u/kaancata

I work mostly around acquisition, CRM, tracking, reporting, and paid media, and I have been trying to make the whole client stack readable by AI.

The part that has actually helped is not "AI writes better copy." It is the read step across systems that usually do not talk cleanly.

Every client gets a context folder.

Emails, meeting transcripts, call recordings, offer docs, pricing, website content, CRM notes, pipeline logic, tracking notes, ad account data, conversion data, previous tests, all of it lives in one place.

Most of it is pulled in automatically through n8n, Codex automations, or whatever connector makes sense for that client.

The folder structure matters more than I expected. Same rough layout across clients, same naming conventions, same instruction files, same connection notes. When I open a client folder in Claude Code or Codex, the model is not starting from a blank chat. It can read the business first.

The useful workflows are very practical.

Stuff like:

• daily account checks
• CRM lead quality review
• broken conversion handoff checks
• form submission into CRM field checks
• offline conversion upload checks
• meeting transcript into open actions
• comparing ad platform numbers against CRM outcomes

The last one is probably the most important for my work. Ad platforms will happily optimise toward the wrong signal if you let them. If the CRM says the lead quality is bad, I do not care that the platform says performance looks good.

I trust scheduled reads more than autonomous decisions.

Most of the useful stuff in my setup runs on a fixed cadence.

Morning account checks. Weekly search term reviews. Monthly reporting passes. Tuesday and Thursday deeper account work.

Some of it runs through Codex automations, some of it through n8n, some of it is still me manually kicking off the workflow.

The point is that the agent is not the router. I am. The agent does the read work, runs the checks, drafts the output, and tells me what deserves attention.

My alerts are mostly email and Telegram, not Slack. Daily account summaries go to my inbox. Telegram is useful when I want a quick pulse or to trigger something from my phone. If I need detail, I open the folder.

Writes stay gated.

Budget changes, paused campaigns, negative keywords, CRM writes, conversion settings, pipeline changes, website deploys, anything that changes state or can cost the client money.

The model can draft, stage, queue, explain. I still review before it goes live.

That is not me being scared of automation. It is just the only version that survives contact with real accounts, messy tracking, delayed conversion data, platform policies, and clients who understandably do not want an agent freelancing inside their business.

I stopped trying to build a dashboard for this too. The folder is the view. The morning emails tell me what needs attention. If something looks off, I open the relevant client folder and inspect the files, logs, and outputs.

For RevOps-type work, I think this is the part people should look at more. Not "AI replaces ops." More like: can your systems be read together well enough that AI can catch gaps before a human spends three hours reconciling them?

Curious if anyone here is doing this around CRM and attribution. Where do you draw the line between read-only automation and writes?

I have been building around agents for client work for a while now, and my take is probably less exciting than the demo videos.

I don't really want an agent waking up, looking around, and deciding what to do. At least not yet. That sounds cool until the work touches real accounts, client data, budgets, CRMs, tracking, websites, or anything where a bad write actually costs money.

The part I trust is structured context plus scoped jobs.

Every client has their own folder. Emails, meeting transcripts, call recordings, offer docs, pricing, website content, CRM notes, tracking notes, ad account data, conversion data, previous tests, all of it lives in one place.

Most of it is pulled in automatically through n8n, Codex automations, or whatever connector makes sense for that client.

The folder structure matters more than I expected. Same rough layout across clients, same naming conventions, same instruction files, same connection notes. When I open a client folder in Claude Code or Codex, the model is not starting from a blank chat. It can read the business first.

That makes the agent much less stupid.

It is not trying to reason from a prompt like "help this client grow." It can look at what the business is, what we tried before, what changed recently, what the CRM says, what the ad platforms say, what the last meeting was about, and then do a narrow job against that context.

Stuff like:

• daily account check
• tracking audit
• search term review
• source health check
• transcript into open actions
• broken conversion handoff check
• draft recommendations with evidence attached

That is the part that compounds. If I improve the tracking audit once, I can run a better version of it across every client. If a weird edge case comes up in one account, it usually becomes a note or rule I can reuse somewhere else later.

