the point. You update a page, you track whether it gets cited, you learn what works in your specific niche, and you apply that to the next batch. The agencies making the most progress are not the ones who overhauled everything in one sprint. They are the ones running a consistent weekly cycle. If you want the full playbook on building an AI-powered agency, start with how to build an AI-powered marketing agency . Frequently Asked Questions How much organic traffic are digital marketing agencies losing to AI search? The numbers vary by industry but the direction is the same everywhere. By 2025, 73% of B2B websites saw significant traffic losses with an average decline of 34% year-over-year. Agencies focused on informational content got hit hardest. Some sectors saw drops of 15-64% since AI Overviews launched at scale in 2024. What is GEO and why do agencies need it now? GEO stands for Generative Engine Optimization. It is the practice of structuring content so AI systems pull from it when generating answers. ChatGPT, Perplexity, and Google AI Overviews all work this way. Traditional SEO got you to the top of Google. GEO gets you cited inside the AI answer, where the click never happens but the brand impression does. Agencies that skip it are invisible to half their potential clients. How do I know if AI Overviews are hurting my organic traffic? Open Google Search Console and look for pages where impressions are flat or growing but clicks are dropping. That gap is AI answering the question before anyone clicks through. The effect is largest on informational pages. Product pages and service pages are less affected because AI systems send those queries to transactional results. Is traditional SEO still worth investing in for agencies? Yes. GEO and traditional SEO are not either/or. AI Overviews strongly favor pages that already rank well in traditional search. Strong domain authority, clean site structure, and quality backlinks feed directly into AI citation probability. Agencies that abandon SEO for GEO end up with neither. How long does GEO take to show results? Faster than most agencies expect. I moved the answer to the top of five pages with no other changes. Three appeared in Perplexity answers within six weeks. Consistent citation takes longer. Building entity authority across multiple sources is a three to six month process, not a one-page fix. What types of content get cited by AI systems most often? Structured content outperforms plain prose. FAQ sections, numbered steps, comparison tables, and clearly labeled sections all raise citation rates. Specific, numbered claims get cited at 40% higher rates than vague statements. "Our reporting time dropped from 8 hours to 90 minutes" gets cited. "Our reporting improved significantly" does not. Do agencies need a separate GEO strategy or can they adapt their existing SEO? No separate strategy needed. The changes are additions to what you already do, not a replacement. Move the answer to the top of existing pages. Rewrite generic headings as questions. Add FAQ sections. Add structured data. The SEO foundation stays the same. The formatting layer changes. How do agencies measure AI search visibility without paid tools? Search your core questions in ChatGPT and Perplexity weekly. Log whether your content appears as a source. That manual check takes 15 minutes and is more useful than most paid tools right now. Track it in a simple spreadsheet: date, question, who got cited. What is the biggest difference between ranking on Google and getting cited by AI? Google rewards relevance signals: keywords, backlinks, click-through rates. AI systems reward clarity and structure. They pull the most direct, well-organized answer to a question, no matter the domain authority behind it. A well-structured page from a smaller agency can beat a vague answer from a major publication. Should agencies be worried about AI replacing their organic traffic entirely? Worried, no. Prepared, yes. The shift is real but not total. Paid search, direct traffic, referrals, and email remain strong channels. The agencies holding ground treat AI search the same way they treated mobile ten years ago. A new surface to show up on. Start with your top ten pages. Get those cited. Build from there. --- ## AI for Performance Marketing: The Complete Guide URL: https://sucana.ai/blog/ai-performance-marketing-pillar Published: 2026-03-14 Author: Virgil Brewster Tags: ai, media-buying, agencies, roas How Do You Actually Use AI for Paid Ads? I use AI for paid ads in three layers: automated bidding, creative testing, and audience discovery. We let AI handle bid adjustments and budget allocation across campaigns. Victor's agency clients see the best results when AI runs the math and humans control strategy and creative direction. You use it where it sees things you miss. And you stay in control where it cannot think. AI reads campaign data faster than any media buyer. It catches patterns in spend, geography, and creative performance that take hours to find manually. But it does not know your client's business. It cannot make judgment calls about what those patterns mean. The agencies winning with AI are not the ones automating everything. They are the ones who know which parts to hand over and which parts to hold. The data on whether AI is actually replacing marketers backs this up. Victor Is Above Average. The AI Still Found What He Missed. Victor my co-founder runs a PPC agency. He manages campaigns across Google, Meta, and TikTok for clients spending real money. Eight years of media buying. He knows when a campaign is dying before the numbers confirm it. One day he ran a full campaign history through our AI. He did not tell it his strategy. He did not explain how he runs campaigns. He just fed it the data. The AI came back and described four phases: launch, scale, saturation, recovery. In the exact order Victor had experienced them. It understood that he launched, scaled aggressively, watched CPL rise, and pulled back the budget. Then it found something else. A landing page conversion drop he had not noticed. A geographic scaling trap where Spain's budget grew 37 times but leads only increased 9 times. These numbers came from a 90-day campaign history across one client's Google and Meta accounts. Victor looked at the screen and said his exact words: "It's real that I'm above average. I'm not the best, but I'm above many media buyers and I wouldn't see that." Victor has eight years of experience. If the AI found what he missed, the question is not whether to use it. The question is where. Here is how it works across every stage of running paid ads. How AI Changes Ad Copy Writing ad copy is the first thing most agencies try with AI. It is also where the limits show up fastest. Google Ads has a 30-character headline limit . Most AI tools ignore that. They write headlines that are 15 characters too long. I wrote the full process for getting AI to write Google Ads copy that actually fits . The fix is giving AI context first. Your client's campaign history. Their landing page. Past winners. What competitors are running. Then ask for headlines. Generic prompts give generic results. The right prompts for PPC start with real numbers, not a blank page. What AI does well: generating 50 headline variations in two minutes. Testing angles you would never think of on your own. What AI does badly: understanding your client's brand voice without heavy guidance. Writing copy that sounds like a real person spoke it. The agencies getting the best results treat AI like a junior copywriter. Unlimited speed. Zero taste. You give it the brief. You pick the winners. You rewrite the almost-good ones. I tested this with a Google Ads campaign for a client in Spain. Fed Claude the landing page, the top three performing headlines from last quarter, and the target audience. It generated 40 variations in under a minute. About 30 were average. Five were bad. Five were angles I had not considered. Those five went into the next test cycle. That is the real value. Not replacing your thinking. Expanding it. How AI Changes Creative Testing on Meta Meta Ads creative testing used to mean uploading five images, running them for a week, and picking the winner. AI changed every part of that process. Now you can test Meta Ads creatives with AI that was impossible two years ago. AI analyzes which hooks get clicks, which body copy converts, and which creative formats hold CPL at scale. Victor found something interesting during a campaign review. The AI recommended a static image for scaling. Not because it had the lowest CPL at that moment. Because it held its CPL at high spend and was cheaper to produce. Victor said: "It really understands the context of an agency." The AI was not just reading performance numbers. It was factoring in production cost and speed. A video might test better in week one. But a static image you can iterate on in ten minutes beats a video that takes three days to reshoot. The best AI tools for Meta Ads in 2026 are the ones that think beyond surface metrics. Victor also taught me something about debugging creative. When a hook has strong CTR but high CPL, the problem is not the hook. People are clicking. The break is in the body copy after the hook. Keep the hook. Test five new body variants. Do not rebuild from scratch when the first line is already working. How AI Changes Reporting and Campaign Analysis This is where AI earns its keep. Every week. For every client. Victor used to spend two hours per client pulling data from Facebook Ads Manager. Dumping it into Google Sheets. Running formulas to match leads to ad spend through UTM parameters. Then recording a Loom video explaining what happened. Five clients. Ten hours a week. Just on reporting. AI changes this in three ways. First, it reads the raw data. Not just the top-line numbers. It reads campaign data the way a senior media buyer would . Phase transitions. Creative fatigue. Geographic efficiency. Spend patterns over time. Second, it explains what happened. Not "impressions went up." More like: "CPL climbed 40% during the scale phase because budget outran audience warmth in Spain." That is the level of analysis most agencies cannot produce manually for every client every week. Third, it makes one recommendation. Not three vague suggestions. One specific action based on the data. "Pause Spain targeting. Redirect to France, which generates 41% of leads at 22% of spend." I built the full system for automating Google Ads reporting with AI . Hours of manual work become minutes of AI analysis. Your judgment goes on top. I also track something I call time-to-insight. How many days between a problem appearing and us catching it. Before AI analysis, that gap was sometimes two weeks. We caught the problem when the client noticed the numbers. Now we catch it before the client sees it. The moment that sold me: Victor showed one of these reports on a Zoom call. His friend was watching. His friend said: "This is the dream for media buyers." Victor turned to camera: "99% of agencies cannot give this kind of report right now." How AI Changes Bidding and Budget Decisions Google's Performance Max and AI Max campaigns now handle bidding automatically. In Q1 2026, Google reported PMax generates 35% more conversions at 20% lower CPA . Those numbers sound great on paper. The reality is messier. A recent industry survey found that 62% of PPC managers cite black-box platforms as their top challenge . You cannot see what the algorithm is doing. You cannot explain to a client why spend shifted to Display when they wanted Search. The agencies that handle this well do two things. They use AI bidding for the heavy lifting. Let the algorithm adjust bids across millions of signals you cannot process manually. That part works. Then they layer their own analysis on top. When your Facebook CPL spikes , AI can identify that the algorithm pushed budget into a cold audience segment. You make the call on whether to pull back or let it learn. The worst move is handing everything to the algorithm and walking away. The second worst is ignoring the algorithm and doing everything manually. Both extremes lose money. What works is a middle ground. Let AI handle the speed. Keep your hands on the steering wheel. I check what the algorithm is spending on every Monday. If it shifted budget somewhere I did not expect, I look at the data before reacting. Sometimes the algorithm found something I missed. Sometimes it made a bad call. Either way, I want to know before my client does. How AI Works Differently on Each Platform Google, Meta, LinkedIn, and TikTok each use AI differently. Understanding those differences saves budget. Google Ads: Google is pushing AI Max for Search and Performance Max hard. Early adopters report a 14% lift in conversions. But Google's own reps are pressuring agencies into tools that often waste spend before they deliver results. Use AI bidding. Set tight guardrails on audience and placement. Meta Ads: Meta's Advantage+ now handles creative, sequencing, placement, and targeting automatically. The AI features in Facebook Ads worth using free up time for creative strategy. The ones to watch: broad audience defaults that burn budget on irrelevant reach. Test Advantage+ with a controlled budget before scaling. LinkedIn Ads: LinkedIn's AI tools are behind Google and Meta. But for B2B lead gen, the targeting precision is still unmatched. I wrote a full guide on using AI for LinkedIn Ads . AI helps most with audience expansion and bid adjustments. The creative and messaging still need a human. TikTok: TikTok's algorithm is the most aggressive black box of all four platforms. The creative does 80% of the work. AI helps most with generating hook variations at scale. Less about bidding, more about creative volume. The common mistake across all four platforms: treating AI as a set-and-forget button. It is not. Every platform's AI requires different inputs, different guardrails, and different review cycles. Google needs weekly bid reviews. Meta needs creative refresh every two weeks. LinkedIn needs audience testing monthly. TikTok needs new hooks constantly. Learn each platform's AI rhythm. That is how you stay in control. The Part AI Cannot Do AI cannot tell you whether a cheap lead is a good lead. Victor showed me this clearly. Spain's budget had grown 37 times. Leads only increased 9 times. CPL exploded and never recovered below 30 again. The algorithm kept spending into cold audiences while the system thought it was still in growth mode. The AI found the pattern. It told Victor the numbers. But it could not tell him why Spain was still worth targeting at a higher CPL. It could not say whether Latin America's cheap leads turned into paying customers. That answer lives in the CRM. Not the ad platform. Victor said: "If we don't have the context of the client, you don't need to just chase the cheaper cost per lead." Two different questions. The AI answers the first one. You answer the second. The same applies to prompt engineering for PPC . The difference between a useful AI output and a useless one is almost always the input. Feed it your client's business context. Not just the campaign data. How to Measure Whether AI Is Actually Working Most agencies add AI tools and never measure whether they made a difference. They feel faster. They think results improved. But they never track it. I track three numbers. First: time per report. Before AI analysis, Victor spent two hours per client report. After: about 40 minutes. We tracked this across five client accounts over six consecutive weeks, logging start and end times for each report cycle. That is measurable. Put it on a spreadsheet. Track it monthly. Second: time-to-insight. How many days between a problem appearing in the data and someone catching it. Before AI, that gap was sometimes two weeks. We caught the problem when the client asked about it. Now we catch it in one to three days. Often before the client sees the numbers. We measured time-to-insight across 4 client accounts over 2 months by logging when each issue first appeared in the data versus when we flagged it. Third: recommendations per report. Most agency reports end with "continue to improve targeting." That is not a recommendation. That is filler. AI analysis makes one specific call per report. "Pause Spain. Move budget to France." Track whether those calls were right after 30 days. If your AI tools do not move at least one of those three numbers, they are not helping. They are just making you feel modern. The agencies I trust do not talk about AI. They talk about what changed after they started using it. Where to Start If You Run Paid Ads Start with reporting. Not creative. Not bidding. Reporting. Reporting is the task every agency runs every week. It eats the most hours. And the output is visible to your clients. Set up one AI-generated report for one client. Include three sections: what happened, why it happened, one recommendation. Show the client. Victor did this with his client Pablo. The reaction was not about AI. It was about the depth of insight. Pablo asked when the next report was coming. That is the proof. From there, move to creative analysis. Let AI tell you which hooks work and which creative formats scale. Then add prompt-based copy generation for ad variations. Save bidding and budget tools for last. They require clean conversion tracking, solid data, and enough volume for the algorithms to learn. Automate the data layer first. Then the analysis. Then the execution. One mistake I see agencies make: they automate everything at once. They plug in PMax, turn on Advantage+, generate all copy with ChatGPT. Then they wonder why results are worse. The order matters. Data first. Analysis second. Automation last. 53% of PPC managers say the job is harder than it was two years ago . 62% cite black-box platforms as their biggest frustration. The agencies ahead of those numbers are not the ones using the most AI. They are the ones using it in the right order. Frequently Asked Questions Can AI actually manage Google Ads campaigns end to end? AI can manage bidding, budget allocation, and placement decisions across Google Ads. Performance Max handles this automatically across Search, Display, YouTube, and Gmail. What AI cannot manage is strategy. Which audiences to target. What message to lead with. When to pull back spend on a market that is not converting. That is still your call. How do PPC agencies use AI without losing control? Layer your control. Let AI handle high-frequency decisions like bid adjustments and placement. Keep human oversight on strategy, creative direction, and client communication. The agencies losing control are the ones who turned everything on and walked away. Will AI replace PPC managers? AI will replace the tasks that involve pulling data, formatting reports, and testing ad variations at scale. Those are already faster with AI. AI will not replace the parts that require client context, judgment calls, and creative strategy. The role changes shape. It does not disappear. What data should you feed AI before writing ad copy? Campaign history. Landing page content. Past winning headlines. Competitor ads. Your client's brand voice guidelines. The more specific you are, the better the output. Never ask AI to write ad copy from a blank prompt. How do you automate Google Ads reporting with AI? Pull campaign data into a structured format. Feed it to AI with a clear prompt: what happened, why it happened, what to do next. The full system takes about 40 minutes per client instead of 4 hours. I built this at Sucana and documented the entire process. Is Performance Max better than manual campaigns? Google says PMax generates 35% more conversions at 20% lower CPA. Those are averages across all advertisers, not specific to your account. For lead gen agencies with clean conversion tracking, PMax often outperforms on efficiency. For agencies with messy data or niche audiences, manual campaigns give more control. Can ChatGPT write Google Ads headlines that fit the 30-character limit? Not by default. Most AI tools ignore character limits and produce headlines that are too long. Include the character limit in your prompt. Ask the AI to confirm each headline fits before presenting it. How is AI changing Meta Ads creative testing in 2026? AI analyzes creative performance at a level most humans cannot match at speed. It identifies which hooks drive clicks, which body copy converts, and which formats hold CPL at scale. The biggest shift is testing volume. What used to take weeks now takes days. What AI tools work across both Google and Meta Ads? Most native AI tools are platform-specific. Google has AI Max and PMax. Meta has Advantage+. Cross-platform analysis requires a tool that pulls data from all platforms into one place. That is what we built at Sucana. How do agencies prove value when clients think AI does everything? Show them a report they have never seen before. Not a pitch about AI. An actual report with specific numbers, clear analysis, and one recommendation. When clients see their own data explained at that depth, the AI question disappears. Is AI-generated ad copy allowed by Google Ads policies? Yes. Google does not ban AI-generated copy. The ads still need to meet all standard policies on accuracy, trademarks, and prohibited content. The real risk is generic copy that does not convert because it lacks your client's specific context and voice. How long does it take to see results from AI in paid media? Reporting improvements are immediate. Your first AI report will be faster and more detailed than your manual process. Creative testing improvements show up in 2-4 weeks. Campaign performance improvements compound over 60-90 days as patterns emerge from the data. What is the biggest mistake agencies make when adding AI to paid media? Automating everything at once. They turn on PMax, Advantage+, AI copy generation, and automated bidding all in the same week. Then they cannot tell what is working and what is hurting. The better approach: add one AI layer at a time. Start with reporting. Measure the difference. Then add creative testing. Then bidding. How much does AI save agencies on reporting time? In our experience, AI cuts reporting time by about 75%. What took Victor two hours per client now takes about 40 minutes. That percentage is based on timed comparisons across five client reports over six weeks. The time saved is not the real value. The real value is what you do with that time. Victor uses it to dig deeper into the data and find problems earlier. Do clients care whether you use AI for their campaigns? Most clients do not care about the tool. They care about the result. When Victor showed his AI-generated report to Pablo, the reaction was about the depth of the analysis. Not about what built it. The agencies losing client trust are the ones explaining AI before showing results. Show the result first. Explain the tool never. --- ## AI LinkedIn Post Generator: How to Create Ads That Convert URL: https://sucana.ai/blog/how-to-use-ai-for-linkedin-ads Published: 2026-03-13 Author: Virgil Brewster Tags: ai, linkedin-ads, media-buying, b2b, agencies What AI Features Work for LinkedIn Ads? The AI features that actually work for LinkedIn Ads are Predictive Audiences for targeting and Accelerate campaigns for fast setup. I tested both on live B2B campaigns with Victor. Predictive Audiences cut our CPL by more than half. Accelerate campaigns saved setup time but needed manual guardrails on budget and audience exclusions to perform. LinkedIn's own AI tools handle most of the heavy lifting now. Predictive Audiences, Accelerate campaigns, and AI-generated ad variants cover targeting, setup, and creative in ways that didn't exist 18 months ago. I tested these features on real B2B campaigns. Some cut CPL by more than half. Others burned budget faster than manual campaigns ever did. Most "AI for LinkedIn Ads" guides list features without testing them. This is what happened when real money was on the line. The Story That Changed How I Think About LinkedIn Ads Victor my co-founder runs ads for B2B clients. Big accounts. His method for years was simple: organize campaigns by awareness levels and micro avatars, then let the platform optimize from there. When he applied that same structure to LinkedIn, something clicked. His exact words: "This method of awareness level is crazy. I'm cutting by two, three the cost per lead from the last launch." CPL dropped by 66%. Not from a fancy tool. Not from a new AI feature. From organizing his campaigns around awareness levels and letting LinkedIn's algorithm work with clear creative signals instead of fighting tight targeting. That's the point of AI for LinkedIn Ads. Not replacing your strategy. Giving your strategy a structure the algorithm can work with. I cover how this applies across LinkedIn, Google, and Meta in my AI for performance marketing guide. LinkedIn's Predictive Audiences: The Biggest Change in B2B Targeting Predictive Audiences use machine learning to find LinkedIn members most likely to convert. You feed it your conversion data or a contact list, and the algorithm identifies behavioral patterns that predict who will take action. Early results show 21% lower CPL compared to standard professional targeting. That number matters because LinkedIn is already the most expensive ad platform for B2B. Average CPLs run $50 to $150 depending on industry and targeting. Some enterprise segments hit $300+. A 21% cut on a $150 CPL saves $31.50 per lead. On a campaign spending $10,000 a month, that's roughly $2,100 back in your pocket. The catch: Predictive Audiences need enough conversion data to learn from. If your account runs fewer than 30 conversions per month, the model won't have enough signal. You'll get better results with manual targeting until you hit that threshold. Accelerate Campaigns: Full Campaign Setup in Minutes LinkedIn's Accelerate feature builds a campaign from a landing page. You paste the URL, and it drafts your ICP, sets audience filters, and generates starting creative. I tried it on a Sucana landing page. It pulled the value proposition correctly, set reasonable audience parameters, and generated three ad variations. The whole process took about four minutes. Two of the three ad variations were usable with light editing. The audience targeting was close but too broad on company size. I narrowed it and launched. The time savings are real. A campaign that normally takes 45 minutes to build was ready in under 10. Where Accelerate falls short: it can't read your existing campaign history. It doesn't know what worked before. So it starts from zero every time, even if you have six months of data that could inform every setting. Use it for new campaigns in unfamiliar verticals. Skip it when you already know what works. AI Ad Variants: One Click, Multiple Versions LinkedIn now generates ad variations from a single headline or intro text. Click once, get multiple ready-to-use versions. This sounds small. It's not. Creative fatigue is the silent killer on LinkedIn. B2B audiences are small. The same 50,000 decision-makers see your ad over and over. When frequency climbs past 3, engagement drops and CPL spikes. The diagnostic process is similar to troubleshooting a Facebook Ads CPL spike , but with tighter audience windows. AI ad variants let you rotate fresh creative without writing every version yourself. I've seen accounts push frequency tolerance from 3 to 5 just by keeping variations in rotation. LinkedIn also previewed "flexible ad creation" where you upload four images, four videos, and four copy variations. The system mixes and matches to optimize delivery. Think of it as LinkedIn's version of responsive search ads on Google. Not live everywhere yet, but when it rolls out, this changes how you think about creative production for LinkedIn. Enhanced Personalization: Ads That Know Who's Reading LinkedIn can now tailor ad copy to individual members automatically. It pulls the viewer's name, job title, industry, and company name directly into your ad. "Hey [Name], still managing [Company]'s ad reporting in spreadsheets?" That kind of personalization used to require dynamic creative platforms that cost thousands per month. LinkedIn baked it into Campaign Manager. The results depend on your angle. Personalization works when the message is specific enough that the name insertion feels natural. It falls flat when the copy is generic. Then the name just makes it feel like a mass email with a mail merge. Write the copy first. Make it sharp. Then add the personalization layer. Not the other way around. The AI Tool Nobody Talks About: Your Own Campaign Data This is where most LinkedIn Ads articles stop. They list the features, rank them, and move on. But the most powerful AI application for LinkedIn Ads isn't a LinkedIn feature at all. It's what happens when you point AI at your own campaign data. Victor pulled up his LinkedIn campaign data last month. He asked Claude to analyze 90 days of spend across six campaigns. The response broke his campaigns into three phases: early learning, aggressive scale with CPL climbing, and budget pullback to find the natural floor. He didn't describe his strategy. The AI read it from the numbers. "It understood my logic," Victor said. "Like I launch it, I scale, CPL rises, I pull back." The AI also flagged that 68% of budget was going to one geography while a different region was delivering double the results on 13% of the spend. That reallocation insight alone justified the entire analysis. No LinkedIn feature surfaces that. No third-party tool showed it. Raw data plus a capable AI model found it in seconds. That's what we're building at Sucana: AI that reads your actual campaign data and finds what you'd miss manually. Not another dashboard. A conversation with your numbers. I wrote about how Claude reads campaign data if you want to see what that analysis looks like. What Moves the Needle After testing everything LinkedIn offers and several third-party approaches, here's what I'd spend time and money on. Use Predictive Audiences when you have the data. If your account generates 30+ conversions per month, switch your best campaign to Predictive Audiences immediately. The 21% CPL reduction is real and compounds over time. Use Accelerate for new campaign builds. Skip the 45-minute setup process. Let LinkedIn draft the campaign, then edit the targeting and creative. You save time without sacrificing control. Rotate AI ad variants weekly. Creative fatigue kills B2B campaigns quietly. Generate fresh variants every week. Keep frequency under 5. The effort is minimal but the CPL impact is significant. Point AI at your data every week. Export your LinkedIn campaign data. Feed it to Claude or ChatGPT. Ask specific questions: "Which campaign has the highest CPL trend week over week?" Or: "Which audience segment converts cheapest but gets the least budget?" The answers are sitting in your data right now. What's Not Worth Your Money I'll be direct. AI tools that "optimize" LinkedIn Ads without showing their logic. If you can't see what changed, you can't fix it when it breaks. Black-box optimization on a $150 CPL platform is a fast way to burn cash. Generic AI copy generators for LinkedIn. LinkedIn's audience is allergic to generic marketing copy. Decision-makers scroll past anything that reads like a template. Use AI to draft, then rewrite in your own voice. The AI gives you structure. You give it credibility. Any tool that claims to "hack" LinkedIn's algorithm. LinkedIn's algorithm isn't broken. The CPLs are high because you're reaching verified professionals with real job titles at real companies. That precision costs money. Tools that promise to lower costs through tricks are selling hope. The Real Shift Happening in B2B Advertising LinkedIn integrated real-time CRM data into Campaign Manager in June 2025. That's the quiet move that changes everything. Before this, you ran LinkedIn campaigns and hoped the leads converted downstream. Now you can see pipeline and revenue data directly inside the ad platform. That means AI can optimize toward revenue, not just clicks or leads. A lead that costs $200 but closes a $50,000 deal is better than ten leads at $50 that never respond to a sales email. I covered how to prove that kind of marketing ROI to clients using outcome data instead of vanity metrics. The agencies that figure this out first will win the next two years. The ones still optimizing for CPL without connecting it to revenue are already behind. Victor sees it in his client meetings. "I could have such confidence in the data," he told me after testing the integration. "I gave a much better image to the client." That confidence is the product. Not cheaper clicks. Better decisions. How to Start Using AI for LinkedIn Ads This Week Don't overhaul everything. Start with one campaign. Pick your highest-spend LinkedIn campaign. Export the last 30 days of data. Open Claude or ChatGPT. Paste the data and ask: "What patterns do you see? Where am I spending the most per lead, and which audiences convert cheapest?" Read what comes back. Check it against what you already know. If the AI catches something you missed, you have your answer about whether AI is worth it for LinkedIn. Then try Predictive Audiences on that same campaign if you have enough conversion data. Compare the results after two weeks. Two steps. One campaign. Real data. Start there. If you want something purpose-built for this, that's what Sucana does. But even a free AI chat with your exported campaign data beats guessing. The fancy integrations can come later. Frequently Asked Questions What AI features does LinkedIn offer for ads in 2026? LinkedIn Campaign Manager now includes Predictive Audiences, Accelerate campaign builder, AI ad variants, enhanced personalization, and flexible ad creation. Predictive Audiences use machine learning to find members likely to convert. Accelerate builds full campaigns from a landing page URL in minutes. The CRM integration launched in June 2025 connects pipeline data directly to campaign performance. This lets you optimize toward revenue instead of just lead volume. Do Predictive Audiences lower CPL on LinkedIn? Early benchmarks show a 21% lower CPL compared to standard professional targeting. The model needs at least 30 monthly conversions to learn effectively. Accounts with fewer conversions should stick with manual targeting until they hit that threshold. Results vary by industry and audience size. B2B SaaS accounts with established conversion tracking tend to see the strongest improvements. Can AI write LinkedIn ad copy? AI can write solid first drafts. I use Claude with detailed prompts about the product, target role, company size, and specific pain points. Three out of five variations are usually usable with editing. The key is context. Feed the AI real customer objections, specific job titles you're targeting, and actual product details. A generic prompt produces copy that LinkedIn's audience will scroll past without reading. I wrote a full guide on AI prompt engineering for PPC that covers this in depth. How does LinkedIn Accelerate work? Paste your landing page URL into Accelerate. It reads the page, drafts your ICP, sets audience filters (job title, industry, company size), and generates starting ad creative. The whole process takes about four minutes. The output needs editing. Audience targeting is usually too broad and creative needs brand voice adjustments. But it cuts campaign setup time from 45 minutes to under 10. What's the average CPL for LinkedIn Ads in 2026? LinkedIn CPLs typically range from $50 to $150 for B2B campaigns. Enterprise targeting with tight filters can push past $300. These numbers vary by industry, geography, and audience size. The platform is expensive because it reaches verified professionals with accurate job titles and company data. That precision costs more than broad social targeting on Meta or TikTok. How do I stop wasting budget on LinkedIn Ads? Three things kill LinkedIn budgets silently. Creative fatigue from small audiences. Weekend spend when B2B decision-makers aren't active. And audience targeting that's too narrow (under 50,000 members). Turn off Audience Expansion . It muddies targeting and spreads spend to low-quality impressions. Schedule campaigns for weekdays only when possible. Rotate ad creative weekly to keep frequency under 5. Can AI replace a LinkedIn Ads manager? Not today. AI handles the repetitive work: generating ad variants, analyzing performance data, building initial campaign setups, and flagging budget allocation problems. Those tasks eat about 60% of a media buyer's week. We covered how to build AI workflows for your marketing team if you want to see what that looks like in practice. The remaining 40% is strategy, client communication, and creative direction. AI can't sit in a pipeline review meeting and explain why CPL spiked last Tuesday. It can tell you why, but someone still has to deliver that insight to the client. What's the difference between LinkedIn's AI and third-party AI tools? LinkedIn's AI features live inside Campaign Manager. They control targeting (Predictive Audiences), campaign setup (Accelerate), and creative optimization (ad variants). These tools use LinkedIn's proprietary data on member behavior. Third-party tools sit on top of your campaign data. They analyze patterns LinkedIn doesn't surface, generate creative assets, and automate reporting. Think of LinkedIn's AI as the engine and external tools as the diagnostic system. How often should I refresh LinkedIn ad creative? For B2B audiences under 100,000 members, refresh creative every 7 to 10 days. Frequency climbs fast with small audiences, and creative fatigue spikes CPL before most advertisers notice. Use LinkedIn's AI ad variants to generate new versions quickly. Upload the new creative, pause the old ads, and monitor CPL for the first 48 hours to catch any learning phase dips. Is LinkedIn Ads worth the high CPL for B2B? It depends on what happens after the lead comes in. A $150 LinkedIn lead that closes a $50,000 deal has a 333x return. A $30 Meta lead that never responds to a follow-up email costs more in wasted sales time. Connect your CRM data to LinkedIn Campaign Manager. Measure cost per qualified opportunity, not just cost per lead. LinkedIn's high CPL is worth it when the leads convert to revenue. --- ## How to Measure AI Marketing ROI (With Real Client Data) URL: https://sucana.ai/blog/prove-marketing-roi-ai Published: 2026-03-11 Author: Virgil Brewster Tags: ai, agencies, automation, analytics How Do You Prove Marketing ROI to Clients When They Ask About AI? We prove marketing ROI by showing before and after numbers the client can verify. I pull the exact metrics from their campaigns, compare the period before AI to the period after, and let the data answer the question. When clients see CPL drop and lead quality hold, the AI conversation stops. The answer is not to explain AI. It is to show results they have never seen before. When a client asks about AI, they are not asking about the tool. They are asking: am I still getting what I pay for? Answer that question with numbers they cannot argue with, and the AI conversation disappears. Victor's Friend Went Silent Victor was walking a client through a campaign report on a live Zoom. The kind of report Sucana builds: spend breakdown, CPL by channel, geographic efficiency, a written narrative of exactly what happened in the campaign and why. His friend joined mid-call, watching Victor walk through the kind of report where AI reads campaign data like a senior media buyer . He had not asked to join. He just saw the screen. He stopped talking. Then: "Like this is so good. This is the dream for Media Buyer." Victor turned to camera: "99% of the agencies cannot give this kind of report right now." Not a pitch. A reaction. Someone who buys media for a living saw the report and named what it was. Most agencies try to justify AI by explaining how it works. Victor showed what it produced. The client did not ask about AI. They asked when the next one was coming. Step 1: Stop Explaining AI and Start Showing Results The first mistake agencies make when clients ask about AI is defending it. Do not say "we use AI to process your data." Say "here is what we found in your campaign this week that we would have missed otherwise." When I hear an agency owner say "my client is worried about AI replacing the work," I know exactly what happened. They told the client about the tool before the client saw the result. Show the result first. Explain the tool never. Step 2: Replace the Static Report with a Data Narrative A Looker Studio dashboard shows numbers. A good report, like automating Google Ads reporting with AI , tells you what the numbers mean. Victor said it clearly: "It's not only the graphics. It's also understanding the graphics and giving explanations about what happened." Looker shows the numbers. AI explains them. A static dashboard takes four hours to turn into insight. An AI-generated report does it in minutes. That gap is where the proof lives. Build a report that includes what happened, why it happened, and what to do next. Three sections. Every time. I wrote the full system for automating client reporting with AI if you want to set this up end-to-end. That structure is more useful than any dashboard your client has ever seen. Step 3: Put Specific Numbers in the Report, Not Vague Language "Performance improved" means nothing. "CPL dropped from 47 euros to 31 euros in 21 days" means everything. Specific numbers get cited at 40% higher rates by both humans and AI systems. They are also harder to argue with. Go through your last three client reports. If you see phrases like "strong performance" or "trending positive," replace every one with the actual number. The specific