Field notes · Manu
Using AI on your ads, then checking every number it gives you
The careful half of the room. They don’t trust AI, they’ve been told to use it anyway, so they use it and then check everything it says. The distrust is right. The checking is where the week goes.
Short answer: if you check every number an AI gives you about your ad accounts, you’ve added a step, not removed one. The fix isn’t trusting it more. It’s checking the method once instead of every answer: one definition per number, data treated before the model sees it, and a stated point to compare against.
Why do careful teams end up with more work?
Here’s the workflow I see most, in agencies and in-house. You export last week from Google Ads and Meta. You paste it into ChatGPT or Claude. You ask what happened. It answers, fluently. Then you open Ads Manager and check every number it gave you, because you don’t trust it.
You’re right not to trust it. But look at what just happened to the report. It used to take an hour. Now it takes the hour, plus the export, plus the prompt, plus the checking.
The sentence I hear most when I talk to agencies is we want AI in the team, but we can’t lose quality, and we don’t trust it. For this group, that sentence has already turned into overtime.
And it doesn’t feel like overtime, which is the strange part. METR measured it with experienced software developers in 2025. They took 19% longer with AI, and still believed it had made them faster:
“Developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%.”
METR, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity, 10 July 2025.
Different job, same shape. The AI step feels fast. The checking after it doesn’t get counted.
Why is checking the right instinct?
Because an AI reading your ad data makes a few mistakes very confidently.
- It agrees with you. Frame the question as this campaign is underperforming, right? and it will usually find the reasons. That’s a measured behaviour of today’s assistants, not a quirk of one tool. Letting AI run your ads goes into it.
- It adds what shouldn’t be added. Google and Meta count conversions differently, and summing them gives you a number neither platform would sign. Reading Google and Meta together has the three that bite most.
- It reads a gap as a fall. A day that didn’t import looks exactly like a day with no sales. A zero is not a gap.
Someone who rolled out an AI analytics layer at their own company put it well on r/analytics: “It’ll confidently give you a number that’s subtly wrong.” That’s the job you’re doing when you check. You’re not paranoid. You’re the only control in the loop.
Doesn’t connecting an MCP fix this?
No. It gives the AI more places to look. That’s all an MCP is: a door into the data.
And more doors can mean more checking, not less. Every source arrives with its own definitions: its attribution window, what it calls a conversion, its currency, when it considers a day closed. Before you can compare two of them, someone has to standardise them. If nobody does, the model does it silently, its own way, differently each time you ask. Then you’re checking that too.
I learned this with my own money. In 2025 I spent more than €10,000 on ad management platforms. What I got back, mostly, was the same comments Google and Meta already give you for free, with a new interface, and the same general rules for every account. Nothing learned from the account itself: what it had tried, what had worked, what the client counts as a good lead.
Connecting your AI to your ad data is the easy half, and worth doing properly. It’s just not the half that removes the checking.
Or see what your team should look at first: the Paid Media Capacity Check, being built nowWhat did a real agency pilot show about checking?
At the agency where I work I run a pilot: one AI assistant for 13 client accounts, with more than 11 media buyers working across them. When I went through the accounts in detail in August 2026, the dominant blocker was measurement, and it wasn’t the AI. Seven of the accounts I read were blocked or held back by not knowing whether their conversion numbers meant anything.
It came in costumes:
- one account recorded more conversions than clicks
- conversion values were set to €1 placeholders, so every ROAS built on them meant nothing
- the same download event was imported into two ad accounts, so the totals counted it twice
- an offline conversion import was switched on across dozens of campaigns and had never fired once
Underneath it was always the same: a number existed, looked usable, and nobody had established what it counted. You can’t verify an AI’s answer against a number nobody defined. So the checking never ends.
Why can’t one lens check every account?
In the pilot, the same symptom meant opposite things in different accounts, and that is what makes measurement hard to hand to a machine. In one account, soft conversions were a design necessity, because a referral step outside anyone’s control came before every booking. In another, tracking had been switched off on purpose and the objective rewritten around it. A third was simply broken. Same symptom, three readings. An assistant checking all of them with one lens is wrong in two of the three, and sure of itself in all three.
What the pilot’s rules got right is the other half of the fix. What you say in the chat ranks last, below every approved fact, so the model can’t simply agree its way into your assumption. Every statement carries a label: confirmed fact, client statement, assumption, recommendation, open question. And anything unknown is written as missing, which means unknown, never zero. With labels, you check the label, not the whole answer.