I trust scheduled agents more than open-ended agents.

I tried the version where an agent wakes up, looks around, and decides what matters. It sounds cool. In practice I don't really trust it that much yet (give it 6 months tbh).

Most of the useful stuff in my setup runs on a fixed cadence.

Morning account checks. Weekly search term reviews. Monthly reporting passes. Tuesday and Thursday deeper account work.

Some of it runs through Codex automations, some of it through n8n, some of it is still me manually kicking off the workflow.

The agent is not the router. I am. The agent does the read work, runs the checks, drafts the output, and tells me what deserves attention.

My alerts are mostly email and Telegram, not Slack. Daily account summaries go to my inbox. Telegram is useful when I want a quick pulse or to trigger something from my phone. If I need detail, I open the folder.

Writes stay gated.

Budget changes, paused campaigns, negative keywords, CRM writes, conversion settings, website deploys, anything that changes state or can cost the client money.

The model can draft, stage, queue, explain. I still review before it goes live.

That is not me being scared of automation. It is just the only version that survives contact with real accounts, platform policies, messy tracking, delayed conversion data, and clients who understandably do not want an agent freelancing inside their business.

So I am less interested in "can the agent run 24/7?" and more interested in "does the agent have a structured place to work from, clear jobs, and hard approval gates?"

Curious how others here are handling this. Are you building open-ended agents, or mostly scoped agents with structured memory/context underneath?

I replied to a thread the other day about AI coworkers running 24/7 and realised it is pretty close to the thing I have been trying to run, just from a different angle.

I don't really think of it as a coworker though. That framing makes it sound like a little employee waking up and deciding what to do. I don't want that, at least not for client work where mistakes cost money.

What I want is simpler: every client becomes readable by AI.

Each client has their own folder. Emails, meeting transcripts, call recordings, offer docs, pricing, website content, CRM notes, tracking notes, ad account data, conversion data, previous tests, all of it lives in one place. Most of it is pulled in automatically through n8n, Codex automations, or whatever connector makes sense for that client.

The folder structure matters more than I expected. Same rough layout across clients, same naming conventions, same instruction files, same connection notes. When I open a client folder in Claude Code or Codex, the model is not starting from a blank chat. It can read the business first.

The repeatable work becomes small workflows.

I don't mean some grand agent framework. I mean boring jobs I have done enough times that they deserve their own instructions and scripts.

Search term review. Tracking audit. Daily account check. Broken conversion handoff check. Meeting transcript into open actions. Drafting ad copy against the actual landing page. Looking at CRM lead quality before trusting what the ad platform says.

That is the part that compounds. If I improve the tracking audit once, I can run a better version of it across every client. If a weird edge case comes up in one account, it usually becomes a note or rule I can reuse somewhere else later.

I trust schedules more than wake-up-and-decide agents.

I tried the version where an agent wakes up, looks around, and decides what matters. It sounds cool. In practice I don't really trust it that much yet (give it 6 months tbh).

Most of the useful stuff in my setup runs on a fixed cadence. Morning account checks. Weekly search term reviews. Monthly reporting passes. Tuesday and Thursday deeper account work. Some of it runs through Codex automations, some of it through n8n, some of it is still me manually kicking off the workflow.

The point is that the agent is not the router. I am. The agent does the read work, runs the checks, drafts the output, and tells me what deserves attention.

My alerts are mostly email and Telegram, not Slack. Daily account summaries go to my inbox. Telegram is useful when I want a quick pulse or to trigger something from my phone. If I need detail, I open the folder.

Tools are mostly APIs and files.

Google Ads API, Meta Marketing API, GA4, Search Console, Tag Manager, GHL, website repos, CMS data, spreadsheets, whatever the client actually uses. GHL handles a lot of the CRM side. n8n handles deterministic pipes. Claude Code and Codex sit on top when the task needs reasoning or code.

I have become pretty allergic to adding another SaaS dashboard just because it has AI in the name. Every tool between me and the source data is another layer making decisions for me. Sometimes that is worth it. Most of the time I would rather connect to the API directly and have the model work from the raw context.