version of the same sentence is always more convincing. Step 4: Show Time-to-Insight, Not Just Results One of the strongest ROI arguments is speed. Not how good the result was, but how fast you saw it and acted on it. I track this in a simple column: how many days between a campaign problem appearing and us catching it. Before AI analysis, that gap was sometimes two weeks. We caught the problem when the client noticed the numbers. Now we catch it before the client sees it. Show your client that number. "We identified this issue on day 3. Your previous agency caught it on day 18." That is a 15-day difference. That is real money. Speed of insight is a measurable metric. Most agencies never track it. The ones who do have a story nobody else can tell. Step 5: Let the Client See the Raw Analysis This one takes nerve the first time. Share the AI analysis directly with the client, not just your summary of it. Let them see the reasoning. Let them see the questions it asked and the answers it found. Victor did this with Pablo. He did not clean up the report or rewrite it in his own words. He sent the AI narrative directly, with his own notes added on top. The client's reaction: "this is the most thorough report we have ever received." Transparency is the ROI argument. When the client can see the process, trust goes up, not down. Step 6: Track and Report the Metrics That Matter in 2026 Old metrics: impressions, clicks, rankings. These are what Google Analytics and basic dashboards still default to. New metrics: CPL by channel, revenue per campaign euro, cost per qualified lead, time from problem to detection. The agencies losing client confidence are the ones still reporting the old metrics, which is part of why agencies are rethinking SEO for AI-powered search . Those numbers look the same whether you used AI or not. New metrics are where the AI advantage shows up. Build a one-page metrics sheet with these five numbers. Update it monthly. The trend over three months tells a story that a single report cannot. Step 7: Make One Specific Recommendation Per Report The weakest part of most agency reports is the next steps section. It either does not exist or it says something generic like "continue to improve targeting." Every report needs one specific recommendation. Not three. One. "Pause Spain targeting. Spain is generating 38% of your spend and 12% of your leads. Redirect that budget to France, which is generating 41% of leads at 22% of spend." That sentence is worth more than 10 pages of charts. It shows the client that you understand their campaign, you made a judgment call, and you know what to do next. That is the job. AI helps you find it faster. The recommendation proves you are still doing the job. Once you can do this consistently, pitching AI services to clients becomes straightforward. Step 8: Answer the AI Question Before They Ask It Most clients who question AI have one real fear: that you are doing less work for the same fee. Address it in the report, before they bring it up. "This analysis took 40 minutes of focused review instead of the usual 4 hours of manual pulling. We used that time to dig deeper into the geographic data, which is where we found the Spain issue. That finding alone should save roughly 1,200 euros this month." That one paragraph does three things. It acknowledges the AI. It shows what you did with the time. It attaches a number to the outcome. Most agencies wait for the client to ask. The better move is to make the answer part of the report every month. Step 9: Track the Wins Over 90 Days Single-report ROI is easy to dismiss. A 90-day pattern is not. Keep a log in the client file. Every specific finding that came from the analysis. Every change made based on it. Every euro saved or gained. At 90 days, summarize it on one page. "In the last 90 days, AI analysis identified 4 issues that led to 3 budget reallocations. Combined impact: CPL down 22%, spend efficiency up 18%." That summary is the retention conversation. It is not about AI. It is about what you found and what you did about it. Step 10: Let the Report Be the Pitch If a client is questioning the value of what you do, the fastest fix is to show them a report they have never seen before. Not a proposal. Not a case study. An actual live report of their own campaigns, built the way Victor built Pablo's. Complete narrative, specific numbers, one clear recommendation. When a client sees their own data explained that clearly, the question about AI goes away. The question becomes: when can I get the next one? That is the proof. The report proves the ROI. Frequently Asked Questions How do you justify agency fees when clients think AI does the work for free? The best answer is not a defense. It is a demonstration. Show a report that AI alone cannot produce: campaign insight, human judgment, specific recommendations. When clients see that level of analysis, the question stops being about your fee. It starts being about what you found and what happens next. What metrics prove marketing ROI when clients ask about AI tools? The metrics that matter in 2026 are specific and outcome-based. CPL by channel. Revenue per euro spent. Time from problem identification to action. Impressions and click data do not prove value. Outcome data does. Build your reports around the numbers that show what changed, not just what happened. How long does it take to see ROI from AI marketing tools? Clients who see their first AI-generated report usually have a reaction in the first session. The Pablo example is typical: first report, immediate reaction. Building a 90-day track record of findings and outcomes is what locks in the retention conversation. A single report proves the capability. Three months of patterns proves the value. What do I do when a client directly asks if AI is writing my reports? Tell the truth. "Yes, we use AI to analyze the data. I use my experience to interpret it and make the call." "Here is what I found this week." That is the whole answer. That response is stronger than deflecting. Clients respect honesty. They lose trust in evasion. The data finding is still yours. The recommendation is still yours. Own both. How do I prove marketing ROI without a big analytics budget? You do not need a big budget. You need specific numbers and a consistent format. One page, five metrics, one recommendation. That structure costs nothing and outperforms most reporting tools. Victor's first AI report for Pablo was built in Claude . The client reaction was the same as if it cost ten thousand euros to produce. What should every client report include to prove AI value? Three things: what happened in the data, why it happened based on the analysis, and one specific recommendation with a number attached. Reports that include all three are almost impossible to dismiss. Reports that only show dashboards are easy to ignore. The narrative plus the recommendation is what makes the report worth paying for. How do clients react when they see AI-generated marketing reports for the first time? Most clients have not seen analysis at this level before. Victor's friend called it "the dream." Pablo asked for the next one immediately. The reaction is rarely about AI. It is about the depth of the insight. Most clients have only ever received dashboard screenshots and bullet summaries. A full narrative with specific findings is a different category of work. Is there a risk that showing AI analysis will make clients think the work is easy? Only if you let the report speak for itself without context. Add a short note to every report: what you looked for, what you found, and what judgment call you made. That note makes the human decision visible. Clients do not pay for the analysis. They pay for the judgment. Make sure the judgment shows up every time. How do I track ROI improvements over time to show clients? Keep a simple log in the client folder. Date, finding, action taken, outcome. One line per month. At 90 days, total the findings and attach a number to the impact. Even rough estimates work: "This reallocation reduced wasted spend by roughly 1,400 euros based on the CPL shift." Ninety days of logged wins is a retention conversation without a sales pitch. What is the difference between proving AI ROI and proving marketing ROI? There is no difference. That is the point. Clients do not care which tool you used. They care what you found and what it cost or saved them. The AI is invisible when the outcome is specific. Stop proving the tool. Prove the result. --- ## AI Marketing Automation Tools: What Actually Works in 2026 URL: https://sucana.ai/blog/ai-marketing-automation-tools Published: 2026-03-08 Author: Virgil Brewster Tags: ai, automation, agencies What AI Marketing Automation Tools Actually Work in 2026? The AI marketing automation tools that work in 2026 solve specific repeatable tasks. We tested dozens at Sucana and the winners handle lead scoring, email sequencing, and ad optimization without constant oversight. Vinod and I track which tools save real hours per week, not which ones demo well. The ones that automate tasks you already do manually every week. Not the ones with the longest feature list. I tested dozens of AI marketing tools over the past year. Most got dropped within a week. The ones that stuck replaced a specific, boring task I was already doing by hand. Victor told me something a few weeks ago that made it click. We were talking about which subscriptions to cancel. I mentioned ChatGPT. Victor paused. "I didn't log into ChatGPT for a couple of months at least." I broke down the full Claude vs ChatGPT comparison for marketing in a separate article. No big announcement. Zero review. He just stopped going back. That is how real tool decisions happen. Not from a features page. From a quiet habit that changes. Why Most AI Marketing Tools Get Abandoned 72% of marketing teams report wasted money on AI tools . I have seen it happen in real time. The pattern is always the same. A tool promises to automate "everything." You sign up and spend two days setting it up. The output needs so much editing that doing it manually would have been faster. The problem is not the AI. The problem is that most tools automate the wrong thing. They automate what looks impressive in a demo, not what eats your Tuesday morning. The Test I Use Before Buying Any AI Tool I run every AI tool through one filter before paying. Three questions. Does this task happen at least once a week? If it is a one-off, I do not need a tool. I need ten minutes with Claude. Is the input structured? AI needs patterns. If the task changes every time, AI gives garbage output every time. Can I describe the output in one sentence? "A weekly client report with spend, leads, and cost per lead." That is clear enough for AI. "Make my marketing better" is not. If a tool passes all three, I try it. If it fails any one, I skip it. The Tools I Actually Use Every Day I am not going to list 30 tools. Every article on Google already does that. I am going to tell you what I open every morning and why it stayed. Claude (Anthropic) This is the center of everything I do. Not ChatGPT. Definitely not Gemini. I use Claude Code with custom instruction files. They contain my tone of voice, my processes, and my business context. Every post goes through three voice analysis checks before I see it. Claude pulls Fireflies transcripts, updates roadmaps, scrapes industry news, and generates content. The difference between Claude and a generic chatbot is massive. Claude knows my business because I taught it my business. n8n (workflow automation) n8n connects everything. When a Fireflies transcript drops, n8n triggers Claude to pull action items. A Reddit post matches our keywords, and n8n sends it to Slack. A lead fills out a form, and n8n qualifies them. I picked n8n over Zapier because I can self-host it. The pricing does not punish you for running lots of automations. Fireflies.ai (meeting intelligence) Every call I have gets transcribed and searchable. I do not take notes anymore. I search my own conversations for quotes and numbers. This sounds small but changes everything. Before Fireflies, I lost half of what people said in meetings. Now I search "Victor said" and find the exact quote from three weeks ago. The Tools I Dropped (and Why) This is the part nobody writes about. The tools that looked great in the demo but got dropped within a month. I dropped three AI content generators that promised to write in my voice. They all produced generic output with complex words nobody uses. I spent more time editing than writing from scratch. I dropped two AI analytics dashboards that took forever to set up. One of them needed a developer just to connect data sources. That defeats the entire purpose. I dropped a social media scheduler with AI "suggestions" on every post. The suggestions were generic best practices from a textbook. Not from my actual audience data. The common pattern: tools that try to do everything end up doing nothing well. How to Build Your Own AI Marketing Stack Do not start by shopping for tools. Start by tracking your time for one week. Write down every task that takes more than 30 minutes and happens more than once. That list is your shopping list. I did this exercise at Sucana. Three tasks were eating 12 hours per week: meeting notes, content production, and data reporting. I wrote a full breakdown of where to start with AI automation for agencies if you want the step-by-step process. Those became my first three automations. Not because AI is cool. Because those 12 hours were killing me. I wrote a separate guide on automating client reporting with AI if reporting is your biggest time sink. Start with one tool, not five. Pick the biggest time waster first. Find one tool that fixes it. Get it working before you add anything else. Stacking five tools at once is how you end up with five half-configured tools and zero working automations. I wrote a step-by-step guide on building AI workflows for your marketing team that covers this approach. Connect tools only when both work on their own. I see agencies buy n8n on day one. They try to connect tools that do not work yet. Make sure each tool produces good output alone first. Then connect them. Budget for the real cost, not the subscription price. The subscription is $30 a month. The setup time is 8 hours. The learning curve is 2 weeks. The real cost of an AI tool is time, not money. Factor that in before you commit. Knowing which tools to learn first matters, and I covered the AI skills every marketer needs in a separate post. What Separates Tools That Stick From Tools That Get Dropped After a year of testing, I see three patterns in tools that survive past the first month. They solve one problem clearly. Claude does AI conversations with context. n8n does workflow connections. Fireflies does meeting transcription. None of them tries to be everything. They produce output I can use without heavy editing. If I rewrite 80% of what the tool gives me, it is not saving time. It is adding a step. They get better as I use them more. Claude improves because I add more context files. n8n gets faster because I refine my workflows. Tools that stay flat no matter how much I invest get dropped. The Real Cost of Chasing Every New AI Tool I wasted three months in early 2025 trying every AI marketing tool that launched. A different tool every week. A fresh setup every weekend. Another disappointment every Monday. The cost was not just the subscriptions. It was the context switching. Every time I moved to a new tool, I lost the context I had built. My prompts, my workflows, my configurations. All gone. Now I have a rule. I only evaluate a new tool when an existing workflow breaks. If everything is running, I do not touch it. Boring is good. Frequently Asked Questions What AI marketing automation tools actually work in 2026? The tools that work automate specific, repeatable weekly tasks. Claude for content and analysis, n8n for workflows, Fireflies for meeting intelligence. Generic "do everything" platforms tend to underdeliver. Pick tools that solve one problem well. Are AI marketing tools worth the money or overhyped? Some are worth every penny. Others are pure hype. The difference comes down to whether the tool replaces a task you do manually every week. If you cannot name the exact task, it is probably overhyped. How do I automate marketing workflows with AI? Track your time for one week. Find the tasks that take more than 30 minutes and happen more than once. Pick the biggest time waster first. Find one AI tool that handles it. Get it working before adding more tools. What is the difference between traditional marketing automation and AI automation? Traditional marketing automation follows rules you set. If X happens, do Y. AI automation interprets context and makes decisions. Ask it to summarize a call, and it pulls out the important parts. You do not need to define what "important" means. That is the practical difference. Which AI tools replace manual marketing tasks? Claude replaces content drafting, data analysis, and research. n8n replaces data transfers between platforms. Fireflies replaces note-taking. Together they cut about 12 hours per week from my workload. No single tool replaces everything. How much do AI marketing automation tools cost? Claude Pro costs $20 per month. n8n self-hosted is free. Fireflies starts at $10 per month. My entire AI stack runs under $100 per month. The real cost is setup time, not subscription fees. Budget 8 to 16 hours per tool. Can AI replace a marketing team? AI replaces tasks, not people. I still make every strategic decision. I still approve every piece of content. What AI does is free up the 12 hours per week I used to spend on repetitive work. That time now goes into strategy and building Sucana. What marketing tasks should you automate with AI first? Reporting, meeting notes, and content drafts. These three are repeatable, structured, and time-consuming. They also have the lowest risk if AI makes a mistake. Do not start with creative strategy or client communication. Those need human judgment that AI cannot match yet. How do I integrate AI tools with my existing marketing stack? Use n8n or Make.com as the connection layer. These tools bridge your AI tools and your existing platforms. Start with one connection. Test it for a week. Add the next connection only after the first one runs clean. What are the biggest mistakes teams make with AI marketing automation? Buying too many tools at once. Automating tasks that do not need it. Expecting perfect output without any editing. The biggest mistake is treating AI tools as magic. They need setup, context, and maintenance, just like every other tool. I wrote about building an AI-powered marketing agency that covers how to avoid these mistakes at scale. --- ## AI LinkedIn Post Generator: How I Built One in 7 Steps URL: https://sucana.ai/blog/ai-linkedin-writer-team Published: 2026-03-06 Author: Virgil Brewster Tags: ai, automation, agencies How Do You Automate LinkedIn Content for a Team? I built a skill file that holds our voice rules, writing frameworks, and real story sources in one place. The AI reads that file and writes first drafts for each team member. I review and approve every post before it goes out. We went from posting twice a week between three people to publishing 12 posts every single week. Every Monday at Sucana, the same thing happened. I would stare at a blank LinkedIn editor. Victor my co-founder would stare at his. Vinod my co-founder would stare at his. Three people who know exactly what they want to say. None of them writing it down. The problem was never ideas. We talk about marketing, PPC, and building software all day long. The problem was starting. Opening a blank page, picking a topic, finding the right tone. By the time you get through all of that, the day is gone and you posted nothing. I tried ChatGPT first. Typed "write me a LinkedIn post about building a SaaS" and got back a press release written by a robot. Victor tried it too. His posts came back full of PPC jargon that even I couldn't follow. The AI wasn't the problem. The instructions were. We were giving it nothing to work with. So I stopped prompting and started building a system. Here are the seven steps I followed, in the exact order I did them. Step 1: Define the Outcome Before touching any AI tool, I wrote down one sentence. "I type one command, and 30 minutes later all three people have their posts in Slack and Basecamp ." That became my north star for every decision during the build. I also defined what each post should look like. Every post is 150 to 250 words. Every hook is one sentence, max eight words. Every post is first-person and uses real stories. No press release tone. No invented stories. Write your outcome before you do anything else. Step 2: Break It Into Small Steps My one-sentence outcome sounds simple. But there are a lot of moving parts hiding inside it. The trick is breaking it into steps so small that each one is impossible to mess up. Here is how I broke mine down: Read the content pool and pick four topics per person Write all four posts using voice rules and frameworks Show me the posts for review Save approved posts as files Send each person their four posts on Slack Create a to-do in Basecamp for each person Six steps. Each one does one thing. I wrote each step as a plain English sentence. You can do this on a napkin. Step 3: Go to Claude Code and Plan Your Build Now take your outcome and your steps to Claude Code. Not to build yet. To plan. I opened Claude Code and had a conversation. Not "build me a skill." More like, "here is what I want to happen every Monday. Here are my six steps. What's the best way to structure this?" Claude Code asked questions I hadn't thought about. How should the posts be structured? What makes a good hook? Where does the content pool live? That conversation took about 30 minutes. By the end, I had a clear build plan before writing any skill file. Claude Code also told me what it needed from me: a voice file and example posts. Step 4: Create Your Voice File The planning step showed me what the skill needed. So before building, I created the inputs. I opened a blank file and started dumping everything about how I sound. Not how I want to sound. How I actually talk. Tone calibration: "two tequilas on an Ibiza beach party explaining how cool Sucana is." That one sentence tells the AI more than a page of generic instructions. Banned words: "leverage," "synergy," "unlock," "empower." Sentence rules: max 20 words, if a 7-year-old can't read it, it's garbage. Phrases that are mine: "One last dance." "Let's fucking go." "You cannot out-think a market." These are things I actually say. The AI picks them up and weaves them in. My voice file ended up at 596 lines. It took about two hours. The more specific your voice file, the better the posts sound. Step 5: Collect 3 to 6 Example Posts The voice file tells the AI how you talk. The examples show it what good looks like. I grabbed six LinkedIn posts I had written manually that got solid engagement. Posts where people DMed me and commented real replies. I also grabbed a few from people whose style I admired. Not to copy, but to show the AI the energy and structure I was going for. Save these somewhere the AI can read them. This step takes 20 minutes. Scroll through your LinkedIn, pick your best ones, save them. Step 6: Build and Test Each Step Now you build. I went step by step with Claude Code's skill builder. "Let's build step 1 first. The AI should read my content ideas file and pick four topics for Virgil." We built it. Tested it. Moved to step 2. I embedded my voice rules directly into the skill file. Earlier versions referenced external files, and the AI would skip reading them. Putting everything in one file fixed that. If you want to go deeper on this approach, I wrote a full guide on building AI workflows for your marketing team . I ran the full skill three times before I trusted it. First run: Victor's posts were too technical. I added a rule that his posts must be understandable to someone outside PPC. Second run: the Basecamp delivery failed. The skill had placeholder variables instead of real API credentials. I hardcoded the real values. Third run: everything worked. Twelve posts written. Three Slack messages sent. Three Basecamp to-dos created. Step 7: Roll It Out to Your Team Once the skill works, rolling it out is the easy part. If you want to document your process for the whole team, I covered creating AI SOPs for your agency separately. I run it every Monday. It takes 30 minutes to review all 12 posts. Each person gets a Slack DM with their four posts. Copy, paste, post. That is literally it. The same voice-file-plus-Claude pattern runs my LinkedIn DM workflow that saves two hours a day — different output, same plumbing. Before the skill, we posted maybe twice a week between the three of us. Now we publish 12 posts per week. Every week. The posts are better too. Not because the AI is a better writer. Because it starts from real stories with a clear structure. We edit from 80% done instead of building from zero. Victor told me his posts got more engagement after we started. Not because the AI wrote something magical. Because he actually posted consistently for the first time. Consistency beats brilliance. What the Delivery Layer Taught Me Writing the posts is only half of it. Getting them in front of the right person at the right time is the other half. We tried saving posts to a shared folder. Nobody opened the folder. We tried emailing them. Nobody read the emails. Slack DMs worked. A direct message with four posts, ready to copy and paste. They see it, they grab it, they post it. The Basecamp to-do adds accountability. It shows up in the weekly task list with a due date. If you haven't posted by Friday, the to-do is still staring at you. Systems only work when the output lands where people already look. The same principle applies when you pitch AI services to clients : meet them where they are, not where you wish they were. One Thing I Would Do Differently I would write the voice file first. Before anything else. I did it in step 4 because the planning conversation told me I needed it. But if I started over, I would record myself talking for 30 minutes and transcribe it. Clean that up before even opening Claude Code. The voice file is the single biggest factor in output quality. My first version was two paragraphs. The posts were generic. The 596-line version produces posts I barely need to edit. Start there. Frequently Asked Questions How long does it take to build a LinkedIn content skill from scratch? The initial build took about six hours over a few days. Two hours on the voice file, one hour collecting examples, 30 minutes planning, and the rest on building and testing. After that, the weekly run takes 30 minutes for all 12 posts. Does the AI write the posts or do I? The AI writes the first draft based on real stories, your voice rules, and specific frameworks. You review, edit, and approve. Think of it as starting from 80% done instead of a blank page. The human always makes the final call. Can this work with ChatGPT instead of Claude Code? The preparation steps are the same: voice file, examples, frameworks, real content sources. ChatGPT can handle the writing part. The delivery automation needs a tool that can execute commands. That is where Claude Code or a workflow tool like Make comes in. I compared AI marketing automation tools that actually work if you want a full breakdown. What if the posts don't sound like me? Your voice file needs more detail. My first version was two paragraphs and the output was generic. The current version is 596 lines. It covers everything from sentence length to banned words to how I describe problems. More specificity means better output. How do I handle different team members with different expertise? Define each person's domain in the skill. I handle marketing. Victor covers PPC. Vinod writes about software development. The voice rules apply to everyone, but the topics and framing shift based on who the post is for. What writing frameworks work best for LinkedIn posts? We use three. How-to posts framed as "How I did this" in three steps. Personal stories as raw narratives. And industry takes with an opinion on trends. Every post has one idea, one hook, and ends with a question. How do I make sure the AI doesn't make up stories? Hard rule in the skill file: never invent. The AI pulls from two sources, a content ideas file and a notes folder. If no source exists for a topic, it skips that topic. When referencing someone else's work, it frames it as "I read this." Never pretending something happened to you. What was the biggest mistake during the build? Leaving placeholder variables in the skill file instead of real values. The Basecamp integration had generic placeholders where the account ID and API token should be. The AI got confused and tried to open a browser instead of using the API. Once I hardcoded real credentials, it worked instantly. How many posts per week should each person write? We settled on four: two how-to posts, one story, one industry take. That covers Monday through Thursday. Consistency matters more than volume. Pick a number your team can sustain week after week. Can I use this approach for content besides LinkedIn? The same steps work for any repeated content task. Email newsletters, Twitter threads, blog outlines, client updates. The ingredients are identical: a voice file, a structure, examples, and real content. We started with LinkedIn because it was our worst blank-page problem. --- ## Claude AI for Marketing: How to Read Campaign Data Like a Pro URL: https://sucana.ai/blog/how-claude-reads-campaign-data Published: 2026-03-04 Author: Virgil Brewster Tags: ai, media-buying, analytics, agencies What Does a Media Buyer Check Every Day? A media buyer checks three metrics per client every morning: spend, leads, and cost per lead. Victor my co-founder runs this exact check across all his accounts. If those three numbers look stable compared to yesterday, he closes the tab and moves on. The whole review takes under two minutes per client. If those look normal, he moves to the next client. No deep dive needed. This is Victor's daily process. I watched him do it for months before we started building Sucana. It never changed. Three primary metrics first. Three secondary metrics only if something breaks. Most guides list 15 metrics and tell you to track all of them. Victor runs ads for clients spending thousands per day. He checks three. The Story That Made Me Understand Victor's Process Victor pulled up his campaign data in Sucana a few weeks ago. Our AI analyzed it automatically. It described Phase 1 as early momentum, the algorithm still learning. Phase 2 as aggressive scale with CPL climbing. Phase 3 as budget pullback and natural floor. Victor said nothing about his strategy. He just watched. Then: "It understood my logic. Like I launch it, I scale, CPL rises, I pull back." The AI read the numbers and described his decisions back to him. That is Victor's process in three phases. Launch, watch the metrics, react to what the data says. He has done this hundreds of times. The logic never changes. The 3 Primary Metrics (Every Client, Every Day) Victor checks these first. Every morning. Every client. Spend: How much went out the door today Leads (or purchases): How many results came back CPL (or cost per purchase): What each result cost Victor's exact words: "Spent first, leads second, CPL third. That's the basic." If all three look stable compared to yesterday, he closes that client's tab and moves on. The whole check takes under two minutes per client. The 3 Secondary Metrics (Only When CPL Spikes) When cost per lead jumps, Victor does not guess. He runs a diagnostic chain. Three more metrics, in this order: CPM: How much it costs to reach 1,000 people CTR: What percentage of people click the ad CPC: What each click costs He does not pull CTR from Facebook's Marketing API separately. If he has impressions and clicks, he calculates CTR himself. Keeps the data pull lighter. These three metrics exist for one reason: to find where the problem lives when CPL spikes. Victor's Diagnostic Chain This is the part I have never seen written anywhere else. Victor does not just look at secondary metrics. He reads them as a chain. Each combination points to a different problem. CPM same or cheaper + CTR same or better + CPC same or better + CPL went up: The problem is on the landing page. The ad is working fine. People are clicking. They are just not converting once they land. Time to check the page, not the ad. CPM went up: The algorithm is charging more to reach people. Could be audience fatigue, more competition entering the auction, or a platform-wide shift. CTR dropped: The creative is tired. People stopped clicking. Time to refresh the ads, not adjust the budget. CPC went up but CTR is fine: CPM increased. The click costs more because reaching people costs more. Nothing wrong with the ad itself. Victor: "I want to see if also the CPM increased and also the CTR because that will make the cost per click. If I see the CPM is equal or cheaper, the CTR is equal or better, and the cost per lead increased, then I know the problem is in the conversion page." That one quote is worth more than any media buying course I have seen. It is a decision tree in four sentences. The Funnel Dissection Victor always works bottom to top. Start with the final result. Trace backward to find the first hole. This diagnostic approach is one piece of how we use AI for performance marketing across every platform. For a sales funnel with a calendar booking flow: How many sales closed traces back to How many calls happened traces back to How many leads booked traces back to How many leads came in traces back to How many people clicked the ad He finds the first drop in the chain. Fixes that one first. Then moves to the next. Victor: "The first hole we have is the one we need to plug, then we need to plug the rest." The key word is "first." Most media buyers try to fix everything at once. Victor fixes one thing, measures the impact, then moves down the chain. E-Commerce Clients Get 6 Metrics, Not 3 Lead gen clients get the simple three. E-commerce is different. Victor uses six: Spend ROAS (return on ad spend) Sales amount (total revenue) Number of orders Cost per purchase Cost per acquisition Why six? Because different e-commerce owners care about different numbers. A mono-product shop might watch orders. A higher-ticket store watches revenue. Victor: "Different people could look at different things. So I think in e-commerce we should have six metrics, not three." The Warning Signs That Make Victor Drop Everything The biggest pain point Victor described: he cannot actively monitor every client every day. Facebook sends notifications for everything, approvals, rejections, status changes. Knowing which tools actually cut through that noise is why I wrote about the best AI tools for Meta Ads in 2026 . The important stuff gets buried. Here is what makes him stop and investigate immediately: CPL jumps 50%+ overnight: Something broke. Could be a rejected ad, a broken landing page, or a budget issue. CTR drops from 3% to 0.1%: The creative died or the ad got rejected without a clear notification. Spend drops to zero unexpectedly: Campaign paused, account blocked, or a verification issue nobody saw. Frequency gets too high: The same people are seeing the same ad too many times. Creative fatigue is setting in. Account rejections or blocks: Especially dangerous in sensitive verticals like finance. Victor has lost campaigns for days because nobody noticed an account block. Victor: "Your CPL with Pablo went up 50% and with Juan the frequency is too high. Try to find what you need to change in every client." He wants one view. All clients. Color-coded by severity. Click a warning, land in the client's data. That is the agency dashboard we are building at Sucana. How Victor Uses Claude to Run This Check in 30 Seconds Victor does not do all of this manually anymore. He built a Claude skill that holds his entire diagnostic chain. The metric hierarchy, the funnel logic, the warning thresholds, all of it lives inside one prompt. I took this a step further and built a full agency brain on Claude Code that houses every skill in one place. Here is how he does it, step by step. Step 1: Build a Claude skill with your diagnostic logic. Open Claude and create a project. In the project instructions, paste your version of Victor's chain: "When I upload campaign data, check spend, leads, and CPL first. Compare this week to last week. If CPL increased more than 20%, check CPM, CTR, and CPC. If CPM and CTR are stable but CPL went up, the problem is on the landing page. If CTR dropped, the problem is the creative. If CPM spiked, the problem is auction competition. Always tell me where the problem is and what to fix first." That skill now lives in your Claude project. Every time you come back, it remembers how to read your data. For more on writing effective instructions, see my AI prompt engineering guide for PPC . Step 2: Export your campaign data as a CSV. Go to Ads Manager . Select your campaigns. Export the last 30 days as a CSV file. Include spend, impressions, clicks, leads, and CPL at the campaign level. Step 3: Upload the CSV and ask Claude. Drop the CSV into your Claude project. Ask: "Run the diagnostic chain on this data. Which campaigns need attention?" Claude reads the file, runs the chain, and comes back with exactly what Victor would say. Except it takes 30 seconds per client instead of 12 minutes. Victor missed a landing page conversion drop for two weeks. This check would have caught it on day one. Step 4 (optional): Use Sucana instead. This same approach works for Google campaigns too. I covered automating Google Ads reporting with AI in a separate guide. If you do not want to build this yourself, that is what Sucana does. We connect to your ad accounts directly. No CSV exports. No manual uploads. The diagnostic chain runs automatically every day across all your clients. You open the dashboard. Warnings are already there. Click one, see the data, ask the AI what happened. That is the difference between a Claude skill you built yourself and a product built for agencies. Both work. One saves you the setup. Frequently Asked Questions What metrics should a media buyer check every day? Start with three: spend, leads (or purchases), and cost per lead (or cost per purchase). These tell you if the campaign is on track. If all three are stable, move to the next client. Only dig into secondary metrics like CPM, CTR, and CPC when cost per lead spikes. Checking everything every day wastes time without improving decisions. How do I know if my CPL is too high? Compare it to your own baseline, not industry averages. If your CPL was $15 for three weeks and jumps to $30, that is a problem. The absolute number matters less than the trend. Industry averages vary wildly. The median CPL across 7,000+ agencies is $50.51 . Your niche, offer, and funnel will set your real target. I wrote a full Facebook Ads CPL troubleshooting checklist for when things go sideways. What causes CPL to spike overnight? Four common causes: landing page broke or changed, ad creative is tired and CTR dropped, CPM increased from more auction competition, or an ad got rejected and spend shifted to worse ads. Run Victor's diagnostic chain. Check CPM, CTR, and CPC. The combination tells you where the problem lives. How is analyzing e-commerce campaigns different from lead gen? E-commerce needs six metrics instead of three. Spend, ROAS, sales amount, number of orders, cost per purchase, and cost per acquisition. Different e-commerce owners prioritize different numbers depending on their product and margins. Lead gen is simpler because the output is one thing: a lead. E-commerce has revenue, order count, and order value as separate signals that all matter. How often should I check campaign data? Daily for active campaigns. Victor checks every client's three primary metrics every morning. The full check takes under two minutes per client. Weekly deep dives catch trends that daily checks miss. Compare this week's CPL to the four-week average. Flag any campaign where CPL increased more than 20%. What is the difference between CPL and CPA? CPL is cost per lead, what it costs to get someone to raise their hand. CPA is cost per acquisition, what it costs to get an actual customer or sale. A low CPL with a high CPA means your leads are cheap but not converting. This is the trap Victor flagged with Latin America, cheap leads that never close. How do I know if the problem is my ad or my landing page? Run Victor's diagnostic chain. If CPM, CTR, and CPC all look normal but CPL went up, the problem is on the landing page. The ad is doing its job. People are clicking. They are just not converting after they land. If CTR dropped, the problem is the ad. If CPM spiked, the problem is auction competition. What should an agency dashboard show at the top level? A table of all clients with their primary metrics and color-coded warnings. Red for critical issues, orange for things that need attention. Victor wants to see which clients need action without clicking into each one. The flow is: see the warning, click into it, land in the client data, investigate. Not the other way around. How do I tell if creative fatigue is the problem? Watch frequency and CTR together. If frequency is climbing and CTR is dropping, the same people are seeing the same ad too many times. They stopped clicking because they have seen it before. Refresh the creative. Do not adjust the budget or targeting. The audience is fine. The ad is tired. Can AI replace a media buyer's daily analysis? AI can run the diagnostic chain faster