How do you check the method once instead of every answer?
The checking doesn’t disappear. It moves: from every answer, every time, to the definitions and the sources, once per client and again when something changes.
| Checking every answer | Checking the method once | |
|---|---|---|
| What you verify | Each number, every time you ask. | Each definition, once, per client. |
| When the AI is wrong | You find out if you happened to check that number. | The answer says what it read and when, so a wrong input shows. |
| A missing day | Looks like a bad day. | Arrives marked as not measured. |
| A new account | Start checking from zero. | Write its definitions first, then ask. |
| Cost over time | Grows with every account you add. | Mostly up front. |
| Best for | One account, the odd question. | Several accounts and a weekly decision. |
What does checking the method look like in practice?
Four things, and none of them needs a particular tool:
- One written definition per number, per client. Which conversion counts. Which attribution window. What a qualified lead is. If a new hire couldn’t read it off a page, the AI can’t either.
- Treat the data before the model sees it. Nothing summed across platforms that shouldn’t be. Missing days marked as missing. One currency.
- Give it the point to compare against. The target, and the same number of days before, fully collected. If the earlier window has a gap, no comparison. A comparison against a hole reads as a miracle or a disaster.
- Make every answer say where it came from. Which account, which days, when it was read, and what was missing. Then you check the source line, not the whole answer.
The same person on r/analytics said the thing that finally stopped finance double-checking was making the answer come back “verified instead of freshly generated”. Different field, same fix.
Or see what your team should look at first: the Paid Media Capacity Check, being built nowWhere did my own hours actually go?
Not in the report. In the jump between accounts.
Once I was running more than five important accounts, every switch cost me at least 30 minutes of getting the context back: what’s happening, what changed, what I decided last time and why.
Today everything I do runs through the system I built. It’s what took me from 7 accounts to 15 a week, while I’m still programming it, with some life left over and even time for meetings. That’s my number, my accounts and my way of working. It isn’t a promise about yours.
What I still don’t know
Whether it holds for a team. For me the checking has moved: I don’t re-check numbers one by one any more, I re-read the definitions when a platform or a client changes something. But those are my definitions, for the way I work. The pilot has more than 11 media buyers with their own habits, and a definition one of them writes isn’t automatically one the others trust. That’s the part I’m still learning.
And how long a definition survives. Google and Meta change what they count with a short announcement, and a definition that was right in September can be quietly wrong in November. Right now the answer is re-reading them when a platform moves. I haven’t found a better one.
The AI is going to agree with you anyway. At least make it agree with you on the right numbers.
Or see what your team should look at first: the Paid Media Capacity Check, being built nowQuestions people actually ask
Shouldn’t you always check what an AI tells you about your ads?
Yes. What changes is what you check. Re-checking numbers one by one grows with every account. Checking the definitions and the sources when something changes grows much more slowly, and every answer should say what it read and when. Most wrong answers come from a wrong input, not a bad sentence.
Isn’t this just better prompting?
Partly. A good prompt pack helps. But a prompt lives for one conversation. A definition of what counts as a lead has to survive every conversation, every person on the team and every account. That belongs in the system the AI reads, not in what you type.
Is the official Google Ads MCP enough?
Google’s official MCP is a good door, and read-only, which is sensible. It gives the model access to the data. It does not tell it which conversion your client counts, what to compare against, or that a missing day is not a zero. That part is yours to write, whichever door you use.
Does this mean you trust AI with your ad accounts?
With the reading, once the reading has rules. Changes are a different matter: those go in as a proposal a person signs, and nothing moves before that signature.
Is this a pitch for Climent Ads Assistant?
Partly. But the four steps on this page work in a spreadsheet and a shared document. The tool exists because doing them by hand across fifteen accounts stopped being possible.
Related reading
- Letting AI run your ads: the brief nobody wrote. The other half of the room: the people who hand the AI the keys, and what a pilot with 11+ media buyers showed about why it fails.
- Reading Google and Meta together. Three mistakes that survive connecting an assistant to both platforms, and the block to paste in before you ask anything.
- 12 Ad-Ops Skills for Claude. The checks written down once, so the model runs the same one the same way every time.
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Who wrote this
I’m Manu. This is the system I wanted and couldn’t buy, so I’m building it. Climent Ads Assistant reads Meta, Google Ads, GA4 and Search Console one client at a time, treats the numbers before the model sees them, and says what it read and when. It changes a campaign only through a proposal that a person signs in the product.