Writes stay gated.

This is the part I think people underplay when they talk about autonomous agents.

Budget changes, paused campaigns, negative keywords, CRM writes, conversion settings, website deploys, anything that changes state or can cost the client money. The model can draft, stage, queue, explain. I still review before it goes live.

That is not me being scared of automation. It is just the only version that survives contact with real accounts, platform policies, messy tracking, delayed conversion data, and clients who understandably do not want an agent freelancing inside their business.

I stopped trying to build a dashboard.

I had the instinct to make one. Nice overview, all clients, all tasks, agent activity, source health, the whole thing.

Then I realised I barely wanted to look at it.

The folder is the view. The morning emails tell me what needs attention. Telegram gives me a quick pulse when I need it. If something looks off, I open the relevant client folder and inspect the files, logs, and outputs. A dashboard would mostly become another thing I have to maintain.

So the version I am aiming for is less "AI employee running around 24/7" and more "the business is structured enough that AI can read it and help operate it."

For services work, that is already extremely useful. I don't need the model to decide my whole day. I need it to keep the client context current, run the boring checks, find the weird stuff faster than I would manually, and draft the next thing I should review.

Curious if anyone else is building it from this angle, especially for client/services work rather than a product. What does your client folder or context layer look like, and where do you draw the line on approvals?

Not another "AI is replacing media buyers" post. Just what I actually run for B2B clients now.

Most Meta lead gen setups I see stop at "leads are flowing in". The half that pays off is what happens after the form submission. CRM, qualification, offline conversions back to Meta. Nobody builds it because it's annoying.

What I built sits between the Meta Marketing API, the CRM, and Claude Code. End-to-end:

• Claude Code/Codex drafts the campaign for the client based on their actual offer, ICP, and what's worked historically. Audience, ad copy, 3 creative variant briefs, the lead form questions, the qualification logic.
• Lead forms get built via the Meta API directly. Same with the campaign upload. Everything goes up paused, I review, then publish.
• Form submissions land in the CRM (GoHighLevel in most of my cases) with the full lead context attached, not just name + email.
• Once a lead is qualified or closed, that status fires back to Meta via the Conversions API as an offline conversion with a value. Meta's bidding optimizes against qualified leads, not raw submissions.

For B2B specifically this matters because Meta's default signal is the form submission. Optimize against that and you get a flood of tire kickers, because that's what Meta knows how to find. The moment you feed back qualified-or-closed as the signal, the cost per submitted lead goes up but the cost per actually-useful lead goes down.

The Claude Code/Codex piece is just the surface. The actual leverage is having the offer, ICP, past campaign data, CRM schema, and qualification rules all in one folder the model can read. Without that context every recommendation is generic. The folder is what makes the model useful inside an actual business.

Setup is a one-time thing. Meta Developer App, API credentials, CRM API access, Conversions API event mapping. After that everything runs from prompts and scheduled jobs.

Another thing is that Meta will absolutely ban API developer apps and ad accounts they think are abusing the Marketing API, and after security that's the biggest practical constraint of running this kind of setup. In practice it means respecting rate limits, keeping all writes paused until I manually publish, not scraping, and not doing anything the docs explicitly say not to do. Treat it like you're being audited. AI can move fast, but that doesn't mean it should.

Happy to go into any specific piece. Form schema, qualification logic, the offline conversion mapping, whatever's most useful.

Documenting where I'm at because some of this took me a while to figure out, and might help someone running a similar shape.

First and foremost, AI has completely changed my business.

I run a services practice. Multiple retainer clients across paid ads, websites, CRM, tracking. The thing that's actually allowed me to keep adding clients without hiring isn't a faster brain or a better dashboard. It's that I rebuilt the way I work so the model can actually see what's going on inside each client's business.

Every client has their own folder on my machine that pulls in emails, meeting transcripts, ad account data, conversion data, CRM exports, the website repo when there is one, tracking notes. Then a connection.md file that maps the business to its services, env vars for the keys, small scripts the model can run. Claude Code or Codex sits on top and can answer real questions about that specific business.