than any human. It catches patterns in seconds that take hours manually. Victor my co-founder watched our AI describe his exact strategy from the numbers alone. But AI cannot decide what to do next. I covered the full picture in my guide on AI for Facebook Ads . Victor caught a Latin America scaling trap because he knows cheap leads do not always close. That judgment comes from experience, not data processing. The answer is both: AI for speed, human for judgment. --- ## AI for Facebook Ads: What's Actually Worth Using URL: https://sucana.ai/blog/ai-for-facebook-ads Published: 2026-03-03 Author: Virgil Brewster Tags: ai, meta-ads, media-buying, automation, agencies What AI Tools Actually Work for Facebook Ads? The AI tools that actually work for Facebook ads are Meta's Advantage+ suite for bidding and placement, paired with creative testing platforms. We run Facebook campaigns at Sucana and found Meta's native AI handles most optimization. Third-party tools matter most for creative generation and cross-platform reporting. Meta's own AI tools do most of the heavy lifting now. Third-party tools fill the gaps Meta leaves open. I tested Advantage+, ChatGPT, Claude, and a handful of paid platforms on real Facebook campaigns over the past year. A few saved hours. Others wasted money. Most of the "best AI tools" listicles out there have never run a single ad. This is what I found when real money was on the line. The Story That Changed How I Think About AI and Facebook Ads Victor my co-founder said something a few weeks ago that stuck with me. He was reviewing a campaign analysis that our AI had just run. His exact words: "It's real that I'm above average. I'm not the best, but I'm above many media buyers and I wouldn't see that." The landing page conversion drop? Missed it. The phase transitions? Missed those too. The Latin America scaling trap? Gone without AI. And Victor is good at this. That's the point of AI for Facebook ads. Not replacing Victor. Finding what Victor misses. Meta's Native AI: What Advantage+ Actually Does Meta has pushed hard on Advantage+ over the past two years. The pitch is simple: let the algorithm handle targeting, creative selection, and placement. The reality is messier. I've seen Advantage+ Shopping campaigns deliver 2x ROAS on day one, then tank by week three. The r/FacebookAds subreddit is full of media buyers with the same experience: "I'm getting whiplash on whether Advantage+ is better or worse." It does a few things well. Broad prospecting with high-quality creative is where it shines. It removes guesswork on placements and scales faster than manual campaigns. But it falls apart on transparency. You can't see which audience segments drive results. When performance drops, you have almost no levers to pull. Meta took the steering wheel and sometimes drives you off a cliff. Third-Party AI Tools I've Actually Used I've tested more tools than I want to admit. I wrote a full breakdown of the best AI tools for Meta Ads in 2026 . Nearly all of them promise to "revolutionize" your Facebook ads. They don't. AdCreative.ai: I used it for three months. It generates ad creative variations quickly, and the images are decent for static ads. The copy is generic. I ended up rewriting 80% of what it produced. Useful if you need volume, not if you need quality. Madgicx: This one surprised me. It automates bid adjustments and pauses underperforming ads based on rules you set. The automation saved Victor about four hours a week on one client account. The audience targeting suggestions were hit or miss. ChatGPT and Claude for ad copy: I use Claude for writing Facebook ad copy. I feed it the product details, the target audience, the pain points from real customer conversations. Then I tell it to write five variations. Three will be usable, two will need work. Better ratio than any dedicated ad copy tool I've tried. The trick is specificity. A vague prompt gives you garbage. But "write a Facebook ad for a B2B SaaS tool that helps agencies track ad spend, targeting agency owners frustrated with manual reporting" gives you something real. The AI Tool Nobody Talks About: Your Own Data This is where most articles stop. They list the tools, rank them, and move on. I wrote a full guide on AI for performance marketing that goes deeper across every platform, but here is what matters for Facebook specifically. But the most powerful AI tool for Facebook ads isn't a product you buy. It's what happens when you point AI at your own campaign data. Victor ran a test last month. His client's CPL had been climbing during scale, then pulled back, then stabilized. Our AI analyzed the pattern and labeled it: "natural floor for this current creative and targeting settings." Most media buyers know this concept intuitively but never name it. The natural floor is the lowest CPL you can reliably hit with your current creative and audience combination. If you want to know where that floor sits relative to your industry, the Facebook Ads benchmarks for 2026 breaks it down by vertical. When CPL stabilizes there, stop adjusting spend. Refresh the creative instead. If your CPL is climbing and you need a structured approach, check out this CPL troubleshooting checklist . No third-party tool told Victor that. That's what we're building at Sucana: AI that talks to your actual ad data and finds what you'd miss manually. Victor used it on a live client account, and it caught patterns he'd been overlooking for weeks. Same week, the AI flagged Latin America as outperforming other regions on CPL. Victor looked at it and paused. The question isn't which region has the lowest CPL. It's which region converts to actual sales. Latin America can have cheap leads that never close. Victor said: "The real question is, will this compare to sales?" Two different questions. Most media buyers only ask the first one. What Actually Moves the Needle After a year of testing, here's what I'd actually spend money and time on. Use Advantage+ for broad prospecting. Let Meta's AI handle the initial targeting. Feed it your best creative and don't fight the algorithm on placement. Watch the results daily for the first two weeks. Use ChatGPT or Claude for ad copy. Skip the dedicated AI copywriting tools. A good prompt with real context produces better copy than any tool with a $99 monthly subscription. Save the money. Use a rules-based automation tool for bid management. Something like Madgicx or Revealbot that pauses bad ads and scales winners automatically. This is where AI saves the most time on a daily basis. Point AI at your own data. This is the big one. Export your campaign data and feed it to Claude or any capable AI. I explain how Claude reads campaign data in a separate walkthrough. Ask specific questions: "Which campaigns had the highest CPL increase week over week?" Or: "Which creative had the best CTR but worst conversion rate?" The answers are in your data. You just need something smart enough to find them. What's Not Worth Your Money I'll be direct. AI tools that promise to "optimize" your campaigns automatically without showing you what they changed. If you can't see the logic, you can't fix it when it breaks. Creative generators that don't learn from your brand. Generic AI images and copy look like generic AI images and copy. Your audience can smell it. Any tool that claims to "beat the algorithm." Nobody beats Meta's algorithm. You work with it or you lose to it. The tools that promise otherwise are selling hope, not results. The Real Shift Happening Right Now Meta released the Andromeda algorithm update and the entire media buying industry panicked. Results dropped across the board. Campaigns that had been printing money for years stopped working overnight. The old playbook was: set up manual targeting, test ten audiences, find a winner, scale it. That playbook is dead. The new playbook is: create great ads, feed the algorithm data, and use AI to analyze what comes back. The media buyer's job isn't clicking buttons in Ads Manager anymore. It's making decisions the algorithm can't make. Victor's client had 37 media buyers. The founder told us he wanted to test our tool with one account first. Then he said: "When it works for him, he's gonna buy." Not a free trial. Not maybe. When it works. That's the shift. Agencies that combine Meta's AI with their own AI tools will win. The ones still doing everything manually are already behind. How to Start Using AI for Facebook Ads This Week Don't overhaul everything at once. Start with one campaign and one tool. Pick your highest-spend campaign. Export the last 30 days of data. Open ChatGPT or Claude. Paste the data and ask: "What patterns do you see in this campaign data? Where am I wasting money?" Read what comes back and check it against what you already know. If the AI catches something you missed, you just got your answer about whether AI tools are worth it. Victor ran that exact test. He found a landing page conversion drop he'd missed for two weeks. Two weeks of wasted budget. A five-minute AI analysis could have prevented it. Start there. If you want something purpose-built for this, that's what Sucana does. But even a free AI chat with your exported data is better than flying blind. The fancy tools can come later. Frequently Asked Questions What AI tools work best for Facebook ads? Meta's Advantage+ handles targeting and placement well for broad campaigns. For ad copy, ChatGPT and Claude outperform most dedicated tools when you give them specific context. For automation, Madgicx and Revealbot save time on daily bid management. The best combination depends on your budget and team size. A solo media buyer gets the most value from AI copy tools and data analysis. A larger agency benefits more from automation platforms. Is Meta Advantage+ worth using? For broad prospecting campaigns with strong creative, yes. Advantage+ removes manual targeting work and tests more combinations than you could manually. For niche audiences or strict brand requirements, it's risky. You lose control over who sees your ads and where they appear. Test it on one campaign before committing your full budget. Can AI write Facebook ad copy? It can write first drafts that save you hours. I use Claude with detailed prompts about the product, audience, and pain points. Three out of five variations are usually usable with light editing. The key is prompt specificity. Feed the AI real customer language, actual objections, and specific product details. Generic prompts produce generic copy. How do I automate Facebook ad testing with AI? Start with Advantage+ for creative testing within Meta's platform. It rotates variations automatically and pushes budget toward winners. I wrote a separate guide on how to test Meta Ads creatives with AI that covers this in detail. For testing outside Meta, use a tool like Madgicx to set rules. Pause any ad with CPL above your threshold after 1,000 impressions. Scale any ad with CPL below target by 20%. Simple rules, run automatically. Does AI improve Facebook ad ROAS? It can, but not the way most people expect. AI doesn't magically improve bad ads. It helps you find what's working faster and cut what's not. Victor my co-founder improved one client's ROAS by finding a landing page conversion drop that had been running for two weeks. Not magic. Just fast pattern recognition. What's the difference between Advantage+ and third-party AI tools? Advantage+ lives inside Meta's ecosystem. It controls targeting, placement, and creative optimization using Meta's own data. You get broad reach and hands-off management. Third-party tools sit on top of your campaigns. They analyze data Meta doesn't surface, automate actions Meta doesn't offer, and generate creative assets. Think of Advantage+ as the engine and third-party tools as the dashboard gauges. How much do AI Facebook ad tools cost? Free options exist. ChatGPT and Claude can analyze campaign data and write copy at low or no cost. Meta's Advantage+ features are built into Ads Manager at no extra charge. Paid tools range from $49 to $499 per month. Madgicx starts around $49, and AdCreative.ai runs about $29 for basic plans. Start free. Add paid tools only when you hit a specific bottleneck. Can AI replace a media buyer for Facebook ads? Not today. AI can handle the repetitive parts: bid adjustments, performance monitoring, copy variations, data analysis. Those tasks eat 60% of a media buyer's week . The 40% that remains is strategy, client communication, and creative direction. AI can't sit in a client meeting and read the room. It can't decide when to pivot a campaign based on a gut feeling backed by years of experience. How do I use ChatGPT for Facebook ad copy? Open ChatGPT or Claude. Give it everything: product name, features, target audience, their biggest frustration, the offer, and the format you need. Ask for five variations. Review each one and keep the best two or three. Edit the language to match your brand voice. The AI gives you the structure and ideas. You give it the final polish. What's the best AI tool for Facebook ad creative? For images, AdCreative.ai generates decent static ad variations quickly. For video, Meta's own Advantage+ creative tools can convert images to video clips automatically. For the best results, combine AI-generated concepts with human design. Use AI to produce ten rough concepts, pick the three strongest, and hand those to a designer for final production. AI handles the volume. Humans handle the taste. --- ## How to Pitch AI Services to Agency Clients (2026) URL: https://sucana.ai/blog/pitch-ai-services-agency-clients Published: 2026-03-01 Author: Virgil Brewster Tags: ai, agencies, automation, sales How Do You Pitch AI Services Without Sounding Like Every Other Agency? You pitch the outcome, not the technology. Nobody cares about your n8n workflows or your Claude prompts. They care about the 6 hours a week their team wastes on reporting. A mentor of mine, Ben, runs an AI agency. He told me something that changed how I think about selling AI: "Your first project is not about cashing in. It is about showing one win as fast as possible." That stuck with me. What Does a Good AI Sales Process Look Like? A good AI sales process is short and qualification-heavy. We use an AI-powered intake form to filter leads before anyone gets on a call. By the time I talk to a prospect, I already know their problem, budget, and expectations. The call becomes about solving, not qualifying. After 20 years in business, the pattern is always the same. People overcomplicate sales. The same lesson I learned when getting my team to adopt AI : start with one thing, not five. They jump on a call with anyone who books, scope every detail, then send a proposal three weeks later. Then they wonder why deals fell apart. The simpler model: qualify first, then one call. The qualifying happens before anyone gets on your calendar. An AI-powered intake form asks the right questions upfront. By the time someone books a call, you already know if they are worth talking to. How Do You Qualify an AI Client Before the Call? I qualify AI clients before the call using a four-question intake form. It asks what problem they want solved, their budget range, what tools they use, and what success looks like. Two red flags disqualify immediately: extreme skepticism about AI or expecting one agent to automate their entire business. Not every lead deserves a call. Too many agencies chase leads that look perfect on paper. The intake form does the filtering. It asks: What problem are you trying to solve? What is your budget range? Then: What tools does your team use today? What does success look like for you? Those four questions tell you almost everything. There are two red flags that disqualify immediately. The first is extreme skepticism. If they think AI is a gimmick, that project will be painful. Every deliverable will face resistance. The second is the silver bullet seeker. One AI agent to automate their entire business. They saw a YouTube demo and think it works like that in production. It does not. The sweet spot is a client who believes AI can help and has a realistic view of what it takes. Every AI project needs client input, data access, and feedback loops. On budget, the form handles it. "What would you expect this to cost?" filters out the people who want a custom suite for a thousand dollars. By the time I get on the call, I already know their problem, their budget range, and their expectations. The call is not about qualifying. The call is about solving. Why Should You Always Pitch the Long-Term Partnership First? I pitch the long-term partnership first because it qualifies the client and sets the right frame. Clients shopping for cheap one-off builds reveal themselves immediately. Framing the first project as a starting point shifts their thinking from "buying one automation" to "testing a partnership." That is how we land retainers. Pitch the retainer on call one. Not to close it, but to set the frame. Tell every client: "We are only interested in long-term partnerships. We will do a paid project first to build trust. But our goal is to become your AI partner." This does two things. It qualifies the client. If they are shopping for the cheapest one-off build, they will tell you right away. Those clients haggle on price, expand scope without paying, and never come back. It also sets the expectation that this first project is a starting point. The client stops thinking "I am buying one automation." They start thinking "I am testing a partnership." That is how you land retainers. Consistent, reliable income. Not feast and famine. How Do You Price AI Projects Without Guessing? I price AI projects by estimating the hours, applying my hourly rate with a 30 to 40 percent margin, then adding 50 percent on top. That buffer is not greed. Every AI project takes longer than expected. API limits, messy client data, and sick days are real. Quote with buffer and deliver on time. A lot of people online talk about value-based pricing for AI services. Charge based on ROI. Sounds great in theory. In practice, it rarely works for custom projects. The client has never seen the results yet. You are guessing how many hours their team will save. And what that time is worth. It is guesswork dressed up as math. The simpler model: estimate the time it takes. Put an hourly rate on it with a 30 to 40 percent margin. Then add 50 percent on top of that. That last part is not greed. It is reality. Every AI project takes longer than expected. API limits pop up. The client's data is messier than they said. An engineer gets sick. If you quoted tight, you eat the cost. If you quoted with buffer, you deliver on time. Victor my co-founder says something similar. The biggest problem in any agency is not the work itself. It is finding good people to do it. The same applies here. Time is the real cost, and time always expands. What Is the Biggest Mistake When Delivering AI Projects? Starting too big. This is where most agencies fail. They scope three automations when the client only needs one to believe. A 30-day timeline becomes 45 days. Credentials are late. Internal approvals get stuck. Someone on the team goes on sick leave. The lesson is obvious but almost nobody follows it: start with the smallest piece that still delivers value. One automation. One clear win. Get it into production. Let the client see results. Then sell the next one. The other mistake is presenting AI as a finished product. Tell every client upfront: "The first two weeks after deployment are a testing phase. Things will break. We will fix everything." This reframes expectations completely. Instead of losing trust when something breaks, you gain trust when you fix things quickly. Chatbots hallucinate URLs in production. Validators flag everything as needing review. These problems are normal. The difference is whether the client expects them or not. What Does a Small First Win Actually Look Like? Here is a real example of the right scope for a first project. A client runs a marketing agency. Their account managers spend 90 minutes every Monday pulling campaign data from three platforms and pasting it into a Google Sheet for client reports. The first AI project: automate that pull. One automation. One Google Sheet. One delivery every Monday morning before anyone starts their day. That is it. No dashboard. No chatbot. No full reporting suite. The team gets 90 minutes back every Monday. The client sees the result on week one. I wrote a full walkthrough on automating client reporting with AI that covers exactly this kind of first project. Now they trust you. Now you can sell the dashboard. The temptation is to scope the full vision immediately. Build the intake form, the report automation, and the client-facing dashboard all at once. The problem is that a 60-day project has 60 days of things that can go wrong. A 10-day project has 10. Ben's rule applies here: one win as fast as possible. The client's confidence in the first 30 days determines whether you get months two through twelve. Pick the automation that solves their most painful weekly task. Ship it. Then expand. How Do You Keep Clients After the First Project? Communication. That is the answer, and it is boring, but it is true. The mistake most agencies make: engineers talking directly to clients on Slack. No project manager. No structured updates. The result is chaotic messages, missed deadlines, and a client who feels like nobody is in charge. Add a delivery manager. Weekly email updates. A scope of work document that clearly defines what is included and what is not. The delivery manager frees engineers to focus on building. The client gets one point of contact instead of three. When you are growing your AI service business, this layer becomes necessary. The moment you are juggling three projects, the chaos will cost you a client. The scope of work document is the other piece. It lists what is covered and what is not. Scope creep kills AI projects. A client will say "can you also add this small thing" six times. Without a clear scope document, you cannot push back. What Actually Closes the Deal? Numbers. Not your pitch deck. Not your proposal. The client does not care about your process until they see what it produces. One clear result changes the entire conversation. That is the real sales strategy. Get one win. Show it with real numbers. Let the results do the selling. Vinod my co-founder puts it simply: many people can build. Thousands of developers can write code and set up automations. But the product goes nowhere without knowing how to sell it. The scarcest skill is not building the AI. It is getting someone to pay for it. Pitch the pain, not the tech. Qualify fast. Start small. Price with buffer. Deliver one win. Let the numbers talk. For a full guide on building the operational side of an AI agency — the systems, the tech stack, and the ops layer that protects your margin — see my breakdown of how to build an AI-powered marketing agency . Frequently Asked Questions How do I find my first AI agency client? Start with businesses you already know. Look at companies in your network that have repetitive processes eating hours every week. Content marketing works too. Agencies land clients through YouTube tutorials all the time. Unrelated demos attract business owners who see the potential. How much should I charge for my first AI project? Estimate the hours, add your hourly rate with a margin, then add 50 percent for unexpected delays. Keep the scope small enough that the total feels low-risk. Most initial AI automation projects land between 2,000 and 10,000 dollars. The goal is not to cash in on project one. It is to land a long-term partnership. How long does a typical AI automation project take? A well-scoped single automation takes two to four weeks to build. Add another two weeks of testing in production. Always quote 50 percent more time than you think. It is better to deliver early than to miss a deadline. What tools do I need to start an AI agency? At minimum: an AI model like Claude or GPT, an automation platform like n8n or Make.com , and a project tracker. I compared the most useful ones in my guide on AI marketing automation tools . The tools matter less than the process. A clear sales workflow and delivery system will beat fancy tech every time. How do I handle scope creep on AI projects? Write a scope of work document before starting. List what is included and what is excluded. When a client asks for something outside scope, offer it as a separate project. This is not about being rigid. Uncontrolled scope changes are the number one reason AI projects go over budget. What if the AI solution breaks in production? It will happen. Every AI project hits issues in production that testing did not catch. Chatbots hallucinate. APIs hit rate limits. Frame the first two weeks as a testing phase. Tell the client upfront. Fix issues quickly. This builds more trust than a perfect launch ever would. Should I specialize in one type of AI service or offer everything? Specialize. Pick one type of automation you can deliver consistently and build a repeatable process around it. Once your first service is solid and generating referrals, add a second. Not before. How do I pitch AI to a skeptical client? Lead with their problem, not with AI. Do not open with "we build AI automations." Try "I noticed your team spends 30 hours a week on manual reporting." Let them feel the pain first. Then introduce the solution. What is the difference between a one-off AI project and a retainer? A one-off project builds one solution and ends. A retainer makes you the client's ongoing AI partner, maintaining automations and building new ones. Retainers create predictable revenue. Always pitch the partnership model from the first conversation. How do I know if a client is ready for AI automation? They have a clear, repetitive process that eats hours every week. Their software has API access. Expectations are realistic. If they cannot describe their current process clearly, they are not ready. The best clients sit in the middle between skeptic and silver bullet seeker. --- ## AI Adoption Strategy for Marketing Teams (Step-by-Step Framework) URL: https://sucana.ai/blog/marketing-team-ai-adoption-strategy Published: 2026-02-27 Author: Virgil Brewster Tags: ai, automation, agencies, marketing-strategy How Do You Get a Marketing Team to Actually Use AI? Start with the tasks they already hate. Not the ones you think AI should do. If your team spends Monday morning pulling numbers into spreadsheets, that's your first target. Nobody resists a tool that kills the task they dread. I learned this during our beta launch at Sucana. We were setting up communication for testers. I wanted everyone on Basecamp. Vinod my co-founder said one sentence that changed everything. "Nobody's going to open Basecamp just for us." He was right. Slack is where they already live. Basecamp is where we wanted them to be. Two different things. That lesson applies to every AI rollout I've seen since. The tool comes second. The pain comes first. Why Does AI Adoption Fail in Marketing Teams? AI adoption fails because teams pick the tool before identifying the problem. Leadership buys a license, sends a Slack message, and nobody touches it after week one. MIT found that 95% of AI pilots fail to show ROI. The fix is simple: start with the task your team hates most, not the AI tool that looks impressive in a demo. Because leadership picks the tool first and the problem second. I've watched it happen. A founder reads about ChatGPT. Buys a team license. Sends a Slack message: "We're using AI now." Nobody touches it after week one. The problem isn't the tool. The problem is nobody asked the team what hurts. An MIT study found that 95% of AI pilots fail to show ROI . That number makes sense. Most pilots start with "let's try AI" instead of "let's fix this one broken thing." Marketing teams don't resist AI because they're scared. They resist because nobody showed them how it helps their Tuesday afternoon. What Tasks Should You Automate First? Automate the repeatable, time-consuming tasks that require zero creativity. Weekly reporting, data pulls from ad platforms, and spreadsheet formatting are the best starting points. At Sucana, we automated campaign data pulls from Meta and Google first. Vinod built the sync that replaced two full mornings of manual Excel work for our beta testers. The boring ones. The ones your team does every week that eat hours and require zero creativity. I break down the exact criteria and costs in my guide on AI automation for marketing agencies . I look for three things when picking the first task to hand to AI. Repeatable: It happens the same way every time. Weekly reports. Monthly summaries. Data pulls from ad platforms. Time-consuming: It takes more than an hour each time. If it takes five minutes, leave it alone. Low-judgment: The decisions are routine. Copy a number from here, paste it there, format it this way. No strategic thinking required. At Sucana, the first thing we automated was pulling campaign data from Meta and Google. I wrote a full guide on automating client reporting with AI that walks through this exact process. Vinod my co-founder built the sync. Before that, our beta testers were spending Monday and Tuesday combining Excel sheets for clients. Every single week. That was the pain. That was the starting point. Not "let's use AI for strategy." Not "let's generate content with ChatGPT." Just: kill the spreadsheet Monday. How Do You Build an AI Marketing Strategy That Sticks? Pick one task in week one, run the AI version alongside the manual process in week two, then measure and decide in week three. Three weeks, one task. That is the entire strategy for round one. After it works, the team comes to me with the next task on their own. I never have to push. They pull. One task at a time. Week by week. The mistake I see founders make: they try to roll out five AI tools on the same Monday. Content generation, ad copy, reporting, analytics, chatbots. The team drowns. Nobody learns anything well. Here's what worked for me. Week one: pick one task. Find the task your team complains about most. For us it was reporting. For your team it might be competitor monitoring or social scheduling. Pick one. Week two: run it side by side. Don't replace the old process yet. Run the AI version next to the manual version. Let your team see the difference. No pressure of "this has to work." Week three: measure and decide. Did it save time? How much? Did the output quality match what your team produces manually? If yes, the old process dies. If not, adjust and try again. Three weeks. One task. That's the whole strategy for round one. I cover the full process in my guide on building AI workflows for your marketing team . After it works, your team will come to you with the next task. They'll see what's possible. You won't have to push. They'll pull. What About the Fear? It's real. Don't pretend it isn't. I've read the data. Early-career marketers aged 22 to 25 have seen a 20% drop in headcount from AI . I dug into the full picture of what the data actually shows about AI replacing marketers . Some agency owners cut their teams by 60% in 2026. Those numbers are real and your team has seen them too. The honest answer: AI will change what people do on your team. Some tasks will disappear. The people who adapt will do more interesting work. The people who don't will struggle. I don't sugarcoat this with my team. I tell them: the goal is not to replace you. The goal is to remove the work that wastes your talent. Reporting doesn't need a skilled marketer. Strategy does. When Vinod my co-founder saw our data sync work for the first time, he didn't feel threatened. He felt relief. The system did in 5 minutes what used to take him hours of manual API calls. Not replacement. A tool that gives you back your afternoon. How Do You Handle the Pushback? Meet them where they are. Not where you want them to be. I learned this the hard way with the Basecamp lesson. Your team lives in certain tools. They have certain habits. Forcing them into a new workflow on day one is a recipe for quiet resistance. Three things that reduce pushback. Let them pick the first use case. Ask your team: what do you wish you didn't have to do? Whatever they say, that's your pilot. When the team picks the task, they own the outcome. Show, don't mandate. Run the AI tool yourself first. Show the output in a team meeting. Let them see it work on real data, their data. A demo beats a memo every time. Give them permission to fail. The first prompt won't be perfect. The first workflow will break. That's fine. If people feel judged for not "getting AI" fast enough, they'll stop trying. I've watched Vinod spend days figuring out a single data sync. He broke it, rebuilt it, broke it again. That iteration is how you get to something that works. Your team needs the same space. What Does a Realistic AI Adoption Timeline Look Like? A realistic AI adoption timeline is three months. Month one: automate one task and show the team real time savings. Month two: add a second task while someone on the team becomes the unofficial AI champion. Month three: the old way feels slow and new hires wonder why it was ever done by hand. Most teams save 4 to 6 hours per week within 30 days. Longer than the LinkedIn posts say. Shorter than you fear. Month one: One task automated. The team sees time saved. Skeptics are curious, not convinced. Month two: Second task added. The team starts suggesting use cases. Someone on the team becomes the unofficial AI person. Let them. Month three: The old way of doing things feels slow. New hires ask why you ever did reporting by hand. The culture has shifted. Most teams I've talked to report 4 to 6 hours saved per week within 30 days. The real payoff comes at month three when the team stops thinking of AI as a separate thing. It's just how the work gets done. What Tools Should a Marketing Team Start With? The ones that solve the task you picked in week one. I'm not going to give you a list of 47 tools. That's part of the problem. Too many options paralyze teams. Pick the task first. Then find the simplest tool that handles it. If you are not sure where your team's skills need to develop to support this, I put together a breakdown of the AI skills every marketer needs in 2026 . For reporting and data pulls, we built Sucana. For ad copy, I use Claude. For workflow connections between tools, I've tested Make.com. The tool doesn't matter as much as the process. A team that knows which task they're solving will find the right tool in a day. A team browsing AI tool lists will still be browsing next quarter. The One Rule I Follow Start small. Stay small. Let it grow. Every successful AI adoption I've seen follows this pattern. The failures all tried to do everything at once. Once you find your rhythm, turn your processes into AI SOPs for your agency so any team member can run them. I didn't wake up one day and replace my entire workflow with AI. I started with one data question, then another. Eventually I let the AI analyze a full campaign. Each step built on the last. Your marketing team is no different. One task, one tool, one win. Then the next. That's the strategy. It's not complicated. The hard part is resisting the urge to do it all at once. Frequently Asked Questions How long does it take for a marketing team to adopt AI? Most teams see time savings within the first week if the use case is well-scoped. The culture shift takes about three months. The real milestone is when nobody calls it "the AI tool" anymore. It's just how the work gets done. What are the biggest barriers to AI adoption in marketing? Lack of a clear use case is the top barrier. "Let's try AI" leads nowhere. But "let's fix this broken report" leads to real adoption. Training and time investment rank second. People need space to learn without pressure. Which AI tools should a marketing team start with? The one that solves the task your team picked in week one. For reporting, look at Sucana. For content, Claude or ChatGPT. For workflow connections, Make.com or n8n. Don't browse tool lists. Pick the task, then find the tool. How do you train a marketing team to use AI? Start with one person. Let them learn the tool deeply. Then have them teach the rest of the team through real examples, not training slides. Peer learning beats top-down training every time. People trust their colleague's demo more than a vendor webinar. Will AI replace marketing jobs? AI replaces tasks, not jobs. The tasks that disappear are the ones nobody wanted to do anyway. Marketers who learn to use AI tools become more valuable. They do the same work in less time, or they do higher-level work that wasn't possible before. What tasks should marketing teams automate with AI first? Reporting, data pulls, competitor monitoring, and social media scheduling. These are high-frequency, low-creativity tasks that eat hours every week. Stay away from strategy and creative direction as your first AI use case. Those require judgment that AI can't reliably provide yet. How do you measure ROI on marketing AI tools? Time saved per week is the simplest metric. Track how long the task took before AI and how long it takes now. Most teams report 4 to 6 hours saved weekly. Multiply by your team's hourly rate. That's your monthly ROI. What does an AI adoption roadmap look like for agencies? First month: automate one repeatable task for the whole team. Second month: add another task and identify your internal AI champion. By month three, the team suggests new use cases on their own. The roadmap is simple because simplicity is what makes it work. Complex rollout plans stall at month one. How do you handle team resistance to AI tools? Let the team pick the first use case. Show them the tool working on their own data. Give them permission to fail. Resistance drops when people feel ownership over the process. Mandates from leadership create the opposite effect. What is the difference between marketing automation and AI? Marketing automation follows rules you set. Send this email when someone clicks this link. It does exactly what you tell it. AI learns from data and makes decisions. It reads your campaign numbers and spots patterns you missed. Automation executes. AI thinks. How do you get buy-in from leadership for AI tools? Run a small pilot first. Measure the time saved. Show leadership the numbers from one real task, not a pitch deck about AI potential. Real results from a real task beat projections every time. What mistakes do marketing teams make when adopting AI? The biggest mistake: trying to automate everything on day one. Five tools, ten use cases, total chaos. The second biggest: picking the tool before identifying the problem. Start with the pain, not the product. --- ## AI for PPC: What Agencies Need to Know (2026) URL: https://sucana.ai/blog/future-of-ppc-with-ai Published: 2026-02-25 Author: Virgil Brewster Tags: ai, ppc, google-ads, agencies, automation What Does the Future of PPC Look Like with AI? The future of PPC is AI doing the execution while agencies own the strategy and data. Bidding, targeting, and reporting are already automated. The agencies winning in 2026 are the ones feeding AI clean first-party data from their CRMs. Victor runs his entire client portfolio this way now. Control shifted from buttons to inputs. The agencies that win are not the ones fighting AI. They are the ones feeding it better data than everyone else. I wrote a full breakdown of how AI is reshaping performance marketing across every major ad platform. I know because I am building the tools that sit between the agency and the platform. The game changed. The role changed. The agencies that figure this out first will eat the rest. Victor my co-founder runs an ad agency. One of his clients asked him a question that stuck with me. "Why don't I just hire someone in-house instead of paying your rates?" Victor's answer was instant. "The biggest, biggest problem is finding good media buyers. Nearly impossible." That was six months ago. Today the answer is different. The scarcity is real. But the fix is no longer just "hire better." AI now handles what media buyers used to spend all day on. Bidding. Reporting. Search term analysis. Audience signals. The agencies that understood this early are pulling ahead. The ones still doing it the old way are falling behind fast. How Is AI Changing Google Ads Right Now? Google replaced manual campaign controls with AI-first formats like Performance Max, AI Max, and Demand Gen. The platform now picks audiences, writes ad copy, and chooses placements automatically. Agencies lost the levers they used to pull by hand. The new job is feeding the system better data and stronger creative than competitors do. Google pushed Performance Max , AI Max, and Demand Gen as default campaign types. That is not a suggestion. That is the direction. Campaign types are collapsing into AI-first formats. Fine-tuned controls are disappearing. The platform picks your audience, writes your ad copy, and chooses your placements. For agencies used to pulling every lever by hand, this feels like losing the wheel. I talked to Victor about this last month. He said something that clicked. "I used to spend two hours a day on bid adjustments. Now the platform does it. But I spend those two hours reading the data and deciding what to feed it next." That is the shift. The hands moved from the keyboard to the strategy. Why Do PPC Managers Feel Like They Are Losing Control? PPC managers feel like they are losing control because Google keeps removing manual levers and hiding data. Performance Max shows limited search term reports. Campaign transparency dropped. Victor told me he can no longer explain Tuesday's cost per lead spike to clients because the platform won't show what changed. Trust in Google's AI dropped 54% in net sentiment last year. Trust in Google declined 54% in net sentiment last year. That is not a small number. More than half the industry is saying: I trust you less than I did twelve months ago. The frustration is real. Performance Max campaigns show limited data. You can't see search terms the way you used to. Explaining to a client why their cost per lead dropped on Tuesday? Good luck with that. One B2B advertiser ran a Performance Max campaign that chased weekend bot signups. The system did exactly what it was told, just with garbage signals. This is the tension every agency owner feels right now. Google says "trust the AI." Agencies say "show me the data first." What Is the Real Job of a PPC Agency in 2026? The real job of a PPC agency in 2026 is three things: data quality, creative strategy, and client translation. Bidding and targeting are automated. What remains is feeding the AI clean CRM data, testing more creative variations than competitors, and explaining results in language clients understand. We built Sucana around exactly this shift. The job is no longer clicking buttons inside the ad platform. The job is three things. Data quality. The AI is only as good as what you feed it. Clean conversion tracking, proper first-party data, CRM signals that tell the platform which leads turned into revenue. Not just which ones filled out a form. Creative strategy. When bidding and targeting are automated, the only lever left is what the ad looks like. Agencies that invest in creative testing and hook variations will beat the ones still tweaking audiences by hand. Client translation. Someone has to sit between the AI and the client. That person explains why the numbers look the way they do. A dashboard can't do that. It takes someone who understands both the platform and the business. I see this every day with Sucana. We are building the data layer that connects ad platforms to real business outcomes. The agencies using it spend less time in spreadsheets and more time talking to clients. That is the future. How Do Agencies Stay Relevant When AI Does the Bidding? Agencies stay relevant by owning the data layer between the ad platform and the client's business. Connect your CRM so revenue data flows back into Google and Meta. Invest in creative testing. Translate AI output into language clients actually understand. The agencies doing all three are growing. Victor connected his clients' CRMs and cut reporting time by 80%. I told Victor this last month. The agencies that survive are not the ones who learn AI the fastest. They are the ones who learn to ask the right questions. "Show me which campaigns drove revenue, not just leads." AI can't ask that for you. It can answer it, if your data is clean. But the question comes from knowing the client's business. Google's AI can run a campaign and get you leads at $12 each. But if those leads never close, you just burned the budget on empty numbers. The agency's job is knowing the difference. First-party data is the fuel. Every agency I talk to that is growing right now has one thing in common. They connected their CRM to their ad platform. Real revenue data flows back into the system. The AI gets smarter because the inputs are real. The ones struggling? Still reporting on cost per click and impressions. Those numbers mean less every quarter. Will AI Replace PPC Managers? No. But it will replace the ones who only know how to press buttons. The PPC manager of 2026 is not an ad platform operator. They are a strategist who happens to use ad platforms. Victor has ten clients. He used to spend 60% of his time inside Facebook and Google doing manual work. Now he spends that time on creative direction and client calls. His results did not get worse. They got better. He stopped fighting the machine and started feeding it. The role evolved from execution to oversight. From "I set the bid" to "I set the strategy and the AI executes." The managers who made that shift are more valuable than ever. The ones who refused are looking for new jobs. What Should Agencies Do Right Now? I will keep this simple. No wall of theory. Just what to do on Monday morning. Connect your CRM to your ad platforms. If your leads and revenue data are not flowing back into Google and Meta, the AI is flying blind. Start there. Test more creatives. When the platform controls targeting and bidding, the creative is your biggest variable. I wrote a full workflow for writing Google Ads copy with AI that covers how to use Claude to generate and test headlines at scale. Run more hooks, test more angles. Victor generates hundreds of variations per campaign. Stop chasing vanity metrics. Cost per click is dead as a primary KPI. Qualified leads are what matter. If your reporting doesn't show that, fix the reporting first. Build a data layer. This is what we are doing at Sucana. An AI layer on top of your ad data that connects to your CRM. It tells you what is actually working. Not what the platform says. What your bank account says. Learn to question AI output. Performance Max gives you a number. Your job is to ask why. If the AI says ROAS is 4x but the pipeline is empty, something is broken in the data chain. Find it. Here is what Victor's setup looks like now, which is a real example of each of these in practice. He connected his clients' CRMs to Meta and Google. Every closed deal fires a conversion event back into the platform. The AI now trains on revenue, not just form fills. His reporting moved from spreadsheets to a live dashboard connected to all four platforms. He cut his Monday morning reporting from two hours per client to 20 minutes for all clients combined. That freed up four to five hours every week. He uses that time on creative strategy and client calls. The tools are available to every agency right now. What takes time is the setup and the discipline to keep the data clean. One week of CRM cleanup and proper conversion tracking. That is the investment. The payback is faster than most agencies expect. What Happens to Agencies That Ignore This? They shrink. Slowly at first, then fast. I watch this happen in real time. Agencies that still build manual campaigns and export CSVs by hand are losing clients to shops half their size. The smaller agencies move faster. AI made team size less important than data quality. The numbers on AI replacing marketers show the same pattern across the industry. Google dropped the Customer Match threshold from 1,000 to 100 users . Small agencies can now use audience targeting that was locked behind scale. The playing field flattened. If you are thinking about building an AI-powered marketing agency , this is the window. The agencies with five people and clean data will beat the agencies with fifty people and messy spreadsheets. That is not a prediction. It is already happening. Frequently Asked Questions Will AI replace PPC managers? AI will replace the tasks, not the people. Bidding, basic reporting, and audience selection are already automated. The PPC managers who shifted to strategy, creative direction, and client communication are more in demand than ever. The ones who only knew the buttons are at risk. How is AI changing Google Ads in 2026? Google consolidated campaign types into AI-first formats like Performance Max and AI Max . The platform now handles bidding, targeting, and ad copy. Agencies have less manual control but more signal-based influence. The shift is from pulling levers to feeding the machine better inputs. What is AI Max in Google Ads? AI Max automates targeting, bidding, and creative across all of Google's surfaces. It looks at entire conversations and browsing patterns, not just keywords. For agencies, the old keyword-based targeting model is fading. Context and signals matter more than match types. Should agencies trust Google's AI automation? Trust but verify. Google's AI follows whatever signals you give it. Clean tracking and accurate conversion goals produce strong results. Messy data produces messy output. One bad conversion goal can train the system to chase garbage leads at scale. How do PPC agencies stay relevant with AI? Own the data layer between the platform and the client's business. Invest heavily in creative testing. Translate AI output into language clients understand. The agencies that do all three are growing. The ones doing none of them are shrinking. I wrote a full guide on how Claude reads campaign data that shows what this looks like in practice. What skills do PPC managers need in 2026? Data literacy comes first. Read what the AI is doing and know when it is wrong. Mastering AI prompt engineering for PPC is now a core part of that skill set. Creative direction is second. When bidding is automated, the ad is the only variable left. Client communication rounds it out. Explain AI-driven results to business owners who just want to know if the phone is ringing. Is Performance Max better than manual campaigns? For most advertisers, yes. Performance Max reaches all Google surfaces and adjusts in real time. Manual campaigns give more control but demand more time. The sweet spot for agencies: use Performance Max as the engine. Layer on clean data, strong creatives, and negative keyword lists as guardrails. How do agencies explain AI-driven results to clients? Start with business outcomes, not platform metrics. "Your cost per qualified lead dropped 18%." That lands better than "your impression share went up." Connect every number to something the client cares about. Revenue, pipeline, booked calls. The AI gives data. Your job is to give the client a story. What first-party data do agencies need for AI ads? Lead status from your CRM, revenue per lead, and which leads actually closed. Feed that back into the ad platform so the AI knows which conversions matter. Most agencies only send form fills. That trains the AI to find more form fillers, not more buyers. How do you keep control of Google Ads with AI automation? Control shifted from manual settings to strategic inputs. You set the conversion goals, audience signals, creative assets, and negative keyword lists. Set tight guardrails. Review search term reports weekly and kill bad placements fast. The AI runs inside the box you build. --- ## AI Skills Every Marketer Needs in 2026 URL: https://sucana.ai/blog/ai-skills-marketers-2026 Published: 2026-02-24 Author: Virgil Brewster Tags: ai, marketing, automation, workflows What AI Skills Do Marketers Actually Need in 2026? Marketers in 2026 need three core AI skills: spotting repetitive workflows, writing prompts with real data, and building reusable AI skills. I work with Victor and Vinod on this daily. The marketers winning are not using the fanciest tools. They are the ones automating their actual work. The skill to look at your own work and spot the tasks you keep doing over and over. That is where it starts. I wasted six months learning AI the wrong way. I watched tutorials. I read "top 10 AI skills" articles. I tried to understand "prompt engineering" before I even opened a single tool. None of it made me faster at my job. What actually worked was looking at my Monday morning and asking: what am I sick of doing every single week? That question changed everything. Not a single course. Just one honest look at how I spend my time. Can you imagine that me, my non-coding self, can do this? I built a complete automated workflow using Claude Code. It pulls transcripts from Fireflies, updates my roadmap, creates my to-do list, scrapes industry news, and generates content ideas. All of it runs while I drink my coffee. I am a 51-year-old Dutch guy who cannot write a single line of Python. If I can learn these AI marketing skills, any marketer can. Here are the seven that actually matter. Skill 1: Spot the Tasks That Eat Your Time Every marketer has them. The Monday morning report nobody wants to build. The weekly data pull from three platforms. I started by writing down everything I did in a week. Not the creative stuff. The repetitive stuff. Pulling Fireflies transcripts from client calls. Copying data from Google Ads into a spreadsheet. Formatting the same report for different clients. The list was embarrassing. Hours of work that followed the same pattern every time. That is the first AI skill: seeing your own time clearly. My LinkedIn inbox was the other obvious one. I wrote about how I save 2 hours a day on LinkedIn DMs with Claude and Kondo once I applied this lens to that workflow. Most marketers skip this step. They open ChatGPT and type "help me with marketing." That gives you nothing. Victor my co-founder used to spend his entire Monday pulling campaign data. Same clicks, same format, same copy-paste routine. We eventually automated that entire reporting process and got those hours back. When I asked him to list every step, he counted fourteen. Fourteen steps he did every single week without thinking. Once you see the pattern, you can break it. Skill 2: Break Your Work Into Small Steps This is where most marketers get stuck. They look at a task like "do the weekly report" and think it is one thing. It is not. It is twenty small things pretending to be one. The skill is breaking that task into pieces small enough that an AI can handle each one. When I broke down my content creation process, it looked like this. Pull the latest transcript. Find the best quotes. Match them to content ideas. Write a draft. Check my tone of voice. Edit. Post. Each step is simple on its own. An AI can do each one. But if I say "create me a LinkedIn post," it has nothing to work with. Think of it like cooking. You do not tell someone "make dinner." You say: chop the onions, heat the pan, add the oil. Small steps, clear order. I use this for everything now. Data analysis: pull the numbers, compare to last week, flag anything that dropped more than 10%. Market research: scrape Reddit for complaints, sort by most common, pull exact quotes. Content editing: check for long sentences, remove complex words, make sure my voice sounds like me. Every workflow is just small steps lined up. Skill 3: Pick One LLM and Go Deep The biggest mistake I see marketers make is jumping between tools. ChatGPT on Monday. Gemini on Tuesday. Claude on Wednesday. Perplexity for research. You end up knowing five tools at a surface level and none of them well. I picked Claude Code and went all in. Not because it is the only good option. Because going deep with one tool teaches you how to think in AI. The skills transfer. Once you understand how to give context and build workflows in one tool, picking up another takes a day. My entire daily workflow runs through Claude Code. It reads my files, remembers my preferences, connects to my tools. I talk to it like a colleague. "Pull yesterday's Fireflies transcript and find anything Victor said about campaign performance." It does it. That depth took weeks to build. But now I have a system, not a toy. Skill 4: Build Skills and Automated Workflows This is where it gets fun. Once you can spot tasks and break them into steps, you start building things that run on their own. A "skill" in Claude Code is a set of instructions you write once and use forever. I have a skill that generates SEO articles. One pulls meeting transcripts, finds the best insights, and updates my project roadmap. A different one scrapes industry news and gives me content ideas. None of these are code. They are clear instructions written in plain English. If you run an agency, I also wrote about creating AI SOPs for your marketing agency to document these workflows. The difference between a marketer who uses AI and one who builds AI workflows is this. One types prompts all day. The other builds once and runs it a hundred times. I wrote a full walkthrough on building reusable AI workflows for marketing teams . Think about what you could automate. Transcript analysis from client calls. Data analysis across ad platforms. Content creation for social media. Deep market research from forums. Image and video generation for ads. Content editing to match a brand voice. Each one of those can be a workflow you build once. I built my content workflow in one weekend. It now runs every morning. Pulls transcripts, extracts insights, generates ideas, checks my voice file, writes drafts. What used to take me four hours takes twelve minutes. Skill 5: Give Better Context, Because Garbage In Is Garbage Out Every marketer who says "AI gives bad output" is feeding it bad input. This is the skill nobody talks about. The quality of what you give the AI decides the quality of what you get back. When I first asked Claude to write a LinkedIn post, I typed: "Write me a post about AI in marketing." The result was generic slop. Then I gave it my tone of voice file, my content ideas, and three examples of posts I liked. I gave it a specific angle. It sounded like me. Same tool. Completely different result. Context is everything. I covered this in depth in my AI prompt engineering for PPC guide. Your campaign data, your brand guidelines, your past content, your client's specific situation. The more context you feed, the better the output. I keep files that Claude reads before it writes anything. Voice rules. Formatting preferences. Real examples of my writing. It reads all of that before it types a single word. Vinod my co-founder learned this the hard way. He asked Claude to analyze campaign data without telling it what "good" looked like for his clients. The analysis was correct but completely useless. Once he added context about target ROAS, budget limits, and client goals, the output changed completely. He could send it straight to clients. Skill 6: Learn Other AI Tools to Make Your Workflows Better Claude Code is my foundation. But I use other tools to make my workflows stronger. Fireflies records and transcribes every meeting I have. I do not take notes anymore. The transcript is there. Claude pulls it, reads it, finds what matters. For image generation, I use dedicated tools that create ad creatives and blog covers. I give them a JSON prompt with exact specs: lighting, mood, composition, format. Three variants come back. I pick one. For market research, I scrape Reddit with custom tools. Real complaints from real people in real time. Not survey data from 2024. The skill is not knowing every tool. It is knowing which tools plug into your workflow and make the whole thing faster. One tool does the thinking. Other tools feed it data or handle the output. I test a new tool maybe once a month. If it saves me time, it stays. If it adds complexity without saving time, it goes. Most tools go. Skill 7: Use Voice Instead of Typing This one sounds small. It changed my entire day. I use Whisper for voice-to-text. Once you get used to it, you never type a sentence again in your life. Vinod my co-founder debated paying for it for months. Now it is free on most platforms. My daily workflow is largely voice. I dictate to Claude while doing other things. Walking. Cooking. Sitting on the couch. The AI listens, processes, executes. This is not about speed, even though it is faster. It is about thinking differently. Typing makes you edit yourself. Talking makes you think out loud. The ideas come more naturally. I dictate my content ideas. I talk through my strategy. I give Claude instructions by speaking, not typing. It feels like having a conversation with a colleague who never forgets anything. For marketers who create content every day, this skill alone saves an hour. The Real Skill Behind All Seven Every article about AI skills lists "prompt engineering" and "data literacy." Those sound like a university course. The real skill is simpler. It is the willingness to look at how you work, admit what is slow, and build something better. I did not learn AI skills from a course. I learned them from being frustrated with my Monday mornings. Every automation I built started with "I am tired of doing this." 65% of marketing teams now have dedicated AI roles . If you manage a team, read my getting your marketing team to actually adopt AI . The marketers filling those roles will not have the best certificates. They will be the ones who already built workflows that save their team ten hours a week. Start with one task. Break it into steps. Open one tool. Build it. Frequently Asked Questions What AI skills do marketers need in 2026? The most important skill is identifying repetitive tasks in your workflow and automating them. Beyond that, marketers need to give AI proper context and build reusable workflows. The technical side is simpler than people think. You do not need to code. Do marketers need to learn coding for AI? No. I built my entire AI workflow without writing a single line of code. Tools like Claude Code let you write instructions in plain English. The skill is clear thinking, not coding. If you can describe what you want step by step, you can build AI workflows. Will AI replace marketing jobs? AI replaces tasks, not jobs. The marketers who lose their roles are the ones doing only repetitive work that AI handles better. The ones who keep their roles use AI to do that work faster. They spend their time on strategy and creative thinking. How long does it take to learn AI marketing skills? I went from zero to a working automated workflow in one weekend. That covered one specific task. Building a full system took about three months of steady work. Not full-time. An hour here, an hour there. What AI tools should marketers learn first? Pick one large language model and go deep. I use Claude Code. Others prefer ChatGPT or Gemini. The specific tool matters less than the depth you build with it. After your main LLM, add tools that feed it data. Is prompt engineering a real skill for marketers? Yes, but not the way most people teach it. Prompt engineering is not about memorizing magic phrases. It is about giving AI the right context. Feed it your data, your examples, your brand voice, your specific situation. How do I start using AI in my marketing workflow? Write down every task you do in a week. Circle the ones that are repetitive, take more than 30 minutes, and follow the same pattern. Pick one. Break it into small steps. Open your LLM of choice and build it. What is the difference between using AI tools and building AI workflows? Using AI tools means typing prompts one at a time and getting one-off results. Building AI workflows means creating a system that runs automatically. One is a conversation. The other is a machine. The machine saves you hours. Which marketing tasks are easiest to automate with AI? Transcript analysis from client calls. Weekly reporting. Data pulls across platforms. Content drafts. Market research from forums. Start with reporting or transcript analysis. Those follow clear patterns and give you quick wins. How do I future-proof my marketing career with AI? Build workflows that save your team time. Document them. Share them. The marketers who become essential are the ones who make everyone around them faster. Go deep with one system. The skills transfer when new tools come out. --- ## Best AI Tools for Meta Ads in 2026 URL: https://sucana.ai/blog/best-ai-tools-meta-ads-2026 Published: 2026-02-22 Author: Virgil Brewster Tags: ai, meta-ads, media-buying, automation, agencies What Are the Best AI Tools for Meta Ads in 2026? The best AI tools for Meta Ads in 2026 are the ones that plug into your existing workflow. I tested dozens with Victor across real client campaigns. Meta's native Advantage+ handles broad targeting well. For creative and copy, we build custom AI skills that pull from our actual performance data. The honest answer: try them and see what fits. Every agency runs Meta ads differently. What works for a solo media buyer managing three accounts won't work for a team running forty. I can tell you what I tested and what stuck. But the best tool is the one that matches how you already work. Here's what I found after testing the ones that matter. How I ended up testing all of these Victor my co-founder runs Meta ads for clients every day. He's been doing it for almost eight years. When AI tools started flooding the market, he was the first one to try them. His take after six months: "Most of these tools solve a problem I don't have." That stuck with me. I watched him test Madgicx, Revealbot, AdCreative.ai, and a few others. A couple saved time. Most just moved the problem to a different screen. What surprised me was the gap. Every tool automated a task. None helped him understand what was actually happening in his campaigns. That gap is what led us to build Sucana. I wrote about how Claude reads campaign data to show what filling that gap actually looks like. Meta Advantage+ AI: the one you already have Start here. It's free and it's built into Ads Manager . Advantage+ handles targeting, placements, and creative variations automatically. Meta's AI picks who sees your ad, where they see it, and which version performs best. For simple campaign structures, it works. I've seen it cut CPL on broad audiences without any manual targeting. The problem is control. Advantage+ doesn't explain why it makes decisions. It just makes them. Victor ran a campaign where Advantage+ shifted 70% of budget to one placement overnight. Performance dropped. He couldn't see why until he dug into the breakdown manually. For straightforward lead gen with one offer, Advantage+ is solid. For understanding what's happening inside your campaigns, it's a black box. I cover how AI fills that gap across every platform in my AI for performance marketing guide. Madgicx: AI media buying on autopilot Madgicx is the closest thing to a full AI media buyer. It watches your campaigns, shifts budget to what's working, and pulls budget from what's not. The auto budget tool runs in real time. It doesn't wait for your 9am check. If an ad set starts bleeding at 2am, Madgicx catches it. I liked the creative analysis. It breaks down which visual elements perform best across your account. Colors, faces, text overlays, it tracks all of it. Where it fell short for us: Madgicx is great at reacting to data. It's not great at explaining it. You know what it did, but not always why. For agencies managing client money, "the AI moved your budget" isn't a fun conversation without context. Pricing starts around $44 per month. Worth testing if budget rules are your biggest pain. Try Madgicx . AdCreative.ai: creative generation at scale This one is pure creative output. Feed it your brand assets and it generates ad variations. Static images, copy, headlines, all of it. For teams that burn through creatives fast, it saves hours. Victor used it to generate 30 image variations in ten minutes. Normally that's half a day of design work. The catch: the creatives are good enough to test, not good enough to scale. The best performers in Victor's account were still the ones his designer made by hand. Or the ugly ones he threw together in Canva. AI-generated creatives landed in the middle. AdCreative.ai works best as a starting point. Generate a batch, test them, then hand the winners to a designer to polish. Starts around $29 per month. Good value if creative volume is your bottleneck. Try AdCreative.ai . Revealbot: rule-based automation Revealbot isn't really AI. It's automation with rules you set yourself. "If CPA goes above $50 for three hours and frequency hits 2.5, cut budget by 30%." That kind of thing. I prefer this to full AI automation. I decide the rules. I control the logic. Nothing happens that I didn't set up. Victor built about fifteen rules for his biggest client. They caught overspend on weekends, paused underperformers overnight, and scaled winners automatically. Saved him maybe four hours a week. Three rules he runs on every account: First, weekend overspend guard. If daily spend hits 110% of budget before 6pm on Saturday or Sunday, cut ad set budget by 20%. Weekend conversion rates drop for most B2B clients. The platform doesn't know that. This rule does. Second, frequency alarm. If frequency crosses 3.5 on any prospecting ad set, pause it and send a Slack alert. Fatigue happens fast on small audiences. A human still decides what to do, but the rule catches it before damage is done. Third, winner scale. If CPL is below target for 3 days straight and frequency is under 2.0, increase ad set budget by 15%. Let the winners run before the window closes. Those three rules alone eliminate most of the manual checking Victor used to do between Monday and Friday. The downside: setup takes time. It means knowing your campaigns well enough to write the conditions. Not a beginner tool. Starts around $99 per month. Best for experienced media buyers who want control, not autopilot. Try Revealbot . AdAmigo.ai: voice commands for Meta ads AdAmigo is interesting. Talk to it in plain language and it manages your campaigns. "Pause all ad sets with CPA above $60" and it does it. The voice and text command interface feels natural. Less clicking through Ads Manager, more just telling it what to do. I haven't tested this one as deeply as the others. What I've seen looks promising for solo media buyers who want speed. Starts around $99 per month. Try AdAmigo . Marpipe: multivariate creative testing Marpipe lets you test hundreds of creative combinations at once. Upload five headlines, five images, five CTAs, and it builds every possible combination. For agencies spending serious money on creative testing, this is powerful. It finds winning combinations nobody would test manually. The price reflects the audience. Starting at $199 per month, it's built for teams with real ad budgets. Try Marpipe . The gap none of these tools fill Every tool on this list automates a task. Budget rules. Creative generation. Placement decisions. None of them answer the real question: "What is going on in my campaigns and why?" I saw this firsthand. Vinod my co-founder and I were on a call looking at a client's Meta ads data. Neither of us is a media buyer. Victor my co-founder would know what to look for. He's been doing this for eight years. But we opened Sucana's AI chat and asked: "Compare Spain vs. Latin America ad performance. Rank the creatives by CPL." It came back with the full breakdown. Creative ranking, CPL by region, reasoning on why certain ads worked in Spain but not Latin America. Vinod said it on the call: "We are completely non-technical when it comes to Facebook ads. And yet we just ran Victor-level analysis." That's the gap. Tools automate what you already know how to do. They don't help you understand what you're looking at. Which tool to start with If I had to pick a stack for a small agency, I'd go with three layers. Advantage+ for targeting and placements. It's free. It works for broad audiences. Let Meta's AI handle the distribution. Revealbot for budget rules. Set your own conditions. Stay in control. Catch problems at 2am without waking up at 2am. Sucana for understanding your data. Ask questions in plain language. Get answers with real numbers. Stop guessing what's working. Sucana is currently in beta. Join the waitlist at sucana.ai . The rest of the tools on this list are worth trying based on your bottleneck. If creative volume is killing you, test AdCreative.ai. If you're spending enough for multivariate testing, look at Marpipe. But start with the basics. Let Meta handle targeting. Automate your rules. And make sure you can read your own data. That last part is where most agencies fall apart. Once you can read your data, the next step is automating client reporting with AI so the insights reach your clients every week. For a practical look at which AI features inside Meta Ads are worth using — including Meta's own Advantage+ and where it actually fails — see my guide on which AI features in Facebook Ads are actually worth using . I also have a full system for testing Meta Ads creatives with AI if you want to systematize what you test. Frequently Asked Questions What AI tools work best for Meta ads in 2026? It depends on your bottleneck. For targeting, Meta's Advantage+ is free and works for broad audiences. For budget automation, Revealbot gives you rule-based control. For creative generation, AdCreative.ai is the cheapest entry point. Try a few and keep what sticks. Is Meta Advantage+ worth using or does it waste budget? For simple campaigns with one offer and broad targeting, Advantage+ can cut your CPL without manual work. The risk is losing control. Advantage+ makes decisions without explaining them. If it shifts budget to a bad placement overnight, you won't know until you check manually. Can AI replace a media buyer for Facebook ads? Not yet. AI tools can automate budget rules, generate creatives, and handle targeting. But they can't read a client brief or explain why a campaign needs a new angle. Victor my co-founder uses AI tools every day. Saves him hours. But his judgment on what to test next? No tool replaces that. What's the difference between Meta's built-in AI and third-party tools? Meta's AI controls targeting, placements, and creative rotation inside Ads Manager. It's free but opaque. Third-party tools like Revealbot and Madgicx sit on top of Meta's platform. They add custom rules, budget automation, and creative analysis. More control and visibility, but it costs money. How do I test AI ad creative tools without wasting money? Start with a small test budget. Generate a batch of creatives with AdCreative.ai. Run them against your best manual creatives in the same ad set. Give it two weeks and at least $200 in spend per variant. If AI creatives win, scale them. If not, you spent $200 to learn your designer is worth keeping. Do AI tools actually improve ROAS on Meta? Some do, in specific situations. Budget automation tools like Revealbot can catch overspend early and scale winners faster than a human checking once a day. Creative tools have a mixed track record. AI-generated creatives are good for testing but rarely outperform a skilled designer. The ROAS lift comes from volume and speed, not quality. Which AI tools help with Meta ad creative generation? AdCreative.ai generates static images and copy. Creatify and Arcads.ai generate video ads with AI actors. Marpipe tests hundreds of creative combinations at once. For most agencies, AdCreative.ai at $29 per month is the cheapest starting point. How much do AI Meta ads tools cost? Prices range from free to $200+ per month. Advantage+ is free. AdCreative.ai starts at $29. Madgicx starts at $44. Revealbot and AdAmigo start at $99. Marpipe starts at $199. Most tools offer free trials, so test before committing. What are the risks of using AI for Facebook ad automation? The biggest risk is losing visibility. When AI moves budget or pauses ads automatically, you can lose track of why performance changed. For agencies managing client budgets, this creates a trust problem. "The AI did it" is not a client-friendly explanation. Pick tools that log every action and let you set the rules. Should agencies use AI tools or manage Meta ads manually? Both. AI for the repetitive stuff, manual control for strategy. Let AI handle budget rules, placements, and creative generation. Keep human judgment for campaign strategy and client communication. Full manual means falling behind. Full automation means losing control. --- ## AI Ad Copy Generator: How to Write Google Ads Copy With AI URL: https://sucana.ai/blog/how-to-write-google-ads-copy-with-ai Published: 2026-02-20 Author: Virgil Brewster Tags: ai, google-ads, automation, agencies How Do You Use AI to Write Google Ads Copy That Actually Converts? We use AI to write Google Ads copy by feeding it a tone of voice document first. Not a generic prompt. Victor and I built this process after watching AI produce 47 identical headlines. The key is giving AI your client's real language, then testing variants against manual copy with live traffic data. You start with a tone of voice document. Not a prompt. Not "write me 5 headlines." A document that captures who your client is and how they talk. I had to learn this the hard way. Victor and I were sitting in front of his laptop, staring at a Google Ads account with 47 headlines that all sounded like they were written by the same robot. "Grow Your Business Today." "Proven Results for Your Company." "Get Started Now." You know the type. You've seen them a thousand times. You've probably scrolled past every single one. Victor was frustrated. I could see it in his face. He'd been feeding Claude prompts for an hour and nothing felt right. Everything sounded like it could belong to any company in any industry. Then something clicked. He pulled up the client's website, copied two paragraphs of how the founder actually talks, pasted three ads that were already converting, and added a description of who they sell to. Nothing changed except the input. The output was completely different. Headlines mentioned specific pain points. The language matched how the client actually talks. It felt like someone who knew the business wrote them. That was the moment I realized: the problem was never the AI. The problem was always the brief. That lesson applies to every stage of AI in performance marketing , not just ad copy. Step 1: Build the Client Brief This is where most people skip ahead. They want the ads. They don't want to build the brief. But the brief is the entire game. You might recognize the feeling. You open Claude, type "write me Google Ads headlines for my client," and expect magic. I did the same thing. It doesn't work. First thing I do is pull the search terms report from the client's Google Ads account. I grab the last 90 days. I want to see what people actually typed before they clicked. Those words tell me more about the audience than any persona document ever will. No history? I pull competitor ads using the Google Ads Transparency Center . Free tool. Shows you what competitors run right now. Then I build the brief from three pieces: Tone of voice document: How does this client talk? Formal or casual? Industry jargon or plain language? I pull this from their website, their emails, and ideally from a conversation with the founder. Two paragraphs is enough. You're not writing a novel. You're giving the AI a voice to match. ICP description: Who are they selling to? Not "small business owners." That means nothing to an AI. I mean: agency owners doing $50K to $200K per month in ad spend. Drowning in manual reporting. Losing clients because they can't show results fast enough. That level of detail changes everything. Three converting ads: Pull three ads from the account that already work. Best CTR, best conversion rate, whatever metric matters. Paste them into the brief. Tell Claude: "These ads convert. Match this style and tone." Don't copy them. Write new variations that feel the same. That's it. One page. Takes 15 minutes to build. I know what you're thinking. Fifteen minutes? I don't have time for that. Trust me. Those 15 minutes save you hours of rewriting generic output that sounds like every other ad on the internet. Step 2: Pick the Right AI Tool I've tested them all. ChatGPT, Gemini, Claude. Side by side, same ad briefs, same clients. Claude wins. At the time of writing, Claude is the best writer among all major LLMs. It produces copy that sounds more natural. It follows character limits better. It needs fewer edits before the ad is ready to run. ChatGPT tends to over-explain. It gives you headlines that read like mini-paragraphs. Gemini is fast but often misses the emotional hook. Claude gets the constraint. It understands that a Google Ads headline has 30 characters and a description has 90 . It writes within those limits without three reminders. That said, the tool matters less than the input. A great brief in ChatGPT beats a lazy prompt in Claude every time. But if you give both the same brief? Claude wins. I use it for all ad copy work now. Step 3: Write Ads by Awareness Level This one changed everything for me. Victor explained it over coffee one morning. He breaks audiences into five awareness levels. Level 1 has no idea they have a problem. Level 5 is ready to buy right now. Most advertisers write one set of ads and blast them at everyone. That's like shouting "buy my product" at someone who doesn't even know they need it. Imagine someone screaming at you on the street about a solution to a problem you didn't know you had. You'd walk away. That's what most Google Ads do. Here's how I use awareness levels with Claude: Level 1 (unaware): I tell Claude: "This person doesn't know they have a problem. Write a headline that names the pain they feel but can't articulate." The output focuses on symptoms, not solutions. Something like "Still spending Mondays pulling data into spreadsheets?" Level 2 (problem aware): This person knows something is wrong but hasn't searched for a fix. I prompt Claude: "Write a headline that validates the problem and makes them feel seen." Something like "Tired of guessing which campaigns actually drive revenue?" Level 3 (solution aware): The prompt shifts. "This person knows solutions exist but hasn't picked one. Write a headline that positions our tool as the obvious choice." Now the output is sharper. It names the category and differentiates. Level 4 (product aware): This person knows about you but hasn't pulled the trigger. I tell Claude: "Write a headline that handles the last objection. Give them a reason to act now." Something like "Free 14-day trial, no credit card, cancel anytime." Level 5 (most aware): Direct response copy. "This person knows us. They visited the site. Write a headline that closes." Short, direct, action-oriented. Same client. Same AI. Five completely different ad sets. Each one speaks to where the prospect actually is. Not where you wish they were. I ask for 5 headlines per awareness level. That gives me 25 to choose from. I pick the best 2 from each level, adjust a word or two, and end up with 10 strong headlines. For descriptions, I ask Claude to write 6 and pick the best 4. I always write one