So instead of opening 4 tabs and reading 3 spreadsheets and typing into a blank chat, I open the chat and type "audit this account for the last 30 days, what's wasting spend, what's actually producing qualified leads in their CRM" and the model goes and does it. Same chat I'd use for anything else, just pointed at the business.

The path used to go through a person. Me, an analyst, a dev, an external operator. Now it goes through the layer. Anyone with the right access can ask a real question and get a real answer. Context is already there. The model just turns it into something you can talk to.

What's actually working:

• I can take more clients than I should be able to as a solo operator
• The model catches drift I'd otherwise miss until Friday
• First-pass reports get drafted in minutes, not hours
• Context for client calls is ready before the call starts

What's still messy:

• Multi-tenant rate-limit handling. Some APIs throttle hard when you hit them across many clients in parallel.
• Approval gates for writes. I don't let the model move budget or pause campaigns without checking. The line between "read everything" and "writes that need a human" took some iterating.
• Onboarding a new client into the system still takes me a day. I want it to take an hour. This will happen soon however.
• Some artifacts that should be in the layer (calls, real-time CRM events) still aren't. Working on those.

If you're running services or operations solo and trying to figure out how people get past the operator ceiling without hiring out, this is one shape that works. Not the only shape. What mattered for me: artifacts in one place, queryable through one chat. Scripts the model can run. Env vars for the keys. Writes gated by approval.

Bigger picture though, I think this is roughly the shape most operational businesses end up in over the next few years. Not because of any specific tool. Because once you've structured the business so the model can read it, you don't really go back. Each business has its own stack, but the operating-layer pattern on top is what makes natural-language access possible. That's the bet I'm making with how I run mondaybrew, anyway.

Curious if anyone here is running something similar. Where does your context live, and what's still locked outside the chat?!

The actual edge in the next 2-3 years isn't just a smarter model, especially not when many SMB's still don't know how to utilize the models. The edge is whether the business is structured so the model can actually see it. I know this sounds like a Twitter prediction post. It's not. I run this every day for client work, so what follows is the practice, not theory.

The simple version of the experience is this. I open a chat and type "audit this account for the last 30 days, what's wasting spend, what's actually producing qualified leads in the CRM" and the model goes and does it. Same chat I'd use for anything else, just pointed at the business.

That works because behind the chat there is an operating layer between the business and the model. A connection.md file maps the business to its services. Env vars for the keys. Small scripts the model can run. The actual stack varies by business. Mine is ad APIs, CRM, website repo, transcripts, emails. Someone else's would be a totally different list. Whatever the business actually runs on, structured so the model can read it.

The way it used to go is someone had a question, asked the person who had the data and the context, waited, got an answer back. The marketing team. An analyst. The dev who set up tracking. An agency. The shape is the same and the person in the middle is the gate. In the operating-layer version that gate is gone. Anyone inside the business asks the question in natural language and gets a real answer. The context is already there, the model just turns it into something you can talk to.

The companies that have this in 2-3 years aren't "using AI better." They are running on a different operating model. The model is reading structured business context every day, surfacing drift, drafting reports, flagging tracking issues, comparing weeks. The companies that don't have this still email each other reports and ask each other what changed.

Both companies can buy the same Claude license. Only one of them can ask a real question and get a real answer.

If you're trying to figure out where to start, pick one part of the business. Smallest scope that has its own data. Get the artifacts (calls, emails, ad data, CRM, tracking, whatever applies) into one place where Claude Code or Codex can read them. Add a connection map and a few scripts. Ask the boring questions first. Why are leads down. Did tracking break. What changed week over week.

Curious if anyone else here has built something like this for their own business or for clients. Where does your operating layer sit, and which artifacts are still locked outside the chat?

I have been playing more with Claude Design, and my take is that people are underrating it in a slightly wrong way. Most of the conversation is still kind of: "can it make a nice landing page?"