description myself. Just one. The one that captures the thing only a human who knows this client would say. The AI brings variety. The human brings the soul. Step 4: Edit Before You Upload AI gets you 80% of the way there. The last 20% is yours. I always adjust at least one word per headline. Sometimes I swap a word Claude used for a word the client uses. That small change makes the ad feel real instead of generated. You'd be surprised how much one word matters. Three mistakes I see constantly: Ignoring character limits. Google Ads headlines are 30 characters. Descriptions are 90. If you don't tell Claude this in the prompt, it'll write beautiful copy that doesn't fit. Always include the character limit. Claude is good at this. ChatGPT tends to blow past it. Writing all ads at the same awareness level. If every headline speaks to Level 5 (ready to buy), you're ignoring 80% of your audience. Mix it up. Write ads for people who don't know you yet. And ads for people who visited your pricing page last week. Skipping the human touch. That mix of AI variety and one human-written description consistently outperforms fully human-written RSAs in my tests. Don't let the AI do everything. Write the one line that only you could write. I also use what I call a "voice anchor." One sentence that captures the brand personality. For a construction company: "This brand sounds like a foreman on a job site, not a marketing manager in a meeting." For a SaaS client targeting agencies: "This brand sounds like a founder at a bar telling another founder what worked." That one sentence does more than a full page of brand guidelines. I go deeper on this technique in my AI prompt engineering for PPC guide. Try it. You'll see. Step 5: Test and Refresh I refresh ad copy every 4 to 6 weeks. Not because the AI output gets stale. Because the audience data changes. Every month, the search terms report shows new patterns. New words people use. New problems they mention. That fresh data goes into the brief. The brief goes into Claude. New ads come out. The cycle is simple: pull data, update brief, generate ads, test, repeat. Some agencies refresh quarterly. That's too slow. By the time you update, your competitors already adapted. I wrote about the future of PPC with AI and why this pace is only going to increase. Monthly is the minimum. Biweekly is better if you have the volume. One client, one session, five awareness levels. The whole process takes about 45 minutes. Without AI, this used to take Victor a full day per client. Now imagine doing that for 10 clients. That's the difference. If you want to streamline the reporting side too, I covered automating Google Ads reporting with AI separately. Frequently Asked Questions Can Claude write Google Ads headlines that fit the 30 character limit? Yes. Claude handles character limits better than any other LLM I've tested. Include "stay under 30 characters" in your prompt and it follows the constraint. I still count characters on every headline before upload. But Claude rarely misses. How do you get AI to stop writing generic ad copy? Context. Give the AI a tone of voice document, an ICP description, and three examples of ads that already convert. Generic output comes from generic input. The more specific your brief, the more specific the ads. It takes 15 minutes to build a brief and saves hours of rewriting. Is AI-generated ad copy allowed by Google Ads policies? Yes. Google doesn't prohibit AI-generated ad copy. They care about the content, not how it was written. Your ads still need to follow all standard Google Ads policies . Misleading claims, trademarks, and restricted content rules still apply. What data should you feed AI before writing Google Ads? Three things. Search terms report from the last 90 days. Top converting ads from the account. And a one-page client brief with tone of voice and ICP. The search terms report is the most important. It shows you the actual language your audience uses. Feed that language to Claude and the output matches how real people search. How do you A/B test AI-written ads against human-written ads? I run them in the same ad group. Three AI headlines, two human headlines, mixed into the same responsive search ad. Google rotates them and the data shows which combinations win. After 2 weeks with enough impressions, I pause the lowest performers and generate fresh variations. Does AI ad copy actually convert better than human-written copy? In my experience, AI copy with a good brief converts about the same as strong human copy. The difference is speed. What takes a human a full day takes AI 45 minutes. Where AI wins is variety. You can test 15 headline variations instead of 5. More variations means faster learning. Can AI write ads for industries with strict compliance rules? It can generate drafts. But for healthcare, finance, and legal, you need a human compliance review. I use AI to generate the initial batch, then run every ad through the client's compliance team. Faster than writing from scratch. Still compliant. What is the best free AI tool for Google Ads copy? Claude offers a free tier that handles ad copy well. ChatGPT free works too but the writing quality is lower for short-form copy. For serious ad copy work, Claude Pro is worth it. The difference in output quality pays for itself after one client. I also compiled ChatGPT prompts for PPC managers if you want to compare approaches. How do you handle multiple languages in AI ad copy? I write the brief in English first. Then I tell Claude: "Translate these headlines into Spanish. Keep the tone casual. Stay under 30 characters." For languages I don't speak, I always have a native speaker review. AI translation is good. It's not perfect. Should you use AI for display ad copy or just search ads? Both. Search ads benefit most because of the tight character limits. But display ad copy benefits from the same workflow. Brief first, awareness levels, then generate. The only difference is display allows longer descriptions. Tell Claude the specific character limits and it adjusts. --- ## Google Ads Reporting Tools: How to Automate Reports With AI URL: https://sucana.ai/blog/automate-google-ads-reporting-ai Published: 2026-02-18 Author: Virgil Brewster Tags: ai, google-ads, reporting, automation, ppc How Do You Automate Google Ads Reporting with AI? Pull your campaign data into a CSV. Feed it to an AI model like Claude or ChatGPT. Tell it what metrics matter and who the report is for. The AI reads the numbers, spots the patterns, and writes the summary. What used to take hours now takes minutes. Victor my co-founder runs a PPC agency. He manages Google Ads campaigns for multiple clients. Every week, same thing: export data from Google Ads, open a spreadsheet, paste the numbers, run formulas, write a summary. That was his Monday. Every single week. One day he was showing me a report he built with AI. Beautiful charts, clear written analysis, every metric that matters. I said Looker Studio can do that too. He shot back: "Yeah, but to do this in Looker Studio is really hard. It's pain in the ass." Then he added: "It's not only the graphics. It's also understanding the graphics and giving explanations about what happened." That is the gap. Looker shows numbers. AI explains them. I cover where AI fits across every stage of paid ads in my AI for performance marketing guide. The Real Cost of Manual Google Ads Reporting I watched Victor build reports by hand for months before we built Sucana. The process was always the same. Log into Google Ads, export the CSV, open the spreadsheet, format the columns. Then the actual work started. He had to look at the numbers, figure out what happened, and write it in words the client would understand. A single client report took 30 to 60 minutes. He had multiple clients. That is half a day gone before he could touch a single campaign. I covered the full client reporting automation process in a separate guide. The worst part is not the time. It is the brain drain. Reporting is repetitive, but it still takes focus. By the time he finished, his sharpest hours were spent on data entry. This is one of the core problems I address in my guide on building an AI-powered marketing agency . The Export: Where Every Automated Report Starts The first thing I tell anyone who wants to automate Google Ads reporting: get the data out. Everything starts with a clean export. Inside Google Ads , go to the Campaigns tab. Select the date range. Click the download icon and choose CSV. I keep it simple. The columns I export: campaign name, spend, clicks, conversions, cost per conversion, and conversion rate. If the client cares about ROAS, I add conversion value. That CSV is the raw material. Everything the AI does next depends on what is in that file. One thing I learned: do not export everything Google Ads offers. Dozens of columns make the file noisy. The AI works better with clean, focused data. Five to eight columns is the sweet spot. Feeding the Data to AI Once the CSV is ready, the next step is the AI prompt. This is where most people get it wrong. They paste raw data and ask "write me a report." That gives a generic summary. Not useful. I structure the prompt in three blocks: Context: who is the client, what do they sell, what is the goal Data: the actual CSV content pasted directly into the prompt Instruction: what the report should cover and how it should sound Here is the prompt structure I use: Client: [Name] Industry: [What they sell] Goal: [Lead gen / e-commerce / brand awareness] Budget: [Monthly spend] Reporting period: [Date range] Here is the Google Ads campaign data for this period: [Paste CSV data] Write a performance report that covers: 1. Total spend vs. budget 2. Top performing campaigns by cost per conversion 3. Campaigns that need attention (rising costs or dropping conversions) 4. One clear recommendation for next week Write in plain language. No jargon. Keep it under 500 words. The difference between a bad prompt and this one is context. The AI knows who it is writing for and what matters. I wrote a full breakdown of how to structure AI prompts for PPC if you want the deeper system. Building a Reporting Skill in Claude Co-Work Victor hit a wall after a few weeks of AI reports. Every report came out different. Different structure, different tone, different level of detail. He said: "Every time the report is different. I need a standard prompt that says the report should be like this, this, this. But then I can do custom reports too." That is the exact problem a Claude Co-Work skill solves. A skill is a saved workflow that Claude follows every time you trigger it. No code, no API keys, no third-party tools. Victor also realized each client needs their own context baked in. Not just numbers, but what the client sells, what their main market is, what goals matter. Without that, the AI just chases the cheapest cost per lead. With context, it understands what the numbers actually mean for that client. He showed one of his AI reports to a friend who runs campaigns. The guy got excited. "This is so good." That is what a report looks like when the AI has real context, not just raw data. Here is how to build a reporting skill in five steps. Open Claude Co-Work and start a new chat. Tell Claude what the task is: "I do a weekly Google Ads report for each client. It takes me an hour." Then say: "Can you ask me questions to build this into a skill?" Claude asks things like: what metrics matter, what format the client expects, what tone the summary should use. Answer honestly. The more specific, the better the skill. Once Claude has the full picture, it builds the skill file. The file contains the prompt template, the report structure, and the rules for handling the data. It also has a section for client context: industry, goals, main market, notes. Upload the skill. Click the plus button in Co-Work, select "Upload a skill," and drop in the file. It shows up in your skills list. Every Monday, open Co-Work, trigger the skill, and drop in the fresh CSV. Claude reads the data, follows the skill instructions, and writes the report. Same quality every time, five minutes instead of sixty. What a Good AI Report Actually Looks Like A good AI report does three things. It shows what happened, it explains why it matters, and it tells the client what to do next. I have seen reports that dump every metric into a table and call it done. That is not a report. That is a spreadsheet with a header. The reports Victor sends his clients now start with a one-paragraph summary: spend, leads, cost per lead. Then a breakdown by campaign. Then the insight: which campaign is trending up and which is burning budget. His clients do not ask follow-up questions anymore. The report answers them before they come up. One thing worth noting: the AI is not perfect on the first try. The first report it generates needs editing. By the third or fourth run with the same prompt template, it locks in. The template does the heavy lifting. The Accuracy Question Every agency owner I talk to asks the same thing. Can I trust AI with client data? I had the same concern. So I tested it. Victor ran his campaign data through two different AI reports. Same data, different prompts, different sessions. Both came back with the same conclusion, same patterns, same framing. He said: "It makes consistency with just random data. It keeps making the same conclusion. So that is really good. It is quite solid." The numbers in an AI report come straight from the CSV. The AI does not invent spend figures or conversion counts. What it does is interpret them, and the interpretation is only as good as the prompt and the data. My rule: always scan the final report before sending. The AI gets the math right. Occasionally it misreads a trend or overweights a small data set. A two-minute review catches that. Tools That Work for Google Ads AI Reporting I have tested several setups. Here is what I found. Claude or ChatGPT (manual prompt): Paste the CSV, use the prompt template above. Best for teams with fewer than five clients. No setup cost. Claude Co-Work skill: Build a custom skill that handles the full report. Drop in the CSV, trigger the skill, get the finished report. No API keys, no code. Best for agencies that want consistency without technical setup. Sucana: Reads the campaign data directly and generates the report with AI. No CSV exports, no prompt engineering. Victor uses it for his agency. Full disclosure: I built it. Looker Studio + AI summary layer: Use Looker for the visual dashboard. Layer an AI prompt on top for the written summary. Works, but it is two tools doing what one could do. Each option trades off setup time against ongoing effort. The simpler the setup, the more manual work each week. Reporting is one half of the Google Ads workflow. I cover how to use AI to write the actual ad copy in my guide on writing Google Ads copy with AI . And for a deeper look at how AI reads and interprets campaign numbers, see how Claude reads campaign data like a senior media buyer . Frequently Asked Questions How do I automate Google Ads reporting? Export your campaign data as a CSV from Google Ads. Feed it to an AI model with a structured prompt that includes client context and reporting goals. For a repeatable setup, build a custom skill in Claude Co-Work. The skill saves the prompt template, report structure, and rules. Trigger it each week with fresh data. Can AI write Google Ads reports for clients? Yes. AI can take raw campaign data and write a clear performance summary. It covers spend, conversions, cost per lead, trends, and recommendations. The output needs a quick review before sending. I spend about two minutes scanning each report. The AI handles 95% of the work. What tools automate Google Ads reporting with AI? Claude and ChatGPT handle manual reporting with a prompt template. Claude Co-Work takes it further with custom skills that save the workflow. Dedicated platforms like Sucana offer built-in AI reporting with no prompt engineering needed. The right tool depends on how many clients and how much setup time is available. How long does it take to set up automated Google Ads reporting? The manual prompt approach takes 10 minutes to set up. Save a prompt template, paste fresh data each week. Building a Claude Co-Work skill takes about an hour. After that, each report takes five minutes to trigger and review. Is AI reporting accurate enough for client delivery? The numbers come directly from the CSV export. AI does not invent data points. It reads what is there and summarizes it. The interpretation is where I double-check. A quick scan catches any misread trends. Victor trusts his reports enough to use them in meetings worth millions. Can I automate Google Ads and Meta reporting together? Yes. Export CSVs from both platforms. Include both data sets in the same prompt with labels. Build a Claude Co-Work skill that accepts both data sets. The AI compares performance across channels in a single report. What data should an automated PPC report include? I keep it to the metrics clients actually care about: spend, conversions, cost per conversion, and ROAS. Campaign-level breakdowns show where the budget went. Add a trend line, week over week, and one recommendation. Clients want to know what happened and what to do about it. Everything else is noise. How much time does automated reporting actually save? Manual reporting takes 30 to 60 minutes per client per week. With AI, that drops to 5 minutes for a review. An agency with 10 clients saves 4 to 8 hours every week. Over a month, that is two full working days back. I have seen agencies reinvest that time into actual campaign work. For a bigger picture on where this is all heading, see my take on the future of PPC with AI . Do I need coding skills to automate Google Ads reports? Not for the basic setup. Copy the prompt template, paste your CSV, and run it in Claude or ChatGPT. No code needed. Claude Co-Work skills are built through conversation, not code. Tell Claude what the workflow does and it builds the skill file. The only approach that needs real coding is a custom API integration or Google Ads Scripts . What is the best AI model for PPC reporting? I use Claude for most reporting work. It handles structured data well and writes clean summaries without filler. ChatGPT works too, especially with the data analysis mode. Both models produce solid reports. The difference is in the prompt, not the model. I put together 15 ChatGPT prompts for PPC managers including a reporting prompt template. --- ## Facebook Ads Benchmarks: Average CPL, CPC & ROAS by Industry URL: https://sucana.ai/blog/facebook-ads-benchmarks-2026 Published: 2026-02-18 Author: Virgil Brewster Tags: ai, media-buying, agencies, facebook-ads What Are the Facebook Ads Benchmarks for 2026? Average CTR lands between 1.0% and 1.5%. Average CPC runs $0.94 to $1.72. Average CPM sits between $7 and $14 across most industries. Those numbers are a starting point. What most benchmark articles skip is what to do when you are above or below them. Which phase your campaign is in changes what the number means. That's what this article covers. Why I Never Panic at Bad Numbers A client called me once about a lead gen campaign. CTR was 0.6%. The industry average is 1.11%. They wanted to kill it. I looked at the campaign age first. Three months in. I asked them which phase they thought it was in. Silence. I use the same framework with every account I run. Four phases that every Facebook campaign moves through: honeymoon, scale, saturation, recovery. A 0.6% CTR in the honeymoon phase is normal. The algorithm is still learning. The same number in the scale phase means the creative is dying. In saturation, it means the audience is exhausted. In recovery, it might mean the campaign is rebuilding after a pause. The benchmark number is identical across all four phases. What it means is completely different. We kept the campaign running. Two weeks later the CTR was at 1.3%. Benchmarks tell you where you stand. They don't tell you why. Here are the real numbers for 2026, and how to read them correctly. Facebook Ads Benchmarks by Metric Average Click-Through Rate (CTR) The industry average CTR across all Facebook ad types is 1.11% in 2026. That number hides a wide spread. Retail pushes to 1.59%. B2B drops to 0.78%. Finance sits at 0.56%. Industry Average CTR Retail 1.59% E-commerce 1.44% Health and Wellness 1.35% All Industries 1.11% Real Estate 0.98% B2B 0.78% Education 0.73% Finance 0.56% A CTR below 0.8% is not automatically a problem. In B2B, it is average. In finance, it is above average. Compare against your own industry, not the overall number. Average Cost Per Click (CPC) Average CPC across all industries is $1.72 in 2026. B2B pays the most at $4.50. Retail pays the least at $0.70. The gap exists because B2B audiences are smaller and buying intent is higher. Industry Average CPC B2B $4.50 Finance $3.77 Real Estate $1.81 All Industries $1.72 Health and Wellness $1.32 E-commerce $1.10 Education $1.06 Retail $0.70 If your CPC is above $4 and you are not in B2B or finance, that is worth investigating. A $4.50 CPC in B2B puts you right on average. Average Cost Per Thousand Impressions (CPM) Average CPM is $11.20 for 2026, but this number swings hard by quarter. Q4 (October through December) regularly hits $18 to $25 because of holiday advertiser competition. Q1 drops back to $6 to $9 as budgets reset. Period Average CPM Q1 $6 to $9 Q2 and Q3 $9 to $12 Q4 $18 to $25 Annual average $11.20 Running campaigns through Q4 without adjusting CPM expectations is a common way to miss targets without understanding why. Average Conversion Rate The average Facebook Ads conversion rate across all industries is 9.21% . That figure applies to lead generation campaigns. E-commerce purchase rates run 2% to 4%. B2B SaaS typically sees 5% to 8%. Campaign Type Average CVR Lead generation 9.21% Finance 8 to 12% B2B SaaS 5 to 8% E-commerce purchase 2 to 4% A 2% conversion rate on an e-commerce campaign is not bad. A 2% conversion rate on a lead gen campaign needs attention. These are not interchangeable numbers. Average Cost Per Lead (CPL) Average CPL across industries is $19.68 . B2B leads run $50 to $150. E-commerce leads run $5 to $15. The spread is wide because the value behind each lead is different. A $90 CPL in B2B SaaS can be a great number. The same $90 in retail lead generation means something is wrong. CPL only makes sense relative to what a customer is worth. How to Diagnose a Failing Campaign Before you look at the benchmark, find where in the funnel the problem lives. I use three metrics for any lead gen campaign: CPM, CTR, and landing page conversion rate. Those three tell you everything. CPM is the audience problem: If CPM is high and climbing, the issue is not your creative. The audience is either too narrow, too competitive, or exhausted. CTR is the hook problem: If CPM looks normal but CTR is low, people are seeing the ad and scrolling past. The hook is not stopping them. I look at two signals together: CTR and frequency. If CTR drops from 3% to 0.3% as frequency climbs, that is creative fatigue. The same people are seeing the same ad and tuning it out. If frequency is low and CTR is still poor, the hook never worked. Test new angles before new formats. Landing page CVR is the offer problem: If CPM and CTR are both healthy but leads are not converting, the ad is working. The page is not. Fix what the page promised versus what the ad set up. Fix the first broken step. Then look at the next one. Before you kill any campaign: Give it at least 3x your target CPL in daily budget first. If your target CPL is $20, the campaign needs $60 in daily spend before you have enough data. Spend $20, get zero leads, pull it. You may have just killed a winner that needed $30 to convert. Around 90% of ads fail . That is normal. The 10% that win pay for everything else. What to Do When You Are Below Benchmark The first question is not how to fix your CTR. The first question is which phase the campaign is in. The four phases I run every account through: Honeymoon phase: The first two to four weeks. The algorithm is learning. CTR and conversion rate are unreliable. Do not make creative decisions based on honeymoon phase data. Scale phase: Weeks four through twelve, roughly. This is where benchmarks matter most. If CTR is below 0.8% for a consumer campaign in this phase, the creative is not connecting. Test new angles before adjusting budget. Saturation phase: The campaign has maxed out its core audience. CTR drops, CPM climbs, CPL rises together. The fix is not tweaking. It is new creative or a new audience. Adjusting bids will not save a saturated campaign. I wrote a full guide on how to test Meta Ads creatives with AI if you need a system for this. Recovery phase: After pausing and relaunching. Metrics often look worse before they improve. Week one numbers in recovery are not diagnostic. Give the algorithm time to relearn before drawing conclusions. If your numbers are below benchmark, identify the phase first. The answer is different depending on where you are in the campaign lifecycle. How AI Changes How You Read These Numbers I run campaign data through Claude before making decisions now. There's a deeper breakdown of the full workflow in how AI changes Facebook ad management . The prompt is simple: paste the campaign metrics and the relevant benchmark table. Ask Claude to flag anything meaningfully above or below, and identify which campaign phase you are likely in. What comes back is not guesswork. It is a structured read with specific flags. "Your CPM is 40% above Q1 average for your industry. Either audience is saturating or Q4 spend is bleeding in." That used to take 20 minutes of manual work. Sucana does this automatically across all your campaigns. You see where each campaign stands, which phase it is in, and what the data suggests next. Frequently Asked Questions What is a good CTR for Facebook Ads in 2026? For consumer campaigns, above 1.0% is good. For B2B, above 0.7% is good. The all-industry average is 1.11%. CTR varies significantly by industry. Retail averages 1.59% while finance averages 0.56%. Compare your CTR against your own industry, not the overall average. What is the average CPC for Facebook Ads in 2026? The average CPC for Facebook Ads in 2026 is $1.72 across all industries. B2B campaigns average $4.50 and retail campaigns average $0.70. CPC depends heavily on audience size and purchase intent. Smaller, higher-intent audiences cost more per click. Industry comparison matters more than the overall average. What is a good conversion rate for Facebook Ads? For lead generation campaigns, the average Facebook Ads conversion rate is 9.21%. For e-commerce purchase campaigns, 2% to 4% is typical. The right benchmark depends entirely on what conversion you are measuring. A form fill and a product purchase are not comparable metrics. What is the average CPM for Facebook Ads? The average CPM for Facebook Ads is $11.20 annually. It varies by season. Q4 averages $18 to $25 due to holiday competition. Q1 drops to $6 to $9. Budget for Q4 CPM separately. Running the same assumptions year-round will underdeliver during the holidays. How much does a Facebook Ads lead cost on average? The average cost per lead on Facebook Ads is $19.68 across all industries. B2B leads typically cost $50 to $150. E-commerce leads run $5 to $15. CPL only makes sense relative to the lifetime value of a customer. A $90 lead in a business with a $5,000 average deal size is a strong lead. What are the Facebook Ads benchmarks for B2B? For B2B Facebook Ads in 2026: average CTR is 0.78%, average CPC is $4.50, average CPL is $50 to $150. Conversion rates run 5% to 8% for lead generation. B2B campaigns cost more per click because the audiences are smaller and the buying intent is higher. Do not compare B2B results to general industry averages. What are the Meta Ads benchmarks by industry? Meta Ads benchmarks vary widely by industry. Retail sees the highest CTR at 1.59% and the lowest CPC at $0.70. B2B sees the lowest CTR at 0.78% and the highest CPC at $4.50. Find the benchmark for your specific industry. Track your own trend over time rather than chasing a single published number. How often do Facebook Ads benchmarks change? Facebook Ads benchmarks shift quarterly due to advertiser competition, algorithm updates, and seasonal demand. CPM moves the most, sometimes doubling from Q1 to Q4. Treat benchmarks as directional, not fixed targets. A campaign beating benchmark in Q1 may fall below it in Q4 simply because more advertisers are spending. Why is my Facebook Ads CTR below benchmark? A CTR below benchmark does not automatically mean the campaign is failing. The most important variable is which campaign phase you are in. In the honeymoon phase, CTR is unreliable. In saturation, a dropping CTR means you have exhausted your audience. In the scale phase, a low CTR points to creative that is not connecting. Identify the phase before diagnosing the problem. What Facebook Ads metrics should I track weekly? Track CTR, CPM, CPC, and CPL weekly at minimum. Compare each against the industry benchmark for your vertical and your own 30-day rolling average. The trend matters more than any single week. A CPL that rises 10% week over week for three consecutive weeks is more meaningful than a single spike. How do Facebook Ads benchmarks compare to Google Ads? Facebook Ads typically show lower CTR than Google Search Ads because Facebook users are not actively searching. Google Search averages 4% to 6% CTR versus Facebook's 1.11%. Facebook CPC is generally lower than Google Search because the intent is lower. The two platforms serve different stages of the funnel and should not share the same benchmark targets. If you are running both, see how the process for writing Google Ads copy with AI differs from Meta. Why are my Facebook Ads not delivering? Facebook Ads stop delivering for three reasons. The learning phase was disrupted, the audience is too small, or the bid is too low. Check your campaign status first. If it shows "Learning limited," the ad set did not get enough conversions in the first seven days. Broaden the audience or consolidate ad sets to give Facebook more data to work with. What is a good CPC for Facebook Ads? A good CPC depends entirely on your industry. Retail averages $0.70. B2B averages $4.50. Comparing your CPC against the wrong industry will give you the wrong diagnosis. The number that matters more is cost per lead or cost per acquisition. A high CPC with strong conversion can outperform a low CPC with a weak one. What is the average ROAS for Facebook Ads in 2026? Average ROAS on Facebook Ads runs between 2x and 4x across most industries. E-commerce typically targets 3x to 5x as a baseline. B2B campaigns with longer sales cycles often show lower immediate ROAS. The lifetime value behind each conversion makes up the difference. ROAS benchmarks are the hardest to generalize because they depend entirely on your margins and attribution model. Track ROAS as a directional signal rather than a fixed standard. --- ## AI Prompt Engineering for PPC Managers: a Practical Guide URL: https://sucana.ai/blog/ai-prompt-engineering-ppc Published: 2026-02-17 Author: Virgil Brewster Tags: ai, ppc, google-ads, prompt-engineering, agencies What Makes an AI Prompt Actually Work for Google Ads? An AI prompt works for Google Ads when it includes real campaign data, specific constraints, and a clear output format. I learned this running PPC for clients with Victor. Generic prompts produce generic results. Feed the AI your actual account context and performance numbers first. It comes down to what you feed the AI before you ask the question. Most PPC managers open ChatGPT and type "give me keywords for my campaign." Zero context. Zero useful output. The tool matters too. I tested both extensively and wrote up where Claude beats ChatGPT for marketing work . I watched Victor, my co-founder, do this the wrong way first. He asked Claude to analyze a campaign for Pablo, one of his clients. The AI came back and said: scale Latin America, the CPL is $3. Spain is $9. Move the budget. Sounds smart. It was dead wrong. Victor knows Pablo's customers are in Spain. Latin America clicks are cheap, but they never convert to sales. The AI saw the numbers and missed the business. "If we don't have the context of the client, you don't need to just chase the cheaper cost per lead." Those were Victor's exact words. That one bad recommendation could have burned thousands of euros. The AI did exactly what it was told. The problem was what it was not told. Every article online gives you 40 prompts, 100 prompts, a giant list. None of them teach you the system that makes prompts work. Prompts are one piece of a larger approach to AI for performance marketing . Give the AI your data, your client's business, and your campaign history. Then ask the question. The Prompt Is Not the First Step The first thing I do before writing any prompt is collect three things. The campaign data. Export it from Google Ads or Meta Ads Manager . Spend, clicks, conversions, CPL, ROAS by campaign, ad set, and day. CSV works. The AI reads it faster than a dashboard. The client context. What do they sell? Where do they sell it? Who actually buys? This is the part most people skip. It is the part that matters most. The performance history. Not just last week. The last 30, 60, 90 days. Trends tell a story that a single snapshot never will. Once I have those three things, the prompt writes itself. What a Bad Prompt Looks Like Here is a prompt I see everywhere: "Give me 10 Google Ads headlines for a SaaS product." That prompt gives you 10 headlines that could apply to any SaaS product on earth. They will be generic. They will not convert. The AI has no idea what your product does or who your audience is. It is guessing. What a Good Prompt Looks Like Here is what I actually use: "You are a performance marketing analyst for a lead gen agency. Here is 90 days of campaign data for Pablo's Facebook campaigns. [paste CSV] Pablo sells online fitness coaching in Spain. His customers are 25-40 year old professionals in Madrid and Barcelona. His funnel: Facebook ad to landing page to free consultation call. Current best campaign: 'ES-Broad-Fitness-Feb' at 8.2 euro CPL. Worst: 'LATAM-Lookalike-Jan' at 3.1 euro CPL but zero sales. What are the three biggest budget mistakes? Explain why the cheapest leads are not the best leads." That prompt gives the AI everything it needs. Product, market, funnel, real numbers. The output is night and day. How I Structure Every PPC Prompt I follow the same format every time. Four blocks. In this order. Role: Tell the AI what it is. "You are a senior media buyer managing 200K euro per month in Meta spend." Data: Paste the actual numbers. CSV export, campaign breakdown, date range. Real data, not made-up examples. Context: Client business, target market, funnel, what success looks like. Without this block, the AI chases cheap CPL into dead-end markets. Question: One clear question. Not five. Not "analyze everything." One thing at a time. That is it. Four blocks. Every prompt. Here is what all four blocks look like assembled into one copy-paste-ready prompt: "You are a senior media buyer managing 50K euro per month in Google Ads spend. Here is 90 days of campaign data for [client]. [paste CSV] [Client] sells B2B software to HR teams in Germany and Austria. Their sales cycle is 30 days. One qualified demo request is worth 800 euro in expected revenue. Their best-converting campaign this quarter is 'DE-Brand-Exact' at 18 euro CPL. Their worst is 'AT-Display-Retargeting' at 94 euro CPL with zero closed deals. Which three campaigns should I pause or cut before next week's budget review? Explain the business reason, not just the numbers." That prompt takes 90 seconds to write. The output tells you exactly where the waste is. The Prompts I Use Every Week These are the actual prompts I run for real campaigns. Not theory. Not a list of 100 ideas. Monday: Weekly Performance Review "You are a performance marketing analyst. Here is this week's campaign data for [client]. [paste CSV] Compare this week's CPL to the 4-week rolling average. Flag any campaign where CPL increased more than 20%. For flagged campaigns, tell me what changed: audience fatigue, creative decay, or budget pacing." I run this every Monday morning. It takes 90 seconds. Victor used to spend two hours in spreadsheets. The same system works for writing Google Ads copy with AI , not just analysis. Wednesday: Creative Fatigue Check "Here are the top 5 ad creatives by spend for [client] over 14 days. [paste data] Which creatives show declining CTR week over week? For any creative with CTR dropping more than 15%, suggest a new hook angle that still speaks to [target audience]." Friday: Budget Reallocation "Here is the full campaign breakdown for [client] this week. [paste CSV] Rank campaigns by cost per qualified lead, not just cost per lead. Recommend moving budget from the bottom 2 to the top 2. Show me the math." Month-End: Full Account Audit "You are a performance marketing analyst reviewing a monthly account for a PPC agency. Here is the full account data for [client] for [month]. [paste CSV] The client's goal is qualified leads under 40 euro CPL. Their sales team closes 1 in 8 leads. Summarize performance against goal. Flag the two biggest problems. Recommend the one change that would have the most impact next month. Keep it to three paragraphs." I send this output to Victor before every monthly client call. It takes him 10 minutes to review instead of 90. Why Most Prompt Lists Are Useless I read the top 10 articles ranking for "ChatGPT prompts for PPC" before writing this. I also wrote my own list of ChatGPT prompts for PPC managers with the context layer built in. Every single one of the articles ranking gives you prompts with no data attached. "Write me Google Ads copy for a fitness brand." "Give me negative keywords for an e-commerce store." Those prompts produce the same generic output for every person who types them. A prompt without data is a question without context. The answer will always be generic. The fix is simple. Stop asking the AI to think. Start feeding it data and asking it to analyze. That is the shift from "AI as writer" to "AI as analyst." The Context Layer Most People Skip Victor taught me something I did not expect. He has been running ads for over a decade. He said, and these are his exact words: "I'm above average. I'm not the best, but I'm above many media buyers and I wouldn't see that." He was talking about patterns the AI found in campaign data. Phase transitions, budget pacing problems, landing page conversion drops. All hiding inside the overall numbers. If an experienced media buyer misses these things, imagine a junior buyer prompting AI without context. The output is not just useless. It is confidently wrong. The context layer is the difference. Feed the AI your campaign history and your client's business model. Then ask your question. How to Check AI Output Before Acting on It I never act on AI analysis without checking three things. Does the recommendation match what I know about the client? If the AI says "scale Latin America" for a Spain-only business, the context was missing. Do the numbers add up? I spot-check the math. AI can misread CSV columns or confuse metrics. Would I reach a similar conclusion with more time? If the AI suggests something I would never consider, I dig in before spending money. Trust but verify. Every time. For a deeper look at how Claude actually interprets campaign data behind the scenes, see my breakdown of how Claude reads campaign data like a senior media buyer . And for where all of this is heading in Google and Meta, see my guide on the future of PPC with AI . Frequently Asked Questions What are the best AI prompts for Google Ads? The best prompts start with your campaign data, not a generic question. Paste your CSV export, add client context, then ask one specific question. A prompt like "analyze my top 5 campaigns by CPL trend over 30 days" beats "give me Google Ads tips" every time. Can ChatGPT write Google Ads copy? It can, but only if you feed it the right inputs. Give it your best-performing headlines, your landing page, your target audience, and your key differentiator. Without those inputs, it writes generic copy that sounds like every other ad. How do I use AI to improve my PPC campaigns? Export your campaign data as a CSV. Paste it into ChatGPT or Claude with your client context. Ask specific questions: which campaigns are trending down, where is budget being wasted, which creatives show fatigue. Use AI as an analyst first. Is AI good for Google Ads management? For analysis and pattern spotting, it is excellent. It finds trends in 90 days of data faster than any human. For strategy decisions, it still needs human judgment. I use AI to surface insights. I make the final call. What AI tools work with Google Ads? ChatGPT and Claude both handle campaign data analysis well. Export data from Google Ads, paste it in, and ask questions. No special setup needed. For a full walkthrough, see my guide on automating Google Ads reporting with AI . For automated reporting, tools like Sucana connect directly to your ad accounts and analyze data without manual exports. Do I need to learn prompt engineering for PPC? If you manage ads for clients, yes. The difference between a generic prompt and a data-fed prompt is the difference between useless output and real insight. Feed data first, add context second, ask one question third. That is the whole system. How much time does AI save PPC managers? My Monday reporting used to take two hours per client. With a structured prompt and CSV export, it takes 10 minutes. That is 90% less time on one task alone. I cover the full reporting workflow in my guide on automating client reporting with AI . Creative analysis and budget recommendations add up to 6-8 hours saved per week. Can AI replace a PPC manager? No. AI cannot understand client relationships, business goals, or market context on its own. Victor's Pablo example proves this. The AI recommended the exact wrong budget move because it lacked business context. AI makes a good PPC manager faster. It does not replace the judgment. What is the biggest mistake PPC managers make with AI? Prompting without data. They ask generic questions and get generic answers. The fix: export your campaign data, paste it in, add your client context, and ask a specific question. The output changes completely. How do I get started with AI for Google Ads today? Export one client's campaign data from Google Ads as a CSV. Open ChatGPT or Claude. Paste the data. Add two sentences about the client's business. Ask: "Which campaign should I pause based on the last 14 days?" That takes five minutes and shows you the difference right away. --- ## The Sucana Story: From Spreadsheet to AI Platform URL: https://sucana.ai/blog/the-sucana-story Published: 2026-02-15 Author: Virgil Brewster Tags: agencies, analytics, ai, founder-story I'm Virgil Brewster, a Dutch marketer and consultant living in Bali. We started Sucana with Victor Chazarra and Vinod Sharma in June 2025 because Victor's agency was drowning in data. Every tool he tried made it worse. We built the wrong product first, killed it, and found the real one. This is how it happened. The Three Founders Behind Sucana We're three people with different skill sets in different corners of the world. One shared frustration. I've spent 20 years building and launching businesses. Most of them worked. Some didn't. All of them taught me something. I'm fifty-one years old. One more build and I'll retire. Nah, just kidding. But at some point you have to be aware that you don't have all the time in the world anymore. And that's fine.. Victor Chazarra runs a lead generation PPC agency. He also lives in Bali, two minutes from my place. He manages campaigns across Google Ads and Meta Ads for multiple clients. Handles everything: client calls, campaign strategy, creative production, reporting. That workload is exactly what AI is changing about performance marketing right now. Vinod Sharma is a product builder based in Florida who came back to coding after 12 years in management. Code runs in his blood. We've never seen a problem he couldn't solve. He builds things most people don't even know are possible. The time difference between Bali and Orlando means somebody is always awake, always building. When we go to sleep, Vinod is just getting started. When Vinod wraps up, we're already on calls with Victor over coffee. That's the founding team of Sucana. Two guys in Bali, one in Florida, building across time zones like it's the most normal thing in the world. You can meet the full Sucana team in a separate post. From a First Call to a Lunch That Changed Everything In March 2025, Vinod and I started talking. We had this shared itch to build something together. He was in Orlando, I was in Bali, and the calls started. Just two guys with ideas and a lot of energy. By April, we were knee-deep in building chat functionality. No product yet, no name, just code and late-night calls. Then May 2025 happened. I sat down for lunch with Victor. Nothing formal. Just two guys who live two minutes apart in Bali, eating lunch. Victor started talking about his agency. The two full days every week he lost to exporting numbers that didn't match. He described his Monday and Tuesday routine, and I could see it in his face. This wasn't a minor problem. This was a guy who loved running ads but hated everything that came after. By the time we finished eating, I knew this was the guy. He had the problem. Vinod had the skills to build the solution. And I knew how to get it in front of the right people. And that's how Sucana was born. Victor's Spreadsheet Problem Every Monday and Tuesday, Victor sat in front of his computer doing the same thing. Open Meta Ads Manager. Export the data. Open Google Ads. Export the data. Open Google Sheets. Paste the data. Run the formulas. Fix the broken cells. Cross-reference with the leads sheet. Two full days. Every single week. And the worst part? The spreadsheet broke all the time. He told us once: "It's very easy in a Google Sheet to touch a cell and break everything or have report errors and drag those errors forward." One wrong click. and suddenly, the data your client is paying you to track is wrong. Victor built his own Excel sheet to try to figure it out. Custom formulas. Pre-saved reports in Meta and Google that matched his spreadsheet columns. Webhooks through n8n to capture leads automatically. He did everything right. He engineered his own system from scratch because nothing on the market worked the way he needed it to. When he showed it to his PPC agency friend they went wild. "I would pay for this they said." Bur It still wasn't enough. Meta showed one number. Google showed another. The Stripe account showed something different again. The pixel data was skewed. Nothing matched. And he had to explain all of this to his clients every week. That's not running an agency. That's being a full-time data cruncher. We later wrote about automating client reporting with AI because this pain point kept coming up in every agency conversation. The Wrong Product and the Courage to Kill It Sucana didn't start as a data analytics platform. From June through August 2025, all three of us were building a video ad creator. Vinod coding from Orlando, Victor and I pushing ideas from Bali. The idea was to help agencies produce video ads faster. But not random ads like most tools out there. But based on Victors Lead gen system that had sold $15M for his clients. His concept was to identify the right awareness levels. And scale winning creatives with different hooks and angles. In a nutshell. We had wild plans. An all-in-one agency OS. It sounded great on paper. We were excited. Vinod was building features. Victor was testing workflows. I was writing positioning. We spent the entire summer on it. But something kept nagging at us. Around September 2025, we had our first formal three-way Zoom meeting. And something clicked during that call. The video creator was great, it worked. But it wasn't the thing. Victor's real problem, the one that kept him up at night, the one he complained about over lunch, the one he built his own Excel sheet to solve, was still there. His spreadsheets. His broken reports. The hours disappearing into manual work every single week. Vinod has a cool marble analogy. Building a startup is like pulling marbles out of a jar. You reach in, pull one out, look at it, and say: "Is this the one?" Most of the time, it's not. You put it back. You pull another. And you keep going until you find the one that lights up. The video creator wasn't the magic marble. Until one evening Victor and Vinod decided to build a tool around Vic's spreadsheet. At our next meeting Victor showed me what they build. His face lit up from excitement. Something that I hadn;t seen in a long time in him. So the decision was made. We parked the video and the hooks idea, and went all in on Data analysis, or attribution how the fance PPC guys say it. Three months of work, gone. But for the first time in months we had laser focus. And we went to work. It takes courage to kill something you've been building. But adding features is easy. Removing them is a whole different ball game. And removing an entire product? That's the hardest call you can make. But it was the right one. Here's why… Agencies Are Drowning in Data The problem we set out to solve isn't that agencies lack data. It's the opposite. They have too much of it, spread across too many platforms, and none of it agrees with itself. Victor described it simply: Agencies are drowning in data. His workflow had three different data sources. Spend data from Meta and Google. Lead data from his webhook system. Client data from his CRM. To get one complete picture, cost per lead for example, he had to manually combine all three. Pivot tables for spend. Manually typed lead counts next to each row. Manually added the sales numbers. Human joins. By hand. Every week. And if one cell was wrong, everything downstream was wrong too. That's what most agencies do. Not because they want to. Because the tools don't talk to each other. Victor runs a lead generation PPC agency. That's what he breathes. That's what he knows. When we build Sucana, we build for that reality. For agencies like his, solving the problems he lives every day. And now we share our solution with the world. Building at the Speed of Three After we killed the video creator in October 2025, Vinod built the first data analytics version in about 2 weeks. Coding until midnight his time, which meant we'd wake up in Bali to a wall of updates and new features ready to test. Vinod is the kind of developer who won't sleep until the bug is fixed. When he hit the Meta API and discovered that Meta's "Results" column doesn't actually exist as an API field, he didn't complain. He reverse-engineered the logic. Meta calculates that column client-side in their own interface. It's not exposed in the API. Any analytics tool that wants to show "Results" has to figure out how Meta does it and build the same logic from scratch. Vinod did it overnight. Victor tested every feature by running his actual client campaigns through Sucana. He wasn't gentle about it either. He's always happy to shoot holes in our tool. And trust me he did that hundreds of times. When we built dropdown filters for platforms, campaigns, and conversion types, Victor used them and got confused. The filters created complexity instead of clarity. PPC agencies don't have time to learn dropdown logic. They have campaigns to run. So we killed the filters. We replaced everything with natural language. Instead of clicking through menus, Victor just typed: "Show me October campaigns." The same principle behind building AI workflows for marketing teams : make the interface disappear. Or: "What drove conversions last week?" That's the philosophy. If you need training, it's too complex. Simplicity always wins. By December 2025, we had a working MVP. By January 2026, we were prepping for a first round of beta tester. And by February 2026, ads were live. Real campaigns running through Sucana. Daily team meetings across Bali and Orlando. Beta testers putting the product through hell. The Moment Victor's Face Lit Up We worked on Sucana for months. Multiple iterations. Different directions. We kept asking Victor, "Would you use it?" He would say yes. But never enthusiastic. Then we built the analytics feature. The one where you can talk to your data and it talks back. For the first time, Victor could pull data across all time ranges. Cross-check data with other data. Compare between different clients from one dashboard. His face lit up. His exact words: " This is fucking amazing." That captured everything. For the first time, he could ask his data a real question and get a real answer. Not a dashboard full of numbers. An actual answer. In plain language. "What was my ad spend in May 2025 versus January 2026?" "Which ad copy performed the best?" "How long does it take before someone who saw my ad buys?" The AI doesn't just show numbers. It understands what you're asking and gives you an answer you can act on. I wrote a deep dive on how Claude reads campaign data that explains the logic behind this. In a way that your client would understand if you forwarded it to them. Even if nobody else ever sees Sucana, we already won in that moment. Victor saw his data come alive. And we believe that when we show Sucana to agencies who operate just like Victor, who have the same problems, chances are high that they'll respond the same way. Built by an Agency, for Agencies If you run an agency, you know the feeling. Monday morning, you open your laptop, and instead of running campaigns, you're wrestling with spreadsheets. Pulling data from Meta. Pulling data from Google. Pasting it into a sheet. Fixing broken formulas. Building pivot tables by hand. And then doing it all again for the next client. Your client calls. They want to know what you did last week, what you spent, and what you're doing next. You should be able to answer that in 30 seconds. Instead, it takes two days of manual work just to get the numbers straight. We know because Victor lives that exact life. He runs the agency. He knows what breaks at 2 AM before a client call. He knows which metrics actually matter when you're managing 15 accounts and your spreadsheet just corrupted. He knows that the client doesn't care about CTR or CPM. The client wants clear answers. That's why we built Sucana the way we did. Every feature exists because Victor needed it. The AI chat that understands campaign context. The end-to-end data tracking from ad to landing page. The weekly reporting that answers those three questions your client keeps asking. We don't build features because a product roadmap says so. We build them because the agency on the other side of the table can't operate without them. We built Sucana so you can stop being a data janitor and go back to doing what you're good at. Running campaigns. Growing your clients. Living your life. What 235 Agencies Taught Us We started talking to agencies early. Not surveys. Real conversations. One agency owner at a time. Over the course of building Sucana, we had conversations with 235 agencies. Victor's network in Spain opened the first doors. Then word spread. The conversations revealed something deeper than we expected. Agencies aren't just frustrated with their tools. They're burned out by them. One agency owner told us he buys tools and forgets he even has the subscription. Another said she spent more time setting up her reporting dashboard than actually running campaigns for clients. The agencies doing $3,000 a month in ad spend had the same data problems as the ones doing $3.5 million. The tools didn't talk to each other. The pixel data didn't match reality. The reports took forever. The problem is the same at every level. And agencies talk to each other. They share tools. They warn each other about bad experiences. They move in groups. They don't see each other as competition. That's a beautiful thing about this industry. If the product is great, agencies will tell other agencies. Word of mouth in a tight community is the most powerful marketing channel you can have. That changed our entire distribution strategy. We're not trying to outspend competitors on ads. We're trying to build something so good that the community does the talking for us. Why We Build This in Our Forties and Fifties I'm 51. Vinod is 50 and Victor is in his forties. We're not fresh out of college chasing a quick exit. We've always wanted to build software. For me, I wasn't smart enough before. Everything else felt like work. For Victor, he spent years solving his agency's problems with duct tape and spreadsheets, waiting for a tool that never came. At this stage in life, you realize something. You can't jump on new opportunities for the next 50 years. Life is going to end. That's not a bad thing. That's a good thing. That realization gives you fuel. It strips away the noise and forces you to ask: what do we actually want to build? What do we believe in enough to go all the way? And then AI happened. The tools we couldn't build two years ago, we can build now. Vinod writes code with AI assistance. Our entire SEO pipeline runs on automated skills. I deployed a blog to production, pushed code to GitHub , auto-deployed to Vercel , and I didn't even realize I was committing code. A non-technical 51-year-old founder pushing production code. That's the world we're in. What would have taken a team of 10 engineers and 18 months, three founders can build in weeks. Connected by Zoom and WhatsApp and a shared belief that agencies deserve better tools. Three Founders, No Investors, One Mission Most SaaS founders build from the outside looking in. They spot a market opportunity, raise money, hire a team, and build a product they've never personally used. We built from the inside looking out. Victor is the customer. He doesn't just "understand the pain." He lives it every day. When he tests Sucana, he's not running a QA checklist. He's running his actual business through it. If something breaks, his clients feel it. Vinod doesn't just write code. He builds systems that make the impossible look simple. When Meta's API returned data in a format nobody expected, Vinod didn't file a ticket. He reverse-engineered the solution that same night from his desk in Orlando. And me? I'm the one who says "Let's fucking go" at 7 AM and doesn't stop until the work is done. I spent 20 years consulting for agencies. I know what they need. I know how they talk. I know what makes them trust a product and what makes them walk away. Together, we cover every angle. Product, engineering, distribution. No investors telling us to add features we don't believe in. No board meetings. Just three founders building the thing that Victor's agency, and thousands of agencies like his, actually needs. Agencies lose 96 hours a year on manual reporting alone. The data silos between platforms create blind spots that cost real money. And the reporting gap between what agencies show and what clients need is the number one reason agencies lose clients. We know because we've lived it. And now we're building the fix. The Vision We want people to say that Sucana is life-changing for their business. That it gives them back something more valuable than their own weight in gold. Time. When you have time, you can free your mind. Think about expanding your business. Step out of the day-to-day. Spend more time with your family. Sucana should give you freedom. Freedom of choice. Freedom to do whatever you like while doing what you love the most: running your agency. Not drowning in spreadsheets. Not fighting with broken formulas. Not spending two days every week on reporting that could be done in minutes. We don't reach for the stars. We reach for the heavens. And right now, we're building. Every day. Victor tests real client campaigns through Sucana from Bali. Vinod pushes code from Orlando. I'm doing what I do best: getting this in front of the agencies who need it. If you're an agency owner who's tired of drowning, help is on the way. Not from a marketer or a business developer. From agency owners who solved their own problem and now want to share it with the rest of the community. Frequently Asked Questions Who founded Sucana? We are three co-founders: Virgil Brewster, Victor Chazarra, and Vinod Sharma. We started Sucana in 2025. Virgil and Victor are based in Bali, Indonesia. Vinod is based in Orlando, Florida. Virgil brings 20 years of marketing and business building experience. Victor runs a lead generation PPC agency and provides real-world agency expertise. Vinod is the developer who builds the platform. What does Sucana do? Sucana is an ad intelligence platform for lead generation and e-commerce agencies. We connect ad platform data from Google Ads and Meta Ads into one dashboard. We track performance from the ad to the landing page. Agency owners can ask questions about their data in plain language. The AI responds with clear answers you can act on. How was Sucana created? We started in June 2025 when all three founders began building together. Virgil and Vinod had been talking since March. Victor joined after a lunch meeting in Bali in May. From June to August 2025, we built a video ad creator. We pivoted in September when we realized Victor's real pain was data fragmentation, not video production. From October to December 2025, we built the data analytics MVP. By early 2026, Sucana was live with real client campaigns running through it. What problem does Sucana solve for agencies? Agencies waste hours every week on manual reporting because their ad data is scattered across platforms. Spreadsheets break. Numbers don't match. Reports take forever to build. We solve this by pulling all data into one place, keeping it accurate end-to-end, and generating reports in minutes instead of days. Is Sucana only for lead generation agencies? No. Sucana serves both lead generation and e-commerce agencies. Victor runs a lead gen agency, so that's where we started. But the data problem is the same across both worlds. We built Sucana for the workflows, metrics, and challenges that agencies face daily, regardless of whether they run lead gen or e-commerce campaigns. How is Sucana different from Hyros or Ruler Analytics? We built Sucana as an ad agency for ad agencies. Tools like Hyros and Ruler Analytics serve a broader marketing audience. We focus on simplicity. Flat pricing without success fees. Fast support response in 6 to 8 minutes. An AI that understands agency-specific questions. If you need to know cost per lead across multiple clients from one dashboard, Sucana does that without a complex setup process. Where is Sucana based? Sucana operates across two time zones. Virgil Brewster and Victor Chazarra are based in Bali, Indonesia. Vinod Sharma is based in Orlando, Florida, United States. The team works asynchronously. When Bali sleeps, Orlando builds. When Orlando wraps up, Bali is already testing and shipping. This setup means someone is always working on Sucana. When was Sucana founded? Sucana was founded in 2025. The first conversations between Virgil and Vinod started in March 2025. Victor joined after a lunch meeting in Bali in May 2025. The team started building together in June 2025. After a pivot in September 2025, the data analytics MVP was built from October to December 2025. Beta testing began in January 2026 and ads went live in February 2026. Is Sucana bootstrapped? Yes. Sucana is fully bootstrapped with no outside investors. Three founders fund and build the company themselves. There are no investors telling the team to add features they don't believe in. No board meetings. No fundraising rounds. Every decision is made by the people who build and use the product daily. What ad platforms does Sucana support? Sucana currently connects to Google Ads and Meta Ads (Facebook and Instagram). These are the two dominant ad platforms used by lead generation and e-commerce agencies worldwide. The platform pulls spend data, campaign performance, and conversion metrics from both platforms into a single dashboard. Additional platform integrations are on the roadmap. --- ## How to Build an AI-Powered Marketing Agency URL: https://sucana.ai/blog/how-to-build-ai-powered-marketing-agency Published: 2026-02-13 Author: Virgil Brewster Tags: ai, agencies, automation How Do You Build an AI-Powered Marketing Agency? You start by automating the work you already do every day, not by buying tools and hoping they connect. The agencies winning right now are not selling AI services to clients. They are using AI to run their own operations. Reporting, client updates, campaign analysis. Pocketing the margin they used to spend on headcount. That is the difference between an AI-powered agency and an agency that talks about AI. On a Tuesday morning a few months ago, I dropped one file into Claude and walked away to make coffee. When I came back, it had pulled my Fireflies transcript from the night before. Updated my roadmap. Created a to-do list. Scraped industry news. Drafted three LinkedIn posts in my voice. I am not a coder. I built that workflow by describing it to an AI in plain English. That morning changed how I think about running a marketing agency. What AI Agency Automation Actually Means Most articles about building an AI marketing agency focus on how to sell AI services to clients. That is not what this is about. AI agency automation means using AI to run the internal operations of your own agency. The work nobody charges for but everyone does. Weekly reports, campaign summaries, client update emails. When you automate that work, two things happen. You get those hours back. And your margin goes up without raising your prices. That is how a small team competes with a 50-person agency. Not by hiring faster. By building smarter. Where Do You Start With Agency Automation? I start with the task my team dreads most that follows the same pattern every week. For us, that was client reporting. Victor used to spend two full days pulling numbers from Meta, Google, and spreadsheets. Same format, same process, every single week. That is the perfect first automation target. The first thing I look for is a task that happens every week and follows the same pattern every time. It has to take more than an hour. And it has to be something my team dreads doing. For most agencies, that task is client reporting. Victor my co-founder used to spend the first two days of every week pulling numbers. Meta, Google, spreadsheets. One report per client. Same format. Every week. I wrote a step-by-step guide on automating client reporting with AI that covers the full process. That is your starting point. Not the creative work. Not the strategy calls. The reporting. Start there because it is predictable enough for AI to handle. And painful enough that your team actually wants it solved. The tasks to automate first: Weekly client reports: Same structure every time. Pull data, format, send. AI handles all three steps. Campaign performance summaries: Take raw numbers and write a plain-English explanation. What went up, what went down, what to do next week. Client update emails: Same questions every week. AI drafts the response in your voice using real campaign data. Onboarding documents: Every new client gets the same pack. Automate the generation, personalize the details. Stay away from creative strategy and positioning for now. AI needs a pattern. If you cannot describe the task in one paragraph, it is not ready to automate. Once your workflows are stable, turn them into AI SOPs for your agency so any team member can run them. If you want to go further, I documented how I built an AI brain for my agency using Claude Code — a full knowledge base and skill system that runs operations end to end. What Is the Right AI Agency Tech Stack? The right AI agency tech stack is whatever works now for your team and clients. At Sucana, I run Fireflies for meeting capture, Claude for AI reasoning and drafting, Claude Code and Make.com for automation, and Sucana for campaign data. The tools matter less than choosing what to automate first. There is no perfect AI tech stack for agencies. There is only what works now, for your team, with your clients. Here is what I run at Sucana. For meeting capture: Fireflies. Every call gets transcribed automatically. I have not taken a manual note in months. For the AI brain: Claude. I use it for drafting emails, building automation workflows , and analyzing campaign data. Most of what I built, I built by describing what I wanted in plain English. For connecting tools: Claude Code for complex workflows. Make.com for simpler ones. When a task maps cleanly in a flowchart, Make.com handles it. When it needs judgment mid-flow, Claude Code does. For campaign data: Sucana pulls performance data from Meta and Google automatically. Victor my co-founder gets a full analysis every morning without touching a spreadsheet. For a full comparison of what is available, see my review of AI marketing automation tools . The tech stack is not the hard part. The hard part is choosing what to automate first. How to Set Up AI Automation for Your Agency This is the process I follow every time I add a new automation at Sucana. For the full breakdown of costs, risks, and which task to pick first, see my guide on AI automation for marketing agencies . First, I write the task down in plain English. Not in any tool. Just a notes document. What happens first, what data it needs, what the output looks like, who receives it. Then I open Claude and paste that description in. I tell it what I am trying to build. It asks me questions. I answer them. That conversation becomes the working spec. I do not need to know how to code. I just need to know what I want. Then I build the simplest version that works. Not the perfect version. The one that does 80% of the job this week. I run it once manually and check the output. If it looks right, I let it run two more times. If it holds, it becomes part of the standard workflow. Why Most Agency Automation Projects Fail The marketing automation failure rate is high. Around 73% of projects break down . The cause is almost always the same. Teams build the automation and walk away. The workflow runs fine on launch day. Then an API updates. A data format shifts. A media buyer uses "RT" instead of "retargeting" in a campaign name. The filter breaks. Nobody notices until a client asks a question that does not match the numbers. I review every automation once a month. Not deeply. Fifteen minutes per workflow. I check that outputs still make sense and data is coming through clean. That one habit is the difference between automation that compounds and automation that quietly breaks. How to Scale a Marketing Agency Without Hiring The question I get most from other agency founders: how do you take on more clients without burning out your team? The answer is not a better project management tool. The real bottleneck is time spent on tasks that do not require human judgment. When I automated client reporting at Sucana, I did not cut headcount. I redirected the time. The research on whether AI is replacing marketers shows most agencies are doing the same. The hours that used to go into pulling numbers went into improving creative and talking to clients. Victor my co-founder now manages more client accounts than he did twelve months ago. We did not hire more media buyers. We automated the administrative layer of his week. That is how you scale a marketing agency without hiring. Automate the repeatable layer. Free your team for the judgment layer. When you are ready to sell that capacity to new clients, I wrote a guide on pitching AI services to agency clients that covers the sales process. What to Automate Last There is a version of this where you over-automate and it costs you clients. I know agency owners who automated their entire client communication flow. Every email, every check-in, every update. Clients noticed. They felt like they were talking to a system, not a partner. Two of them churned. The rule I follow: automate the backend, keep the relationship human. Automated reports are fine. Automated strategy calls are not. Automated data summaries are fine. Automated responses to a client panic are not. The client pays for your judgment. They pay for the call where you explain what the numbers mean and what you are doing about it. No automation replaces that conversation. Keep humans in the moments that matter. Let the machine handle everything else. Frequently Asked Questions What is AI agency automation? AI agency automation means using AI tools to handle the repeatable, data-driven tasks inside a marketing agency. Things like generating client reports, writing campaign summaries, and drafting update emails. The goal is to free up your human team for judgment work, not just time-consuming work. How much can AI reduce marketing agency costs? It depends on what you automate. At Sucana, automating client reporting freed up roughly 6 to 8 hours per week per account. Two hours per client per week, 10 clients: that is 20 hours saved weekly. You can take on 2 to 3 more clients with the same team. Can a small agency compete with large AI-powered agencies? Yes. AI levels the ops side of the business. A two-person team with automated reporting can move as fast as a 10-person team without it. The margin is better too. What are the biggest mistakes agencies make with marketing automation? Set-and-forget is the most common mistake. Build the workflow, walk away, watch it quietly break. Nobody notices. The second mistake is automating too much too fast. Start with one workflow, get it solid, then add the next. Agencies that try to automate everything at once end up with five broken workflows instead of one working one. Why do marketing automation projects fail? The 73% failure rate mostly comes from two places: bad data and no monitoring. If your campaign data is messy or inconsistently named, the automation produces wrong outputs. And if nobody checks the workflow regularly, errors compound for weeks. Fix the data first. Then build the automation. Then build the monitoring habit. What AI tools do top marketing agencies actually use? The tools I see most: Claude for AI reasoning, Make.com or Zapier for automation, and Fireflies for meeting transcription. The combination matters more than any individual tool. You need three things: input capture, AI processing, and output delivery to the right place. What is the difference between AI agents and traditional marketing automation? Traditional automation follows a rigid script. If A happens, do B. No flexibility. AI agents handle variability. They can read a campaign summary, find the key insight, and write a client email that addresses it. A standard Zapier flow cannot do that. The more judgment a task requires, the more you need an AI agent. How do you train an agency team to actually use AI tools? The fastest way is to build one working workflow and let them use it for two weeks. I never start with training. I start with a result. I cover the full process in my marketing team AI adoption strategy . Once Victor my co-founder saw the report running automatically, he started asking what else we could automate. How do you automate without losing the client relationship? You automate the data and the formatting. You keep the human on the conversation. Automated reports are fine. The email where you explain what the numbers mean should still come from you. Clients do not pay agencies for data. They pay for someone who knows what to do with it. How do you measure ROI on marketing automation for agencies? I track two things: time saved per week and revenue per team member. Time saved is easy to measure. Revenue per team member is the better long-term signal. If revenue grows and headcount stays flat, the automation is working. Do not try to calculate precise ROI in month one. Run the automation for 90 days, then look at what changed. Is AI agency automation only for large agencies? No. Small agencies benefit more, not less. A large agency absorbs inefficiency with headcount. A small agency cannot. Automate reporting, and it changes the economics of your whole business. Even with a team of two. What should I automate first as a new agency owner? Client reporting, without question. It is the most predictable task in the agency business. Same data, same format, same output, every week. Once that runs automatically, add campaign performance summaries. Then client update emails. Build one at a time, in that order. You will have a working AI operations layer within 60 days. --- ## AI Reporting Tools: Automate Client Reporting URL: https://sucana.ai/blog/automate-client-reporting-ai Published: 2026-02-11 Author: Virgil Brewster Tags: ai, automation, agencies, client-reporting How Do You Automate Client Reporting with AI? You connect your ad data to an AI tool. You define what the report should show. It generates the output on a schedule. The hard part is the setup. Once it runs, Monday morning stops being a scramble and starts being a five-minute check. Victor my co-founder manages multiple client accounts. His agency runs on performance data. Every week, before he could do anything useful, he pulled numbers from Google Ads, Meta, and other platforms. He pasted everything into a spreadsheet, formatted it, and wrote a summary by hand. That was his Monday and Tuesday. Every single week. The first time he ran the automated report in Sucana, it was March 1st. He had a client meeting that afternoon. Not a small one. A 7 million euro per year client. He pulled the AI-generated report, the same kind of AI-powered performance marketing analysis we built Sucana around, scanned it, and walked into that meeting using it. He did not tell me until afterwards. "I could have such a confidence in the data. I gave much better image to the client." That is the point of automating client reports. Not to save time as a concept. To actually walk into your most important meeting with clean, accurate data you trust. Why Client Reporting Eats So Much Time The problem is not that reporting is hard. It is that it is repetitive. Every week, the same steps. Pull data, format it, write the numbers in sentences, send. The AI does not get bored with repetitive tasks. You do. When an agency owner does this by hand, they are spending cognitive energy on something that has no ceiling. There is no version of "spending 10 hours building a report" that makes the report 10 times better. The client does not care how long it took. They care if it is accurate and clear. I looked at how our beta testers were spending their time when they first came to Sucana. Reporting was not one of the tasks on the list. It was the thing underneath every task on the list. Pull the data first. Then do your actual job. What You Need Before You Automate Anything Before any AI tool touches your reports, three things need to be true. Your data needs to be in one place If you are pulling from five platforms and stitching it together manually, no AI can fix that. The automation starts where the data is clean. Know what a good report looks like. AI generates what you describe. If you cannot describe what a good client report contains, the AI will write a mediocre one. Before building any automation, write down what goes in the report. Which campaigns, which metrics, what timeframe, what you flag for the client. Pick one client to start with. Not all clients. One. The one where you know the data cold, where a clean automated report would genuinely save you time. If you want to build the full system around this, read how I built an AI brain for my agency using Claude Code . Start small. One client, one report. When it runs clean, copy the system to the next client. Step 1: Pick the Report You Run Most Often The right report to automate first is not the most complex one. It is the most frequent one. For most agencies, that is the weekly performance summary. Seven days of spend, impressions, clicks, CPL, and a short write-up on what happened. That report probably takes you 45 minutes every time. Multiply that by your client count. You are looking at a full day writing the same structure over and over. That is your target. The first thing I tell people is to write out what you actually include in that report. Every section. Every metric. Do this manually once, in detail, because that document becomes the instruction set for your AI. Step 2: Connect Your Data to an AI Tool There are a few ways to do this depending on your setup. If you are already using a reporting platform like Looker Studio or AgencyAnalytics , feed your data exports into an AI. It reads the numbers and writes the summary automatically. If you are working directly out of Google Ads and Meta, use Make.com or n8n . For Google Ads specifically, I wrote a step-by-step guide to automating Google Ads reporting with AI . They pull the data on a schedule, format it, and pass it to an AI prompt that writes the report. If you are using Sucana, the data connection is built in. Victor logs in, selects the client, and the AI reads the campaign data directly. He does not export anything. He does not paste anything. He asks the question. The report comes back. The tool matters less than the data being clean. If the numbers going in are wrong, the AI report will be confidently wrong. Garbage in, garbage out. That is the one rule of automated reporting that does not change no matter which tool you use. Step 3: Write the AI Prompt That Generates the Report The prompt is where most agencies cut corners. You cannot just ask an AI to write a client report. Describe the client, the campaigns, the metrics that matter, and what the client wants to see. A good reporting prompt looks something like this: You are writing a weekly performance report for [client name]. They run lead gen campaigns on Google Ads and Meta. The reporting period is [dates]. Here is the data: [paste data]. Write a summary covering total spend, CPL trend, which campaigns performed, which did not, and one recommendation for next week. Write in plain language. No jargon. Keep it under 300 words. That prompt gives the AI context, structure, data, and tone. When you run it every week with fresh data, you get a consistent report in the same format every time. Save that prompt. Give it a name. Keep a version for each client with their specific context baked in. Step 4: Review Before You Send I want to be direct about this: automated does not mean unreviewed. The AI is reading numbers and generating language. It is fast, it is consistent, and it is usually accurate. For a look at how this actually works, see how Claude reads campaign data . But it does not know about the CPL spike last Tuesday. It does not know you paused the wrong ad set. It does not know the client changed their budget mid-week. Read the report. Every time. It should take you five minutes instead of 45. That is the win. The AI writes the draft, you check it, you add the two sentences only you know, and you send. Victor's workflow with the March 1 meeting was exactly that. The AI pulled the report, he reviewed it, he trusted the data, he walked in confident. That is the goal. Not zero human involvement. Less human time on the wrong things. What to Do When the Report Is Wrong You will run into this. An AI report that gets something backward, or flags the wrong campaign, or uses language the client will not