And yeah, it can. The output has gotten insanely good in my opinion. Better spacing, better sections, better taste, less of that default AI SaaS-card schieße. I was a big fan of Google Stitch, but Claude Design is just on another level.

But the part that I care about is what happens after the page exists.

For the people I work with, making the page look good is almost never the bottleneck. The real problem is whether the page matches the ad, the form captures the right info, the events fire correctly, and the CRM gets enough context to judge the lead.

A landing page made with Claude/Codex can sit inside the same workspace as the repo, tracking plan, ad angles, CRM fields, form logic, design drafts, analytics notes, etc. That changes the job from "make me a pretty page" to "make me a page that actually belongs in the acquisition flow."

Which sounds obvious, but this is exactly where a lot of the work gets weird.

For example:

• the ad promise says one thing
• the page headline says something slightly different
• the form asks for the wrong thing
• GA4 event names are messy
• the CRM never gets the fields needed to judge lead quality
• offline conversions do not make it back to the ad account

That whole chain is usually split between designer, dev, marketer, analyst, and some half-dead Zapier/n8n workflow nobody wants to touch.

And this is boring stuff, but it is also the stuff that decides whether the page actually makes money or just looks nice.

This is where Claude design gets interesting to me.

If the model can help create the page and Codex can inspect the repo/scripts/tracking files around it, design becomes one piece of the agent stack. That sounds like a tiny distinction, but for me it changes the whole thing.

I do not mean "let the model run your business while you sleep." (although we are honestly getting there ngl) I still think you need human taste and someone who knows the business. A model will absolutely make confident dumb choices if you give it vague context.

But if the workspace has real context, it gets a lot more useful:

• build the landing page
• adapt it to the actual offer
• match the ad angle
• wire the form correctly
• check if the events actually fire
• compare page copy against CRM lead quality later
• create another variation based on what actually happened, not just vibes

My current opinion is that people are treating AI design like a toy because they stop at "does this page look good?" The real value is when the design lives next to the code, tracking, ads, CRM, and logs.

Curious if anyone else is using Claude this way. Are you mostly using it for prototypes and vibes, or are you letting it touch production pages / tracking / conversion flows with Codex or other agents around it? I have this running for almost all my clients by now. I couldn't imagine it any other way.

Meta released Ads AI Connectors in open beta yesterday.

Link: https://www.facebook.com/business/news/meta-ads-ai-connectors

From their post, it is basically MCP + CLI for Meta ads. MCP can connect your AI tool to the ad account through Meta auth, and they say no developer credentials, API setup or coding required.

It can pull reports, create/edit campaigns, manage catalogs/product feeds, and check signal diagnostics.

The interesting part to me is how normal this makes AI inside ad ops.

I have been using the Meta API a bit for client work, but very carefully. Mostly read-only or staged. Pull performance. Compare against CRM lead quality. Check events/offline conversions. Draft changes. Then review before anything goes live.

Meta always felt more scary than Google for this stuff, at least to me because of these recent posts about ban-waves.

So the official MCP is quite interesting and seems like a stamp of approval.

For reporting and diagnostics, great. For catalog/feed issues, probably very useful. For campaign creation, maybe useful if the account structure and naming rules are clear. For live edits to budgets, bids, exclusions or conversion settings, I would still want approvals and a change log.

And also the context problem is still there too.

Meta can tell you a campaign got leads. It cannot tell you if those leads were trash in the CRM, if sales ignored them, if the form broke, or if the conversion event is inflated.

So I think the good setup is Meta as one source inside the whole client workspace.

Curious how people are thinking about using it.

Mostly read-only reporting? Campaign creation? Actual live edits?

I have been using Claude Code and Codex for Google Ads/PPC work beyond reporting. Not just "summarize performance" or "write RSA ideas." Actual account, pull data, inspect tracking, find wasted spend, create negative keyword suggestions, write RSAs, restructure campaigns, and in some cases push changes back.

The stack is basically Google Ads API, GA4, Search Console, CRM, offline conversions, website/CMS access when available, and Meta as well for accounts that run it. The main thing I have learned is that Google Ads alone is not enough context.