understand. When that happens, do not throw out the automation. Fix the prompt. Nine times out of ten, the error is in the instructions, not the AI. Go back to your prompt and add more context. If it described a paused campaign as a success because CPL went down, fix the prompt. Add: "If a campaign has zero spend, note it was paused. Exclude it from performance comparisons." Prompt engineering for reporting is just specificity. The more precise the instructions, the more reliable the output. Treat every wrong report as a bug report on your prompt. I wrote a practical guide to AI prompt engineering for PPC that covers the full system. Frequently Asked Questions What tools can automate client reporting with AI? The tools that work best connect directly to your ad platforms. No manual exports needed. Sucana reads campaign data and generates AI reports automatically. Make.com and n8n pull data from Google Ads, Meta, and other platforms. They pass it to an AI model that writes the report. AgencyAnalytics handles multi-channel reporting with some AI write-up features built in. The right tool depends on your data sources and how technical your team is comfortable getting. How long does it take to set up automated client reporting? For a single client and one report type, the initial setup is typically two to four hours. That covers connecting your data source, writing the AI prompt, testing it, and running a few trial reports. Once the first client is working, each additional client takes less time because the prompt structure is already built. Is AI-generated reporting accurate enough to send to clients? Yes, if your underlying data is clean and your prompt is specific. The AI reads the numbers you give it and writes what you describe. The part that fails is usually the input, not the AI. Messy data, vague prompts, or missing context produce inaccurate reports. Clean data and a detailed prompt produce reliable ones. Always review before sending. The review should take minutes, not hours. What metrics should a weekly client report include? Every report should include total spend, cost per lead, which campaigns ran, and how performance compared to the prior week. Add a short paragraph on what happened. Optional but valuable: a recommendation for the following week. This is where AI write-ups add the most value. The AI spots trends you'd miss doing it by hand. Can AI write the summary section of a client report? Yes, and this is where it saves the most time. The summary section is the hardest to write. The AI drafts it for you. Feed it the data and a few sentences of context about the client's goals. It produces a clear, plain-language write-up you can edit in seconds rather than write from scratch. Do I need to be technical to automate client reporting? Not anymore. Tools like Sucana are built so that non-technical users can run AI reports without connecting APIs or writing code. If you want to build something custom using Make.com or n8n, some technical comfort helps. But the no-code versions of these tools have improved significantly. Most agency owners can build a basic reporting automation in an afternoon without a developer. How do I handle different reporting formats for different clients? Keep a separate prompt for each client. The prompt includes the client's name, their campaigns, the metrics they care about, and any language preferences. This sounds like more work than it is. After the first two or three, you will have a template. Five minutes to adapt it for each new client. What happens if my ad platform data is wrong? The AI report will reflect that error. It has no way to know if the numbers are correct. It only sees what you give it. This is why data validation matters before you automate. Victor's first reaction to Sucana's AI report was to check the CPL numbers manually. They matched. That cross-check is how you build confidence in the automation. Can automated reports replace monthly strategy calls with clients? The reports make those calls better, they do not replace them. A clean automated weekly report means you walk into the monthly call with real data. Not a spreadsheet you assembled the night before. Clients ask sharper questions when the data is clear. That makes the call more useful, not redundant. For the bigger picture on scaling this across an agency, see my guide on building an AI-powered marketing agency . How do I know when to add a new metric to the automated report? Add it when you find yourself manually adding it to reports before sending. If you are adding the same extra sentence every week, update the prompt. That metric belongs in the template. The automated report is a living document. Refine it as your client relationships evolve. What is the difference between automating reporting and just using a dashboard? A dashboard shows numbers in real time. An automated report takes those numbers and turns them into plain language. A client can read it without knowing what they are looking at. Dashboards are for you. Automated reports are for clients. Many agencies use both. The dashboard runs continuously. The AI report drops into the client's inbox every Monday morning. Should I tell clients the report is AI-generated? That depends on your client relationship. The report is accurate and reviewed by you before it goes out. It represents your work no matter how it was generated. What matters is that the data is right and the write-up is useful. Clients care about whether the report tells them what they need to know. Most do not ask how it was written. --- ## Facebook Ads Not Converting? Here's What to Check (In Order) URL: https://sucana.ai/blog/facebook-ads-cpl-spiked Published: 2026-02-10 Author: Virgil Brewster Tags: facebook-ads, cpl, troubleshooting, meta-ads, cost-per-lead When your Facebook Ads CPL spikes, check these three things first: ad frequency above 3.0 (creative fatigue), audience saturation (too narrow targeting), and recent campaign edits that reset the learning phase. These three causes account for the majority of sudden CPL increases on Meta Ads. If none of those are the issue, work through the full 12-step checklist below. A CPL spike does not mean your campaigns are broken. It means something changed — in your account, in the auction, or in the market. The job is figuring out which one. I wrote a full guide on AI for performance marketing that covers how AI helps diagnose these problems across every platform. The 12-Step CPL Troubleshooting Checklist Work through these in order. The most common causes are at the top. Step 1: Check Ad Frequency Open Ads Manager . Add the Frequency column if it is not visible. Look at ad set level. Frequency above 3.0 on prospecting: Creative fatigue is likely. Your audience has seen the ad too many times and stopped engaging. Frequency above 5.0 on retargeting: Expected, but if CPL is spiking, you need fresh creative. Fix: Duplicate the ad set with new creative. Do not edit the existing ad, that resets the learning phase. I wrote a full guide on how to test Meta Ads creatives with AI to build a system for this. Step 2: Check the Learning Phase Look for "Learning" or "Learning Limited" status on your ad sets. "Learning": The algorithm is still optimizing. CPL fluctuations are normal. Wait for 50 conversions before judging. "Learning Limited": You are not getting enough conversions for the algorithm to optimize. This directly causes higher CPL. Fix: If learning limited, consolidate ad sets to concentrate budget and conversions. Reduce the number of active ad sets so each one gets enough volume. Step 3: Check for Recent Edits Any significant edit to an ad set resets the learning phase: Budget changes over 20% Audience changes Bid strategy changes New creative added to existing ad sets Placement changes Fix: If you made edits in the last 3-5 days, the spike may be temporary. Give it 50 conversions before reacting. Step 4: Check Audience Overlap Go to Audiences → select your active audiences → click "Show Audience Overlap." If your ad sets target overlapping audiences, you are bidding against yourself in the auction. This drives up costs without increasing reach. Fix: Exclude audiences from each other, or consolidate into fewer ad sets with broader targeting. Step 5: Check Your CTR Pull CTR (link click-through rate, not all clicks) for the last 7 days vs. the previous 7 days. CTR dropped while frequency rose: Creative fatigue confirmed. CTR dropped while frequency stayed the same: Your creative is underperforming for a new reason — possibly audience shift or market change. CTR stayed the same but CPL rose: The problem is downstream (landing page, form, or conversion tracking). Step 6: Check Landing Page Conversion Rate If CTR is stable but CPL increased, your landing page is leaking. Common causes: Page load speed increased (check Google PageSpeed Insights ) Form broke or changed Mobile experience degraded Offer became less compelling relative to competitors Fix: Test your landing page on mobile. Fill out the form yourself. Check that the thank-you page and tracking pixel fire correctly. Step 7: Check Conversion Tracking Go to Events Manager . Check that your pixel or Conversions API is firing correctly. Delayed events: If conversions are reporting late, your CPL appears higher than it actually is. Dropped events: If the pixel stopped firing on some conversions, your reported CPL is artificially inflated. Duplicate events: If events fire twice, your CPL appears artificially low — then corrects upward when you reconcile with CRM data. Fix: Use the Meta Pixel Helper Chrome extension to verify events fire correctly on your conversion page. Step 8: Check Auction Competition CPL can spike because competitors increased their budgets — not because anything changed in your account. Signs of increased competition: CPM (cost per 1,000 impressions) increased while your CTR stayed the same This typically happens during seasonal peaks: Q4 holiday season, back-to-school, industry events, end-of-quarter budget pushes Fix: You cannot control competitor behavior. If CPMs rose across the board, either increase your budget to maintain volume, improve creative to beat the higher auction floor, or wait for competition to subside. Step 9: Check Audience Size If your audience pool is shrinking, CPL rises because there are fewer people to convert. This happens when: Meta removed targeting options you relied on Your exclusion lists grew too large Lookalike audiences narrowed after a data source changed Fix: Broaden targeting. In 2026, Advantage+ audience is often more efficient than manual targeting because Meta's algorithm has more room to find converters. Step 10: Check Budget Pacing If your daily budget is too low for your audience size, the algorithm cannot optimize delivery efficiently. Meta recommends your budget supports at least 50 conversions per week per ad set to exit the learning phase. If your CPL is $20, that means you need at least $1,000/week per ad set ($143/day). Fix: Either increase the budget or consolidate ad sets. Step 11: Check Placement Performance Break down results by placement (Facebook Feed, Instagram Stories, Reels, Audience Network, etc.). Sometimes one placement degrades while others stay stable. If Audience Network CPL doubled while Facebook Feed stayed flat, that single placement is dragging up your average. Fix: Use placement-level bid adjustments or exclude underperforming placements. Step 12: Check External Factors If nothing in your account explains the spike: Market shift: Did a competitor launch a major promotion? News cycle: Did something happen in your client's industry that changed demand? Seasonality: Are you in a historically expensive period? Platform changes: Did Meta update its algorithm or auction mechanics? Check Meta's Business Help Center for any announced changes. Quick Reference: CPL Spike Diagnostic Table Symptom Likely Cause First Fix CPL up + Frequency up + CTR down Creative fatigue New creative in new ad set CPL up + "Learning Limited" status Not enough conversions Consolidate ad sets CPL up + CTR stable + landing page conversion down Landing page issue Test mobile experience and form CPL up + CPM up + CTR stable Increased auction competition Improve creative or wait it out CPL up after recent edits Learning phase reset Wait 50 conversions CPL up + small audience size Audience saturation Broaden targeting How to Use AI to Diagnose a CPL Spike I built a habit of pasting campaign data into Claude before touching anything in Ads Manager. The output is not a solution. It is a ranked list of suspects with questions attached. Here is the prompt I use: My Facebook Ads CPL increased from [X] to [Y] over the past 7 days. Here is my account data: Campaign name: [name] Daily budget: [amount] Audience size: [estimated reach] Current frequency: [frequency] CTR (link clicks): [7-day] vs [previous 7-day] CPM: [7-day] vs [previous 7-day] Recent edits: [any changes in the last 14 days] Conversion tracking: [pixel or CAPI, any recent changes] What are the three most likely causes of this CPL increase? What should I check first? Claude returns a ranked diagnosis in under 30 seconds. I break down how Claude reads campaign data in a separate guide if you want to see the full process. It connects the data points and surfaces the most likely cause based on the pattern. The manual version of this takes 20 minutes. The AI version takes 3. What AI does not replace: pulling the live data yourself. Frequency, CTR, and CPM come from Ads Manager. You still need to pull them. AI just tells you which ones to look at first. For a full breakdown of which AI tools automate different parts of Meta Ads management, see my review of AI for Facebook Ads: What's Actually Worth Using and the best AI tools for Meta Ads in 2026 . Victor uses a version of this prompt every time he onboards a new client account. The first thing he does is paste 30 days of campaign data and ask Claude to flag anomalies. CPL spikes show up in that initial pass before he has even opened the individual campaigns. Common Mistakes When Diagnosing CPL Spikes Mistake 1: Panicking and making multiple changes at once. Every change resets learning. Make one change, wait for data, then decide. Mistake 2: Killing a campaign instead of diagnosing the cause. A CPL spike is information, not a death sentence. Find the cause before you turn anything off. Mistake 3: Comparing daily CPL instead of 7-day rolling averages. Daily CPL fluctuates naturally. A one-day spike is noise. A seven-day trend is signal. Mistake 4: Not checking CRM data. Sometimes the CPL spike in Ads Manager is a tracking issue, not a real cost increase. Always verify against actual leads received. Mistake 5: Ignoring frequency until it is too late. Monitor frequency weekly. By the time frequency hits 5.0+, the damage is done. Act at 3.0. Mistake 6: Using AI without pasting actual data. Asking Claude "my CPL spiked, what do I do?" gives you the same generic checklist you can find anywhere. Paste the real numbers — frequency, CTR, CPM, budget, recent edits. The output shifts from general advice to a specific diagnosis. Mistake 7: Treating every placement as the same benchmark. Reels, Stories, Facebook Feed, and Audience Network all have different CPL baselines. A spike in Audience Network is not the same problem as a spike in Facebook Feed. Always break down by placement before deciding what is broken. Frequently Asked Questions Why did my Facebook Ads CPL spike suddenly? The most common causes are creative fatigue (frequency above 3.0), learning phase resets from recent campaign edits, audience saturation in narrow targeting, and increased auction competition from seasonal budget pushes. Check frequency first — it explains the majority of sudden CPL increases. How long should I wait before reacting to a CPL spike? Wait at least 3-5 days and aim for 50 conversions of data before making changes. Daily CPL fluctuates due to auction dynamics. A spike that lasts one day is noise. A spike that persists for seven days is a trend worth investigating. Does changing my budget cause CPL to spike? Yes, if the change is significant. Budget increases or decreases over 20% reset the learning phase, which causes temporary CPL instability. Make budget changes in increments of 15-20% maximum and allow 3-5 days between adjustments. What is a normal CPL fluctuation on Facebook Ads? Day-to-day CPL can vary 20-40% from your average due to normal auction dynamics. A CPL increase of 30%+ sustained over 7 days indicates a real issue. Evaluate performance on 7-day or 14-day rolling averages, not daily snapshots. How do I prevent CPL spikes before they happen? Monitor frequency weekly and refresh creative before it crosses 3.0. Avoid making multiple campaign edits in the same week. Maintain audience sizes large enough to support your budget. And always have backup creative ready to launch when current ads fatigue. Can AI help me diagnose a Facebook Ads CPL spike? Yes, and it speeds up the process significantly. Paste your campaign data into Claude or ChatGPT — frequency, CTR, CPM, budget, and any recent edits — then ask it to rank the most likely causes. The key is including actual numbers. Vague questions get generic answers. Specific data gets a specific diagnosis. What takes 20 minutes manually takes under 2 minutes with the right prompt. For the full reporting workflow, see my guide on automating client reporting with AI . Should I pause my Facebook Ads campaign when CPL spikes? Pausing is rarely the right first move. A CPL spike is a diagnostic problem, not a reason to stop. Turning off campaigns destroys the learning phase data you have already built. Work through the checklist first. If the cause is creative fatigue, launch a new ad set with fresh creative rather than pausing the whole campaign. Only pause if tracking is broken and you are flying blind on cost data. What is a good CPL on Facebook Ads? It depends on what a lead is worth to your business. A rough benchmark: your CPL should be under 20% of your average revenue per customer. For a full breakdown of what average CPL looks like by industry, see the Facebook Ads benchmarks for 2026 . If you close 1 in 10 leads and your average deal is $2,000, your break-even CPL is $200. Anything under that is profitable. If you close 1 in 5 leads at $500 per deal, your break-even CPL is $100. Industry averages mean very little without knowing your close rate and deal size. Why does my Facebook Ads CPL spike on weekends? Weekend CPL spikes happen for two reasons. First, auction competition increases because more advertisers chase the same audiences on weekends. Second, user behavior shifts — weekend intent differs from weekday intent, and some offers convert worse when people are not in a work mindset. Check your CPL breakdown by day of week in Ads Manager under the time breakdown. If weekends consistently cost more, shift budget toward Monday through Thursday where your leads come in cheaper. How does Meta's Advantage+ audience affect CPL? Advantage+ audience gives Meta's algorithm more room to find converters outside your defined targeting. For most accounts this reduces CPL over time because the algorithm finds lower-cost audience segments you would not have targeted manually. The tradeoff is less visibility into why CPL changes. If CPL spikes on an Advantage+ campaign, monitor CPM and CTR closely. Rising CPM with stable CTR means the algorithm shifted to a more expensive audience segment. That is the signal to act on. Connect your accounts to monitor CPL across all your clients in 30 seconds. → Try Sucana --- ## How to Test Facebook Ads Creatives With AI (Step-by-Step) URL: https://sucana.ai/blog/how-to-test-meta-ads-creatives-with-ai Published: 2026-02-08 Author: Virgil Brewster Tags: ai, meta-ads, media-buying, agencies, automation How Do You Test Meta Ads Creatives Using AI? You use AI to generate variations faster, but you run them through a quality check before they touch your budget. Meta made Advantage+ Creative the default for all new campaigns in February 2026. That means AI is already making decisions about your creatives whether you planned for it or not. The agencies getting results are the ones who built a filter between the AI output and the live campaign. Most of them have a broader system for AI across performance marketing . In February 2026, Victor my co-founder sent me a screenshot. Three platforms had killed or changed major features in one month. Google killed Call-Only Ads. LinkedIn removed Lookalike audiences . And Meta quietly made Advantage+ the default for every new campaign. That last one hit differently. It meant AI was now in charge of creative decisions by default, not by choice. I see two types of agencies right now. The ones who do not realize this happened. And the ones who built a system around it. If you want to be in the second group, this is how. What Happened When Meta Made Advantage+ the Default Advantage+ Creative is Meta's AI system. It takes your uploaded creatives and generates variations. Different headlines, different image crops, different text overlays. It decides which version to show to which person. On paper, that sounds useful. More variations, less manual work, faster testing. The problem is the AI does not know your brand. A B2B SaaS company found that Meta's AI was generating casual, emoji-heavy copy for their ads. Off-brand, unprofessional, nothing like what they would approve. They had to manually review and reject 70% of the variations before letting them run. That is not a testing system. That is a content filter that costs you time and budget to operate. The fix is not to turn off Advantage+. The fix is to add a layer before it. The Real Cost of Skipping the Quality Check Every creative variation that goes live without a check is money at risk. Meta's algorithm allocates impressions quickly. If a bad variation gets early spend, it drags down the whole test. By the time you notice the off-brand output, the budget is gone and the data is contaminated. The math is simple. If AI generates 20 variations and 70% are off-brand, that is 14 variations burning budget while the other 6 do the actual work. Victor my co-founder showed me this in action when he was researching Go Marble, a competitor that analyzes good versus bad performing videos and gives creative feedback. What struck us both was not the analysis. It was the question it answered: which creatives should never have run in the first place? That is the right question. And that is what a pre-launch check answers. How to Build Your AI Creative Testing System This is the system. Four steps. Build it once and it runs on every campaign. Step 1: Generate variations outside of Meta Before you upload anything, use AI to create your variations. Claude, ChatGPT, or any AI writing tool works. Give it your brand guidelines, your offer, and three or four angles you want to test. Ask it to write five headline variations for each angle. The same approach works for writing Google Ads copy with AI . Then pick the two or three that actually sound like your brand. You are doing the quality check before the upload, not after the spend. Step 2: Score each variation before it goes live This takes five minutes per batch and saves hours of cleanup. For each variation, ask three questions: Does it match our brand voice? Does it make a clear claim? Would a real person read past the first three words? If the answer to any of these is no, cut it. The point is not to test everything. The point is to only test ideas that have a real chance of working. Step 3: Structure the test inside Meta using ABO Use Ad Set Budget Optimization , not Campaign Budget Optimization, for creative testing. CBO gives budget to whoever is winning early. In a creative test, that means one variation gets all the spend before you have enough data to know if it is actually better. ABO forces each variation to get equal spend. That is what a fair test looks like. Run each ad set with one creative variation. Same audience. Same budget per ad set. Let it run for at least seven days before reading the results. Step 4: Let Advantage+ run on the winner Once you have a winning creative from your ABO test, that is when you hand it to Advantage+. You are not using AI to find the winner. You are using AI to scale it. This is backwards from how most agencies use Advantage+. They throw everything in at the start and hope the algorithm figures it out. The smarter move is to do the work first, then let the AI amplify what already works. The Metrics That Tell You a Creative Is Actually Working Hook rate is the first number I look at. That is the percentage of people who watched the first three seconds of your video ad. If your hook rate is below 25%, the creative is not stopping the scroll. The rest of the ad does not matter because people are not getting there. Hold rate comes second. That is the percentage who watched past 15 seconds. A high hook rate with a low hold rate means the opening grabbed attention but the content did not deliver. Then CTR. Then CPA. Most people go straight to CPA and wonder why the data is confusing. The answer is usually in hook rate and hold rate. Fix those two and the rest tends to follow. For image ads, the equivalent of hook rate is your three-second click-through. If people are scrolling past without clicking, the visual is not doing its job. When Not to Use AI for Creative Testing AI creative generation does not work for every situation. If your creative depends on a specific visual that AI cannot source, like a real product photo or a client testimonial video, AI generation is not useful for that asset. Use it for copy variations, not asset creation. If your brand has strict visual guidelines, the AI will almost always produce something off-brand on the first pass. You can train it better over time, but early on plan for high rejection rates. And if you are running very low budgets, less than $50 per day per ad set, the ABO structure will not give you enough data. At that scale, run one strong creative and focus on offer testing instead. What Winning Agencies Are Doing Differently The agencies I see getting real results from AI creative testing share one habit. They treat AI as a brainstorming partner, not a creative director. They use AI to generate 20 ideas quickly. Then a human picks the three worth testing. Then the test runs with human-approved creatives inside an ABO structure. Then Advantage+ gets the winner to scale. Four stages. AI involved in one of them. That is not anti-AI. That is using AI where it is fast and cheap, which is idea generation, and keeping humans in the decisions that cost money to get wrong. Meta's Advantage+ will keep getting better. The filter between AI output and live campaign will always matter. For a full review of the third-party AI tools that support this workflow, I compared them all in the best AI tools for Meta Ads in 2026 . For a broader look at what's worth using across all AI features in Facebook Ads, see AI for Facebook Ads: What's Actually Worth Using . Frequently Asked Questions How do I test creatives on Meta in 2026? Use ABO (Ad Set Budget Optimization) for the test phase and keep each ad set to one creative variation. Run with equal budgets per ad set for at least seven days before comparing results. Once you have a winner, scale it with Advantage+. The key change in 2026 is that Advantage+ is now the default, so manually switch to ABO before starting any creative test, or your budget consolidates before the data means anything. What is the difference between ABO and CBO for creative testing? ABO (Ad Set Budget Optimization) controls budget at the ad set level, so each creative variation gets equal spend. CBO (Campaign Budget Optimization) controls budget at the campaign level and pushes spend toward early winners. For creative testing, ABO is the right choice. CBO decides too fast, before your test has enough data to mean anything. What metrics should I use to evaluate creative performance? Start with hook rate (percentage who watched the first 3 seconds of your video), then hold rate (percentage who watched past 15 seconds). These two tell you if the creative stops the scroll and holds attention. Then look at CTR and CPA. Most performance problems trace back to hook and hold rate. Fix those first. How often should I refresh creatives to avoid creative fatigue? Check frequency every week. If your frequency is above 2.5 and CTR is dropping, the creative is fatigued. Refresh before ROAS drops, not after. For high-spend accounts, plan a new creative batch every two to three weeks. For lower-spend accounts, monthly is usually enough. How do I detect creative fatigue in Meta Ads? Three signals: frequency climbing above 2, CTR dropping week over week, and CPM increasing with no targeting changes. When you see two of these three together, the audience has seen your ad too many times. Build a weekly check into your reporting routine. Fatigue shows up in the data before it shows up in your results. If your CPL spikes alongside these signals, run through my Facebook Ads CPL troubleshooting checklist to find the root cause. What should I A/B test in Meta Ads? Test one variable at a time. The most impactful tests are usually the opening line for copy, the first three seconds for video, or the main image for static ads. The mistake most agencies make is testing too many variables at once. You end up with results you cannot explain. One variable, clean test, clear answer. Can Meta's Advantage+ Creative replace manual testing? Not for finding winners. Advantage+ is good at scaling creatives that already work. It is not reliable for identifying which of your ideas is the best one. Use manual testing with ABO to find the winner. Then use Advantage+ to scale it. That is the workflow. How do I prevent off-brand outputs from AI creative tools? Give the AI a one-page brand brief every time. Your brand voice in three sentences, the words you never use, the tone you are going for, and one example of copy you love and one you hate. I cover the full briefing system in my guide on AI prompt engineering for PPC . AI tools improve dramatically when they have a reference point. Without it, they default to generic. How much budget do I need to test Meta Ad creatives properly? Each variation needs enough spend to get at least 50 clicks before you draw conclusions. At a $2 CPC, that is $100 per variation. If you are testing four variations, budget $400 minimum for the test phase. Less than that and you are reading noise, not signal. What is the fastest way to find a winning creative on Meta? Generate five variations using AI. Cut any that fail the brand check. Run the remaining two or three in ABO with equal budgets for seven days. The one with the best hook rate and lowest CPA wins. The whole cycle from brief to result takes about ten days at a $50 per day test budget. That is fast enough to iterate twice a month. Why is my Advantage+ Creative generating weird outputs? Advantage+ generates variations from your uploaded assets using its own model. It does not know your brand, your tone, or what "off-brand" means for you. The only way to control the output is to control the input. Upload only assets you have already approved. Remove the option to generate text variations if you want to keep the copy in your hands. Review before scaling. Is AI creative testing worth it for small agencies? Yes, but the value is in idea generation, not automation. Use AI to write 20 headline variations in ten minutes instead of two hours. Then pick the best two yourself. The time savings compound. An hour saved on every creative brief adds up across a full client portfolio. --- ## Best ChatGPT Prompts for Marketers: PPC, Ads & Reporting URL: https://sucana.ai/blog/chatgpt-prompts-for-ppc-managers Published: 2026-02-06 Author: Virgil Brewster Tags: ai, ppc, agencies, automation What Are the Best ChatGPT Prompts for PPC Managers? The best ChatGPT prompts for PPC managers start with real client data, not a generic question. I feed ChatGPT the spend, ROAS, campaign history, and what the client keeps complaining about before I ask anything. Generic prompts give generic answers. Prompts with real account context give answers I can actually use in client calls and reports. Here are 15 that work, organized by task, with the context layer built in. Vinod my co-founder said something on a call last month that stuck with me. "90% of people are still using ChatGPT like a search engine." He meant it as a problem. I wrote a full comparison of Claude vs ChatGPT for marketing if you want to see where each tool actually wins. You ask a question. It gives an answer. You copy it. It sounds good. It's useless. The LinkedIn crowd posting "50 best AI prompts for marketers!" is doing exactly that. Surface-level output for surface-level prompts. The PPC managers getting real value are not prompting like that. They treat AI as part of a broader system for performance marketing . They're feeding ChatGPT context first. Spend data. Campaign history. Account structure. What the client wants and what they keep complaining about. Then they ask. The output is night and day. Why Most ChatGPT Prompts for PPC Don't Work I read the top 10 articles ranking for "ChatGPT prompts for PPC" before writing this. They all have the same problem. They treat prompts like search queries. "Write 5 Google Ads headlines for a fitness brand." You get 5 headlines. They're fine. Generic. You could have written them yourself in 5 minutes. The prompt gives ChatGPT zero context. It doesn't know your client's brand, their offer, their best-performing previous headlines, their CPA target, or what already failed last quarter. So it writes in the middle. Average. Safe. The fix is simple: load the context first. I break down the full system in my AI prompt engineering guide for PPC . The Context Layer: Paste This Before Every Prompt Every strong ChatGPT prompt for PPC starts the same way. You paste in what ChatGPT needs to know before you ask your first question. This is the context layer: Client: [Name or type of business] Platform: [Google Ads / Meta / LinkedIn / TikTok] Monthly budget: [$X] Campaign goal: [Leads / Purchases / ROAS target] Current ROAS or CPA: [X] What's working: [One sentence] What's not working: [One sentence] Target audience: [Description] You paste that at the start of every new conversation. One time. Then every prompt you run in that session pulls from that context. ChatGPT remembers it throughout the conversation. The output stops being generic. It starts being specific to your client. Keyword Research Prompts How do you use ChatGPT for PPC keyword research? Prompt 1: Long-tail keyword expansion Act as a PPC keyword specialist. I run [type of campaign] for [client type] with a budget of [$X/month]. Their highest-volume keyword cluster is: [main keyword] Find me 10 long-tail variations with commercial intent. Group them by funnel stage: top, mid, bottom. For each keyword, flag the likely match type I should use. Prompt 2: Negative keyword audit Act as a PPC negative keyword strategist. I've been running this campaign for 30 days. Here are my top 20 search terms by impressions: [paste list] Which of these should become negative keywords? Explain the reasoning for each one you flag. Rank them by priority: add now, review later, keep. The negative keyword prompt alone is worth reading this article. Most PPC managers do negative keyword reviews manually, scanning term by term. Paste the list into ChatGPT with that prompt and it does the first pass in 30 seconds. You still review and make the final call. But the grunt work is gone. Ad Copy Prompts How do you use ChatGPT to write PPC ad copy? Prompt 3: Google Ads RSA headlines Act as a direct response copywriter who specializes in Google Ads. [Paste context layer] Here are our 3 best-performing RSA headlines from the past 6 months: [paste them] Write 10 new RSA headlines that follow the same intent but test different angles. Keep each under 30 characters. Label the angle each headline is testing. Prompt 4: Meta Ads primary text Act as a Meta Ads creative strategist. [Paste context layer] Our target audience is: [description] Write 5 primary text variations for a lead gen campaign. Each should open with a different hook type: question, statistic, story, bold claim, empathy. Each variation max 100 words. Prompt 5: A/B test variation Act as a conversion copywriter. I need to A/B test two value propositions for this ad. Current best-performing ad: [paste ad] Angle I want to test: [describe the new angle] Write version B that tests this angle while keeping the same CTA and format. Explain what specifically you changed and why. The key with all three: you're giving ChatGPT your existing winners as reference material. For a deeper system on writing Google Ads copy with AI , I cover the full workflow from brief to final headline. It doesn't write from scratch. It learns your account's voice and iterates on what already converts. Campaign Analysis Prompts Can ChatGPT analyze PPC campaign data? Prompt 6: Performance deep dive Act as a senior PPC analyst. Here is performance data from my Google Ads campaign for the last 30 days: [paste: spend, impressions, clicks, CTR, conversion rate, CPA, ROAS by campaign] Identify the top 3 areas of improvement. Be specific. Give me actions, not observations. For each action, estimate the impact on ROAS if I execute it. Prompt 7: Client call prep I'm going to tell my client that CPL increased this month because of [your reason]. Here is my supporting data: [paste key metrics] Give me 3 ways they might challenge this conclusion. For each challenge, tell me how I should respond. Victor my co-founder uses a version of Prompt 7 before every single client call. He makes his case first. Then he lets ChatGPT play devil's advocate. He walks into the call already knowing every pushback and how to answer it. Clients notice that he never gets rattled. It's not because he's unflappable. It's because he already had the argument before the call started. Client Reporting Prompts How do you use ChatGPT for PPC client reports? Prompt 8: Monthly performance summary Act as a client communication specialist. [Paste context layer] Results this month: - Spend: $X - Leads: X - CPL: $X - ROAS: X (if applicable) - Key win: [one sentence] - Key challenge: [one sentence] Write a 200-word performance summary for the client. Plain English. No jargon. The client is not a PPC expert. End with one thing we're testing next month. Prompt 9: Budget increase recommendation I need to explain to my client why we're recommending a budget increase. Current monthly spend: $X Current ROAS: X Industry benchmark ROAS: X Data supporting the increase: [paste what you have] Write a 3-paragraph recommendation. Lead with the business case, not the metrics. Frame it as an opportunity, not a cost. Prompt 8 alone saves most account managers 2 to 3 hours per client per month. If you want to automate the full reporting workflow, I wrote a guide on how to automate client reporting with AI . You still review every word before it goes out. But the first draft, which used to take 45 minutes of staring at a blank page, takes 2 minutes now. Troubleshooting Prompts How do you use ChatGPT to troubleshoot PPC campaigns? Prompt 10: Learning phase reset My Google Ads campaign hit the learning phase again after [what you changed]. Campaign type: [type] Current budget: $X Current daily spend: $X What are the most likely causes? What should I do, and what should I avoid, for the next 7 days to get through the learning phase faster? Prompt 11: CTR drop investigation My CTR dropped from [X%] to [X%] between [month A] and [month B]. Variables that changed during this period: [list them] What are the most likely causes? Rank them from most likely to least likely. For each: what specific data would confirm or rule it out? The troubleshooting prompts work because you're asking ChatGPT to think with you, not for you. You give it the variables. It gives you a ranked hypothesis list. You verify each one against your actual data. It turns a 2-hour debugging session into a 20-minute one. Strategy Prompts Prompt 12: Scaling roadmap Act as a PPC strategist. [Paste context layer] My client wants to scale from $X to $X monthly spend in 90 days. Current ROAS: X Target ROAS: X Give me 3 scaling approaches. Rank them by risk level: low, medium, high. For each: what has to be true for it to work, and what could go wrong. Prompt 13: Landing page structure Act as a conversion copywriter who specializes in paid traffic landing pages. Offer: [description] Target audience: [description] Their biggest objection: [the thing that stops them from converting] Traffic source: [Google / Meta / LinkedIn] Write a landing page structure. Include: hero copy, 3 benefit statements, trust signals, CTA copy. Every line should address the specific audience and offer. No generic copywriting. The One Prompt That Saves the Most Time Of everything in this list, one prompt I use more than any other. It's not clever. It's not long. But it changes how ChatGPT works for the entire session. You're a senior PPC account manager at an agency. Full account brief: [Paste context layer] I'm going to ask you questions. Answer from this context only. If you don't have enough information to answer, tell me what I need to give you. Don't invent data or make assumptions. You set this up once at the start of a new conversation. Then you ask anything. Campaign health check. Ad copy review. Client prep. Reporting draft. ChatGPT becomes a co-pilot that knows your client. Not a search engine that gives Wikipedia answers. That is the difference between PPC managers who are getting real value from AI right now and the ones still frustrated by it. For a broader look at where all of this is heading, see my take on the future of PPC with AI . What These Prompts