Google can tell you a keyword converted. It cannot tell you whether that lead was useless in the CRM, whether sales marked it unqualified, whether the landing page created the wrong expectation, or whether the conversion event itself is broken. So if the model only sees Google Ads, it can optimize the wrong thing very confidently.

Codex has been much better for the data/account side. Search terms, overspending keywords, weird campaign/ad group patterns, wasted spend, conversion action checks, CRM comparison, that kind of analysis.

Claude Code has been better when the task gets closer to language and structure. RSAs, landing page copy, offer angles, ad group-specific messaging, turning a messy campaign into something that matches intent better.

Most boring but useful example: search terms.

Have it pull the search term report through the API, compare spend/conversions against CRM lead quality, and produce negative keyword candidates with the reason. A lot of wasted spend is painfully obvious when you look at it this way. The issue is usually that nobody wants to do the boring pass consistently.

The more interesting one is tracking.

I built a custom tracking skill for this because tracking is where a lot of PPC work secretly lives. It checks GA4, GTM, Google Ads conversions, forms, CRM status changes, offline conversion uploads, etc. That has been much more useful than I expected because so many "Google Ads problems" are actually tracking/funnel/CRM problems.

I do not think any of this replaces senior PPC people. You still need someone who knows what the business is actually trying to get, what a good lead looks like, what not to touch, when Google recommendations are nonsense, and when the model is being too confident.

But I do think it replaces a lot of junior analyst work.

Pulling reports. Checking search terms. Finding tracking issues. Drafting RSAs. Comparing campaign structure to landing pages. Making weekly notes. Flagging obvious waste. Running the same playbook every week without forgetting half of it because everyone is busy or because the person is managing 40 accounts.

It also changes the economics of smaller accounts. A small account usually does not get deep weekly analysis because the time does not justify it. But if Codex can do the first pass across Ads, CRM, tracking, website, Meta, and landing pages, then the human spends time reviewing decisions instead of digging for the obvious stuff.

Big minus: hallucinations.

If you just ask it "what happened in this account?" "make a giga comprehensive google ads analysis. Make no mistakes." it will 100% invent the answer. The only way I trust it is when it runs scripts and saves outputs.

One script pulls search terms. One pulls campaign/ad group spend. One pulls CRM outcomes. One checks conversion actions. One checks tracking. Then it analyzes the files and cites the actual rows/summaries. Then I ask another model to go through the findings, and keep iterating between two models until it's there.

Basically I treat it less like a smart chatbot and more like an operator that has to work from files, logs, APIs, and scripts.

Same with write access. I will let it write changes, but I want staged actions, change logs, and a reason for each change. Especially negatives, budgets, bids, and conversion settings. No "just go optimize it" nonsense.

My current opinion:

Agencies that do not build this into operations are going to get squeezed. Not overnight, and not because the model magically understands PPC. More because the cost of doing thorough account work is dropping, and clients will eventually expect more depth than a monthly PDF and a few generic recommendations.

Curious who else is already doing this. Are you using Claude Code/Codex with Google Ads API? Keeping it read-only? Letting it write? Connecting CRM/offline conversions/Meta too? I am mostly interested in how far people are letting the system go.

I have been using Claude Code and Codex for Google Ads/PPC work beyond reporting. Not just "summarize performance" or "write RSA ideas." Actual account, pull data, inspect tracking, find wasted spend, create negative keyword suggestions, write RSAs, restructure campaigns, and in some cases push changes back.

The stack is basically Google Ads API, GA4, Search Console, CRM, offline conversions, website/CMS access when available, and Meta as well for accounts that run it. The main thing I have learned is that Google Ads alone is not enough context.

Google can tell you a keyword converted. It cannot tell you whether that lead was useless in the CRM, whether sales marked it unqualified, whether the landing page created the wrong expectation, or whether the conversion event itself is broken. So if the model only sees Google Ads, it can optimize the wrong thing very confidently.

Codex has been much better for the data/account side. Search terms, overspending keywords, weird campaign/ad group patterns, wasted spend, conversion action checks, CRM comparison, that kind of analysis.