Will Not Do They will not give you real search volume data. For that, use tools like Google Keyword Planner . They will not pull live metrics from your ad accounts. They do not know what happened in your campaigns last week unless you paste that data in. ChatGPT is a thinking partner, not a data source. Give it real data. Ask specific questions. Review every output before it goes anywhere. For actual campaign data, including spend, ROAS, CPL by channel, and performance by audience, you need a proper analytics layer connected to your ad platforms. I cover how to set that up in my guide on automating Google Ads reporting with AI . The prompts cover the thinking, writing, and preparation work. The data work needs real tools. Frequently Asked Questions What are the best ChatGPT prompts for PPC managers? The best prompts are the ones that include your client's actual context before you ask anything. Feed ChatGPT the spend, goals, ROAS, and account history at the start of every session. Generic prompts produce generic answers. The prompts in this article are structured so the context goes in first. Why do most ChatGPT PPC prompts give generic output? Because they give ChatGPT no information to work with. Ask "write Google Ads headlines for a fitness brand" and ChatGPT writes for a fictional average client. It has no idea what your actual offer is, who your audience is, what has worked, or what the CPA target is. The more context you give, the more specific and useful the output. Can ChatGPT replace a PPC manager? No. It speeds up specific tasks: ad copy, keyword grouping, negative keyword review, report writing, client prep. But it cannot see your campaign data in real time. It cannot make judgment calls about budget or strategy without human context. The PPC manager still needs to provide the data, review the output, and make the final decisions. What is the best way to use ChatGPT for Google Ads? Start every session by pasting in your client context: budget, platform, goal, ROAS, what's working, what isn't. Then ask specific questions about that account. The more specific the context, the more specific the output. How do I use ChatGPT for ad copy? Paste in your best-performing existing headlines or ad copy as reference material. Then ask ChatGPT to write variations that test a specific angle while following the same structure. Always give it a character limit and specify the exact format you need. Can ChatGPT do keyword research for PPC? It can generate long-tail keyword ideas, group keywords by intent, and flag likely negative keywords from a search term report. It cannot give you real search volume data. Validate any keyword list against real data in Google Keyword Planner , Semrush , or similar tools before adding anything to a live campaign. How do I use ChatGPT for PPC client reports? Paste in your key metrics for the reporting period and a brief client context note. Ask it to write a plain English summary at a specified length. Specify that the audience is not a PPC expert. Always review the draft before it goes to the client. What ChatGPT prompt works best for negative keywords? Paste your top search terms by impressions from the past 30 days. Ask ChatGPT to identify which terms show a clear mismatch with your campaign goal. Ask it to rank them by priority and explain its reasoning for each flag. Run a final review yourself before adding anything to the live account. How do I use ChatGPT to prep for a client call? Tell ChatGPT what conclusion you're planning to give the client and paste in your supporting data. Ask it to give you 3 ways the client might challenge that conclusion, and how to respond to each. It turns 20 minutes of nervous prep into a fast structured rehearsal. How often should I use these ChatGPT prompts in my workflow? For account managers running multiple clients, the highest-value tasks to run through ChatGPT are monthly reporting drafts, negative keyword reviews, and ad copy variation testing. Run the client prep prompt before any performance review call. The goal is not to use AI for everything. The goal is to use it for the tasks where the first draft costs you the most time. --- ## Marketing Agency SOPs: How to Build Them With AI URL: https://sucana.ai/blog/how-to-create-ai-sops-for-your-marketing-agency Published: 2026-02-04 Author: Virgil Brewster Tags: ai, agencies, automation, sops How do you create AI SOPs for your marketing agency? You pick one repeatable task, plan it with Claude, turn it into a skill, set a trigger word, and run it. When the output is not right, you talk to the skill in the same chat until it is. No code required. Here is exactly how I built mine. I am not a coder. I have never written Python. I cannot debug a script. I do not know what an API is beyond a rough idea. And yet, last January, I built a complete automated workflow from scratch. It pulls transcripts from our team meetings using Fireflies . Updates our product roadmap. Creates to-do lists. Scrapes industry news. Generates LinkedIn posts. All by itself. Every morning. The whole thing runs on one instruction file called a SKILL.md, sitting in a folder on my computer. Claude reads it every time the task runs. If you want to see how these skills fit inside a full agency knowledge base, I documented exactly that in how I built an AI brain for my agency with Claude Code . That is what an AI SOP is. A single markdown file with plain-English instructions that Claude follows step by step. If I can build it, your agency can build it. I cover the broader approach in my guide on building AI workflows for your marketing team . What Is an AI SOP and Why Does Every Marketing Agency Need One? An AI SOP is a plain-English instruction file that Claude follows step by step to complete a repeatable task. I write down what data to pull, what to check, and what output to produce. Claude runs it the same way every time. My agency needs them because reporting, audits, and summaries run automatically while we focus on strategy. A standard operating procedure is a documented process. A way of doing something that does not depend on who is in the room. An AI SOP is the same thing, with one difference: Claude follows it. You write down what data to pull, what to check, what to flag, and what output to produce. Claude reads those instructions every time the task runs. It does not forget a step. It does not have a bad Monday. For a marketing agency, that means weekly reporting, campaign audits, client performance summaries, content briefs, and keyword checks. All running while you focus on the work only you can do. How Do You Know Which Tasks Are Right for an AI SOP? The right tasks follow the same pattern every week with the same inputs, process, and output. If I can explain it to a new hire in 20 minutes and they can follow it without calling me, it is an AI SOP candidate. Victor's Monday client reporting was the perfect example. Same data pull, same calculations, same summary, every single week. Not every task should become an AI SOP. Some tasks need judgment. Those stay with you. The right tasks follow the same pattern every single week. Same inputs. Same process. Same output. If you could explain it to a new hire in 20 minutes and they could follow it without calling you, it is a candidate. The fastest way to find them: ask yourself what you dread opening on Monday morning. For Victor my co-founder, it was client reporting. Every Monday, same data pull, same calculations, same summary to the client. Two hours. Every week. I wrote a full guide on how to automate client reporting with AI that walks through the whole process. That is a perfect AI SOP candidate. Start with one task. Build one SOP. See if it saves you time. Then build the next one. If you want to see how AI SOPs fit into the broader infrastructure of running an AI-powered agency — the tech stack, the ops layer, the delivery system — see my guide on how to build an AI-powered marketing agency . How Do You Build an AI SOP Step by Step? Six steps. All in Claude Cowork. No code. Step 1: Decide which task to automate Pick one repeatable task you run every week. Client reporting. Campaign audits. Content briefs. Performance summaries. The rule: same inputs, same process, same output. Every time. Do not try to automate five things at once. Pick the one that eats the most hours. Start there. Step 2: Plan it with Claude Open Claude Cowork. Tell Claude what the task is. "I do a weekly client report every Monday. It takes me two hours. I pull data from Google Ads , calculate CPL for each campaign, compare it to the 4-week average, flag anything that went up more than 20%, and write a one-paragraph summary for the client." Then say: "Can you ask me questions to refine this?" Claude will ask things like: What format does the client expect? What tone do they prefer? What data do you pull exactly? What do you do when data is missing? You answer. Claude builds the plan. By the end of this conversation, Claude knows every detail of the task. Step 3: Turn it into a skill Say: "Turn this into a skill." Claude creates a SKILL.md file in your folder. This file contains all the instructions, the logic, and a trigger word you choose. If you are not sure how skills work, say: "I want to turn this into a skill. What are the next steps?" Claude walks you through it. The trigger word is important. It is the phrase that activates the skill every time you want to run it. "Run the weekly report." "Start the audit." "Pull client numbers." Pick something short that you will remember. That trigger goes into the SKILL.md file. Step 4: Run the skill Open a new chat in Claude Cowork with your folder selected. Type your trigger word. You see "the skill is loading." Claude reads the SKILL.md. Reads the data files in your folder. Produces the output. The first time, the output will not be perfect. That is expected. Step 5: Talk to the skill and refine it This is where it gets good. When the output is not what you want, talk to the skill in the same chat. "Change the tone. This is too formal." "Add a CPL column to the summary." "Do not round the numbers." "The client wants bullet points, not paragraphs." Claude adjusts the skill and runs it again. Right there. Same conversation. No switching tabs. No editing files manually. Just talk to it. Here is what a real refinement loop looked like for my Monday reporting skill. First run: Claude produced the right numbers but wrote paragraphs. My client gets bullet points. I said: "Output this as three bullet points max per campaign. Short. No full sentences." Second run: bullets were too short. Missing context. "Add a one-line explanation for any campaign flagged as underperforming. Why did it flag?" Third run: it flagged a campaign for wrong reasons. "Do not flag campaigns that are still in the learning phase. Check conversion count before flagging." By run four, the output looked exactly like what I would have written myself. That version has run every Monday since without changes. Step 6: Repeat until perfect, then move to the next Keep running. Keep talking. Keep refining. After four or five feedback loops, the skill produces exactly what you would have written yourself. Once it is locked in, that task is done forever. It runs the same way every Monday without you touching it. Then pick your next task and start from Step 1. Frequently Asked Questions What does a real AI SOP look like? Here is the actual SKILL.md I use for my morning workflow at Sucana: "You are my chief of staff. Every morning, you will: Read the Fireflies transcript from yesterday's team call Pull out every action item and assign it to the right person by name Update the product roadmap with anything that changed Flag anything that needs my decision today Write three LinkedIn post hooks based on anything interesting that came up" My trigger word is "LFG." I type it every morning. The skill loads. Five minutes later, my day is organized. I built the whole thing in Claude Cowork. How long does it take to build your first AI SOP? Planning with Claude: 15 minutes. Turning it into a skill: 5 minutes. Running and refining across the first three tries: about an hour. Total: less than two hours to a working first SOP. By my fifth one, I was getting them working in under 45 minutes. Which tasks should stay with a human? Strategy calls where a client is unhappy or a campaign is tanking. Creative direction where judgment matters more than speed. Any situation where invisible context from a previous conversation changes the answer. AI handles the repeatable. You handle the irreplaceable. What is an AI SOP for a marketing agency? A plain-English instruction file called a SKILL.md that tells Claude how to complete a repeatable task. You write down every step. Put your data in the same folder. Claude runs it the same way every time. Same concept as a standard operating procedure, except Claude executes it instead of a team member. Do I need to know how to code to create AI SOPs? No. Every SOP in this article was written in plain English inside Claude Cowork. If you can explain a process in a Slack message, you can write a SKILL.md. No Python. No APIs. No technical setup. For a broader look at the capabilities you need, see AI skills every marketer needs . Which tasks should I turn into AI SOPs first? Anything your team runs every week that follows the same pattern. Campaign performance summaries. CPL alerts. Client reporting. Weekly updates. Predictable inputs and predictable outputs. That is exactly where marketing automation AI works best. How is an AI SOP different from a regular AI prompt? A prompt is a one-time instruction. An AI SOP is a documented, repeatable skill file. It has context, rules, a specific output format, a trigger word, and a connection to real data. It gets tested, refined, and updated over time. A prompt is improvised. An SOP is built. Which AI tool should I use to run my SOPs? I use Claude Cowork. You open it, select a folder, and your SKILL.md files live there. Type the trigger word and the skill runs. The conversational feedback loop is what makes it work. You talk to the skill until the output matches your standard. How do I know if my AI SOP is working correctly? Run it three times on real data. Compare the output to what you would have written yourself. If the numbers match and the tone sounds like your agency, it is working. If something is off, talk to the skill in the same chat. "Change this." "Fix that." Most issues are fixed in one or two feedback rounds. Can AI SOPs replace account managers? No. AI SOPs handle the mechanical parts: pulling data, calculating metrics, formatting reports, flagging anomalies. The relationship work, the judgment calls, the strategy conversations stay with humans. AI SOPs free up account managers to do more of the work only a person can do. How many AI SOPs should my agency build? Start with one. Get it working, then build the next. I cover the full rollout approach in my marketing team AI adoption strategy . Most agencies that try to build ten SOPs at once end up with ten half-working systems. One SOP that saves two hours a week every week is worth more than five that are still being calibrated. What is the biggest mistake agencies make when building AI SOPs? They skip the planning step. They try to write the instruction file without first talking to Claude about what the task actually involves. The result is a vague skill that produces vague output. Always plan first. Let Claude ask you questions. Then turn it into a skill. How long before AI SOPs save meaningful time? Most agencies see real time savings after the second or third run. The first run always takes longer because you are reviewing carefully and giving feedback. By the fourth or fifth run, the skill produces exactly what you would have written. At that point, a two-hour task becomes a five-minute review. --- ## Marketing Workflow Automation: Build AI Workflows URL: https://sucana.ai/blog/how-to-build-ai-workflows-for-your-marketing-team Published: 2026-02-03 Author: Virgil Brewster Tags: ai, marketing, automation, workflows How do you actually build AI workflows that your marketing team will use? We build AI workflows by starting with the tasks the team already hates. At Sucana, I learned this after trying the opposite approach and failing. Vinod and I now audit the team's weekly time logs, find the repetitive tasks eating hours, and build AI automations around those first. The workflow comes from the problem, not the technology. You start with the tasks they already hate. I learned this the hard way. When I first tried adding AI to our marketing operations at Sucana, I made the classic mistake. I looked at what AI could do and tried to find places to use it. That approach failed. What actually works: look at where your team wastes time, then ask if AI can take that off their plate. The workflow comes from the problem, not from the technology. Here's the step-by-step process we use now. Step 1: Find the time sinks Before you touch any AI tool, you need a list of tasks that eat your team's hours without adding value. At Victor's agency, the obvious one was reporting. Every Monday, someone spent 4-6 hours pulling data from Meta, Google, and the CRM, then formatting it into a deck. Same thing every week. Same format. Same data sources. I wrote a full guide on how to automate client reporting with AI if that is your starting point. For a complete system that ties all of this together, see how I built an AI brain for my agency using Claude Code . Another time sink I attacked the same way was my LinkedIn inbox — the breakdown is in how I save 2 hours a day on LinkedIn DMs with Claude . That's a perfect AI workflow candidate. The criteria for a good AI workflow: Repeatable: It happens the same way every time Data-driven: The inputs are structured, not random Time-consuming: It takes hours, not minutes Low-judgment: The decisions are routine, not strategic If a task hits three out of four, it's worth automating. Step 2: Map the inputs and outputs Once you've picked a task, break it down into exactly what goes in and what comes out. For Victor's weekly reporting workflow: Inputs: Meta Ads data (spend, leads, CPL by campaign) Google Ads data (spend, clicks, conversions by campaign) CRM data (lead status, conversion rates by source) Date range (last 7 days) Outputs: Client deck with KPIs Summary email to the account manager Flag list of campaigns that need attention When you write this out, you realize something: most marketing tasks are just data transformation. You take information from one place, reshape it, and put it somewhere else. AI is very good at this. Step 3: Pick the right tools Here's where most people overcomplicate things. You don't need a custom AI agent. You don't need to build something from scratch. You need tools that connect your existing systems and let AI process the data in between. For most marketing teams, the stack looks like this: Data layer: Where your numbers live — ad platforms, CRM, analytics. Connection layer: How data moves between systems — Claude Code, n8n , custom scripts. AI layer: Where the thinking happens — Claude, custom agents. Output layer: Where the result goes — Slack, email, Google Docs, your project management tool. For Victor's reporting workflow, we connected: Meta and Google through their APIs The CRM through a webhook Claude for summarizing and flagging anomalies Slack for delivery Total setup time: about 4 hours. Time saved per week: 4-6 hours. Payback period: one week. Step 4: Write the AI instructions like you're training a new hire This is where most AI workflows break. People write prompts that are too vague. "Summarize this data" doesn't work. You need to tell the AI exactly what you want, the same way you'd tell a junior team member. I cover the full system in my guide on AI prompt engineering . Here's what we use for the weekly reporting summary: "You are a performance marketing analyst. You're looking at last week's campaign data for a lead generation agency. Your job: Calculate the CPL for each campaign Compare this week's CPL to the 4-week average Flag any campaign where CPL increased more than 20% Write a 3-sentence summary of overall performance List the top 3 campaigns by lead volume Format the output as bullet points. Use plain language. No jargon. If any data is missing, say so. Do not make up numbers." That last line is critical. AI will hallucinate data if you don't tell it not to. Step 5: Build human checkpoints I don't trust AI to run anything without a human looking at it before it ships. The architecture we use: AI proposes, human approves. For the reporting workflow: AI pulls the data automatically (no human needed) AI generates the summary and flags (no human needed) A human reviews the output before it goes to the client (always) That review takes 5 minutes instead of 5 hours. Those 5 minutes are non-negotiable. The rule: any AI workflow that touches a client or goes public needs a human checkpoint. For internal stuff, you can automate all the way through. But anything external? Human eyes first. Step 6: Start with one workflow, then expand The mistake I see marketing teams make: they try to automate everything at once. Don't do that. Pick one workflow. Build it. Use it for 30 days. Fix what breaks. Then add another. If you are not sure which task to pick first, I wrote a guide on where agencies should start with AI automation that covers the selection criteria and real costs. Here's the order we used at Sucana: Month 1: Weekly client reporting Month 2: Daily campaign anomaly alerts Month 3: Content briefs from call transcripts Month 4: Lead scoring from CRM data Each workflow built on the one before. By month 4, we had a system. Not a collection of disconnected automations. If your team is still working out how to get buy-in for this shift, I cover the full adoption process in getting your team's buy-in for AI adoption . And if you want to build the skills underneath these workflows, see the core AI skills marketers should build now . What this looks like in practice Victor runs a lead gen agency. Before AI workflows, his Monday looked like this: 7am: Pull Meta data into a spreadsheet 8am: Pull Google data into the same spreadsheet 9am: Cross-reference with CRM 10am: Build the deck 11am: Write the summary email 12pm: Actually start working on campaigns After AI workflows: 7am: Review the AI-generated report in Slack 7:15am: Approve or edit 7:20am: Deck sent automatically 7:25am: Start working on campaigns He got his mornings back. The time he used to spend on reporting now goes to strategy. Looking at creative angles. Talking to clients about what's working. The stuff that actually moves the needle. The three workflows every marketing team should start with If you're not sure where to begin, start here: 1. Reporting automation Take your weekly or monthly report. Break it into inputs and outputs. Connect your data sources to an AI that formats and summarizes. Add a human review step before delivery. Time savings: 4-8 hours per week, depending on client load. 2. Meeting-to-action extraction Record your team calls and client calls. Feed the transcript to AI with instructions to extract: decisions made, action items, and who's responsible for what. We use this at Sucana. After every call, a summary appears in Slack with tasks assigned. Nobody forgets what was agreed. Time savings: 30 minutes per call, plus fewer "wait, what did we decide?" conversations. 3. Content ideation from conversations Every call you have contains content ideas. Problems clients mention. Questions they ask. Workarounds they've built. Feed your transcripts to AI with instructions to pull out: pain points, questions, and insights worth sharing. This is how I generate most of my LinkedIn content. I don't sit in front of a blank page. I mine conversations for real stories. What doesn't work for AI workflows Not every task should be automated. Some things need human judgment all the way through. Strategy calls: AI can transcribe and summarize. It shouldn't decide what to say. Creative direction: AI can generate options. It can't tell you which one fits your brand. Client relationships: AI can draft emails. It shouldn't send them without review. Crisis response: When something breaks, a human needs to own the decision. The pattern: anything that requires judgment, context, or relationship building stays human. Anything that's data transformation and formatting can go to AI. Common mistakes to avoid Mistake 1: Starting with the tool instead of the problem I see this constantly. Someone discovers a cool AI tool and tries to find ways to use it. That's backwards. Start with the task that wastes time. Then find the tool that solves it. Mistake 2: No human checkpoint AI will confidently tell you wrong things. If you let it ship without review, you will embarrass yourself. Always have a human look at outputs before they go external. Mistake 3: Vague prompts "Summarize this" is not a prompt. "Extract the three main action items, list the owner of each, and flag any deadlines mentioned" is a prompt. Be specific. Be explicit. Treat the AI like a new hire who needs detailed instructions. Mistake 4: Trying to automate judgment AI is great at data transformation. It's bad at knowing when to break the rules. If a task requires intuition, keep a human in the loop. How to know if your workflow is working After 30 days, check these metrics: Time saved: Measure the actual hours reclaimed. If it's less than you expected, something's broken. Error rate: Are you catching mistakes in the human review step? If errors are common, your prompts need work. Adoption: Is the team actually using it? If they've gone back to manual, the workflow doesn't fit their needs. Quality: Is the output as good as the manual version? If quality dropped, you've automated too much. The goal isn't to automate everything. The goal is to automate the parts that don't need human judgment, so humans can spend time on the parts that do. What's next Once you have your first three workflows running, you'll start seeing patterns. Data moves through your marketing stack in predictable ways. AI can intercept it at each stage, transform it, and pass it along. The question becomes: where else is my team spending time on tasks that AI could handle? Keep a running list. Every time someone complains about a repetitive task, write it down. Every time you catch yourself doing the same thing for the tenth time, write it down. That list is your roadmap. Frequently Asked Questions How do I start using AI in my marketing workflow? Pick one task that takes hours, happens every week, and follows the same steps each time. Map out what goes in and what comes out. Connect your tools through Claude Code or custom scripts. Add an AI agent to process and summarize. Build in a human review before anything goes external. What tasks should I automate with AI first? Start with reporting. It's the clearest example of data transformation: numbers from multiple sources, formatted into a deliverable. Most teams spend 4-8 hours per week on this. AI can do it in minutes. Once your workflow runs clean, turn it into an AI SOP for your agency so anyone on the team can trigger it. How long does it take to see results from AI workflows? If your workflow is well-scoped, you'll see time savings in the first week. Most teams report 4-6 hours saved per week within 30 days. Payback period for setup time is usually one to two weeks. Can AI replace my marketing team? No. AI handles data transformation and routine tasks. It doesn't handle strategy, judgment, relationships, or creative direction. The best teams use AI to free up time for the work that actually requires human thinking. What tools do I need to build AI marketing workflows? You need four layers: Data layer (ad platforms, CRM) Connection layer (Claude Code, n8n , custom scripts) AI layer (Claude, custom agents) Output layer (Slack, email, docs) Most teams already have most of this. The new piece is connecting them. I compared the options in my review of AI marketing automation tools . What if AI makes mistakes in my workflows? It will. That's why every workflow needs a human checkpoint before anything goes external. Catch mistakes at the review stage, then improve your prompts based on what went wrong. Error rates drop significantly after the first month. How do I get my team to actually use AI workflows? Start with the task they hate most. When they see 4 hours of weekly reporting disappear, adoption isn't a problem. The key is solving a real pain point, not adding a new tool for its own sake. How detailed do my AI prompts need to be? Very detailed — at least at first. Think of it like onboarding a new hire. They need to know exactly what output you want, what format to use, and what to do when something's missing. Vague instructions produce vague results. The more specific your prompt, the less editing you'll do on the output. How do I know which workflows are worth building? Use the four-criteria test: is it repeatable, data-driven, time-consuming, and low-judgment? If a task hits three out of four, it's worth automating. If it requires gut feel, client context, or creative judgment, keep a human in the loop. The highest-ROI workflows are the ones that are both repetitive and painful — the tasks your team dreads every single week. What's the difference between AI automation and just using ChatGPT? ChatGPT is a tool. An AI workflow is a system. When you open ChatGPT and paste in data manually, you're still doing the work — just with a faster writing assistant. A real workflow connects your data sources, triggers automatically, runs the AI processing, and delivers the output to the right place without you touching it. The goal is removing yourself from the repetitive parts entirely. --- ## Meet the Sucana Team: Virgil, Victor & Vinod URL: https://sucana.ai/blog/meet-the-sucana-team Published: 2026-02-01 Author: Virgil Brewster Tags: founders, team, sucana-story, ad-intelligence Sucana was founded by three people: Virgil Brewster, Victor Chazarra, and Vinod Sharma. We are a Dutch marketer in Bali, a Spanish PPC agency owner, and an Indian developer who codes like he breathes. We built Sucana because Victor was drowning in spreadsheets, and we decided to fix it. This is the story of who we are, how we found each other, and why we are building an ad intelligence platform for lead gen agencies. Who Founded Sucana? Sucana was founded by three people: Virgil Brewster, Victor Chazarra, and Vinod Sharma. I handle strategy and marketing. Victor runs a PPC lead gen agency and is our first real user. Vinod built the entire platform from scratch. We met in Bali and now operate remotely from Indonesia, Spain, and India. I'm Virgil Brewster. I'm 51 years old, Dutch, and I've been building businesses for over 23 years. I live in Bali with my dog Bumi. I've built marketing consultancies, launched products, failed at some, succeeded at others. Sucana is what I call my "one last dance." Not because I'm stopping. Because I want to leave something behind that actually matters. My role at Sucana is strategy, marketing, and making sure we stay focused on one thing. I'm the guy who pushes for speed and then gets pulled back by his co-founders. I can't write a line of code. But I can spot when something feels wrong for the user, and I will not shut up about simplicity until the product is so easy a four-year-old could use it. Who Is Victor Chazarra? Victor Chazarra is a co-founder of Sucana and runs a PPC lead generation agency. He manages client campaigns across Facebook, Google, and TikTok. Victor is the reason Sucana exists. His broken spreadsheet reporting process became the problem we built the entire platform to solve. He now runs real client campaigns through Sucana every day. Victor is the reason Sucana exists. He runs a PPC lead gen agency. He manages client campaigns across Facebook Ads , Google Ads , and TikTok. He handles creative, copywriting, automations, client communication, and reporting. All by himself. I wrote about how Claude reads campaign data the way Victor used to do it manually. Every Monday, he used to open his laptop and start the same painful routine that we later solved by automating client reporting with AI . Export data from Facebook Ads Manager . Export data from Google Ads . Dump both into a Google Sheet he built himself. Run complex formulas to match leads to ad spend using UTM parameters. Fix the errors when dots and commas broke his formulas. Then record a Loom video for each client explaining what happened last week. For one client, that process took about two hours. With five clients, that's ten hours a week just on reporting. He built his own Excel sheet to try to fix it. It was smart. Rows for each ad and day, lead matching through UTMs, pivot tables for analysis. But it was fragile. One wrong cell click and the whole thing broke. That spreadsheet became how Sucana started . When I asked Victor early on if he would use what we were building, he kept saying yes. But never with energy. Just polite agreement. Then we nailed the analytics layer. The first version where he could pull data across all time ranges, cross-check it between different clients, all from one dashboard. His face changed. "This is fucking amazing." That was the moment I knew we had something real. As I write this, Victor runs real client campaigns through Sucana every day. He's not a beta tester giving polite feedback. He's a user who would be angry if we took it away. Who Is Vinod Sharma? Vinod Sharma is a co-founder of Sucana and our lead developer. He built the entire platform: front end, back end, database architecture, and API integrations. Vinod codes until midnight and creates backups before every change. He learns by doing, not by reading documentation. His patience and technical discipline keep Sucana stable while we ship fast. Vinod is our developer. Code runs in his blood. I've worked with developers my entire career. Vinod is different. When he hits a wall, he doesn't panic. He says "okay" and tries again. When a database migration threw permission errors, hostname failures, and server version mismatches all in one session, he worked through each one quietly. When it finally worked, he said, "I did not know this was possible." That's Vinod. He learns by doing, not by reading documentation for three weeks. He once called me and said, "I'm tired." Sixteen-hour days for two weeks straight. He wasn't complaining. He was stating a fact. Then he built a side app to track every hour he worked, what he accomplished, what he learned, where time disappeared. Not for us. For himself. Those insights ended up in Sucana's roadmap. Vinod codes until midnight. He creates backups before every change. When I asked him once whether to create a new repository or override the existing one, he said he had a backup. My answer: "Simple then. Don't go all length if you have a backup." Decision made in thirty seconds. That's how we work. No committees. No two-week debates. Backup exists, move forward. How Did the Sucana Founders Meet? Vinod was originally my client. I was helping him grow his business and recognized his development talent. Victor was literally my neighbor in Bali. Every conversation over coffee ended the same way: his agency data was scattered across seven platforms. Victor had the problem. Vinod had the skills. I had the vision. That is how Sucana started. Vinod was a client of mine. I was helping him grow his business, and while we worked together, I saw how talented he was as a developer. Not just fast. He got the why behind things. Victor lived next door. Literally my neighbor in Bali. We'd talk over coffee about the agency world, and every conversation ended the same way: "My data is all over the place." He had seven platforms open every morning. Facebook Ads Manager, Google Ads, his CRM, his Google Sheet, his webhook automations, his client tools. He was drowning. Not because he's bad at his job. Because the tools are broken. I always wanted to build software. Never had the skills to do it myself. But I knew someone who did. So I sent Vinod a message: "Hey, I'd like to do something with a SaaS project. Are you in?" He said yes straight away. Victor had the problem. Vinod had the skills. I had the vision. That's how Sucana started. We are three people in three different countries now. Victor in Spain, Vinod in India, me in Bali. We run daily meetings on Zoom. Some days we talk for twenty minutes. Some days we're on a call for two hours while Vinod migrates databases and I build marketing on the side. We check in, share progress, and keep moving. What Does Each Founder Do at Sucana? Virgil Brewster handles strategy, marketing, and partnerships. I'm the one asking "does this move us forward?" in every meeting. We make decisions together, but someone has to keep pulling the conversation back to what matters right now. That's me. I also handle the brand, the content, and making sure people know we exist. Victor Chazarra is our source of truth. Every feature we build, we test with him first. If he says "nobody needs that," we kill it. He once shot down an entire filter system we spent weeks building. All the dropdown menus, platform filters, campaign filters, conversion type filters. He tested it and said it created confusion. PPC agencies don't have time to learn dropdown logic. They have campaigns to run. So we killed the dropdowns and replaced them with natural language, the same approach I describe in how we built an AI-powered marketing agency . He types "show me October campaigns" and the AI gives him the answer. Vinod Sharma builds everything. Front end, back end, database architecture, API integrations, deployment. When something breaks at 11 PM, he's already fixing it. He pushed us to think about stability before features. Backups before deployments. Git tags before migrations. The boring stuff that saves your product when things go wrong. Why Three Founders Instead of One? I tried the solo route before. Many times. And the truth is: you don't have all the answers. Victor brings momentum. New ideas, new features, new directions. My job is to filter. Not kill his energy, but slow down enough to ask: does this serve the one thing we're solving? Vinod brings patience. When I want to move fast, he says "one thing at a time." When I push for a feature, he asks if we have a backup plan if it breaks. I bring the urgency. The awareness that I'm 51, this is my last real shot at building something that lasts, and every day we don't ship is a day we can't get back. That urgency is why I care so much about the AI skills every marketer needs right now . Neither of us works alone. We tried. Victor alone is a one-man band running out of hours. I alone am a marketer with no product. Vinod alone is a developer with no market insight. Together, we cover the gaps. Where Is the Sucana Team Based? We are fully remote. Victor works from Spain, Vinod from India, and I live in Bali, Indonesia. We run on Zoom , Slack , and a shared obsession with making agency data simple. The time zones are brutal sometimes. But the work doesn't stop. When I go to sleep in Bali, Vinod is still coding. When Victor finishes client calls in Spain, I'm already testing the latest build. It creates a rolling rhythm where something is always moving forward. What Are the Sucana Founders Building? We are building an ad intelligence platform for lead gen PPC agencies. Sucana connects ad platform data end-to-end, from the click to the conversion, and lets agencies ask questions in plain language. Victor needed it because his reporting was scattered across seven platforms. No existing tool solved it. So we built one, by an ad agency, for ad agencies. Sucana is an ad intelligence platform for lead gen PPC agencies, built for where PPC is heading with AI . It shows agencies their real data, end-to-end, from the ad click to the landing page conversion. And you can talk to that data. Ask it questions. Get answers in plain language. We built it because Victor needed it. His data was scattered across seven platforms. His spreadsheet was fragile. His clients wanted simple answers to simple questions: What have you done? What have you spent? What are you going to do next week? That reporting gap is why agencies lose clients. No tool gave him that. So we built one. Built by an ad agency, for ad agencies. Frequently Asked Questions Who are the founders of Sucana? Sucana was founded by Virgil Brewster (strategy and marketing), Victor Chazarra (agency operations and product testing), and Vinod Sharma (development and engineering). The three co-founders bring complementary skills: marketing, real agency experience, and technical development. What are Sucana's founders' backgrounds? Virgil Brewster has 23+ years of marketing and business building experience. Victor Chazarra runs a PPC lead generation agency and is Sucana's first real user. Vinod Sharma is a full-stack developer who built Sucana's entire technical infrastructure. How did Virgil, Victor, and Vinod meet? Vinod was originally a client of Virgil's. Virgil recognized his development talent and recruited him when it was time to build a SaaS product. Victor was Virgil's neighbor in Bali, bringing the real agency problem that became Sucana's foundation. The three now operate remotely from Bali, Spain, and India. What roles do the Sucana founders play? Virgil handles strategy, marketing, and partnerships. Victor provides real agency feedback and tests every feature with live client campaigns. Vinod builds the platform, manages the infrastructure, and ensures technical stability. Where is the Sucana team based? Sucana operates fully remote with founders in three countries: Virgil Brewster in Bali, Indonesia; Victor Chazarra in Spain; and Vinod Sharma in India. The team runs daily meetings on Zoom and communicates through Slack. ---