Claude Code has been better when the task gets closer to language and structure. RSAs, landing page copy, offer angles, ad group-specific messaging, turning a messy campaign into something that matches intent better.

Most boring but useful example: search terms.

Have it pull the search term report through the API, compare spend/conversions against CRM lead quality, and produce negative keyword candidates with the reason. A lot of wasted spend is painfully obvious when you look at it this way. The issue is usually that nobody wants to do the boring pass consistently.

The more interesting one is tracking.

I built a custom tracking skill for this because tracking is where a lot of PPC work secretly lives. It checks GA4, GTM, Google Ads conversions, forms, CRM status changes, offline conversion uploads, etc. That has been much more useful than I expected because so many "Google Ads problems" are actually tracking/funnel/CRM problems.

I do not think any of this replaces senior PPC people. You still need someone who knows what the business is actually trying to get, what a good lead looks like, what not to touch, when Google recommendations are nonsense, and when the model is being too confident.

But I do think it replaces a lot of junior analyst work.

Pulling reports. Checking search terms. Finding tracking issues. Drafting RSAs. Comparing campaign structure to landing pages. Making weekly notes. Flagging obvious waste. Running the same playbook every week without forgetting half of it because everyone is busy or because the person is managing 40 accounts.

It also changes the economics of smaller accounts. A small account usually does not get deep weekly analysis because the time does not justify it. But if Codex can do the first pass across Ads, CRM, tracking, website, Meta, and landing pages, then the human spends time reviewing decisions instead of digging for the obvious stuff.

Big minus: hallucinations.

If you just ask it "what happened in this account?" "make a giga comprehensive google ads analysis. Make no mistakes." it will 100% invent the answer. The only way I trust it is when it runs scripts and saves outputs.

One script pulls search terms. One pulls campaign/ad group spend. One pulls CRM outcomes. One checks conversion actions. One checks tracking. Then it analyzes the files and cites the actual rows/summaries. Then I ask another model to go through the findings, and keep iterating between two models until it's there.

Basically I treat it less like a smart chatbot and more like an operator that has to work from files, logs, APIs, and scripts.

Same with write access. I will let it write changes, but I want staged actions, change logs, and a reason for each change. Especially negatives, budgets, bids, and conversion settings. No "just go optimize it" nonsense.

My current opinion:

Agencies that do not build this into operations are going to get squeezed. Not overnight, and not because the model magically understands PPC. More because the cost of doing thorough account work is dropping, and clients will eventually expect more depth than a monthly PDF and a few generic recommendations.

Curious who else is already doing this. Are you using Claude Code/Codex with Google Ads API? Keeping it read-only? Letting it write? Connecting CRM/offline conversions/Meta too? I am mostly interested in how far people are letting the system go.

I have been using Claude Code and Codex for Google Ads/PPC work beyond reporting. Not just "summarize performance" or "write RSA ideas." Actual account, pull data, inspect tracking, find wasted spend, create negative keyword suggestions, write RSAs, restructure campaigns, and in some cases push changes back.

The stack is basically Google Ads API, GA4, Search Console, CRM, offline conversions, website/CMS access when available, and Meta as well for accounts that run it. The main thing I have learned is that Google Ads alone is not enough context.

Google can tell you a keyword converted. It cannot tell you whether that lead was useless in the CRM, whether sales marked it unqualified, whether the landing page created the wrong expectation, or whether the conversion event itself is broken. So if the model only sees Google Ads, it can optimize the wrong thing very confidently.

Codex has been much better for the data/account side. Search terms, overspending keywords, weird campaign/ad group patterns, wasted spend, conversion action checks, CRM comparison, that kind of analysis.

Claude Code has been better when the task gets closer to language and structure. RSAs, landing page copy, offer angles, ad group-specific messaging, turning a messy campaign into something that matches intent better.

Most boring but useful example: search terms.

Have it pull the search term report through the API, compare spend/conversions against CRM lead quality, and produce negative keyword candidates with the reason. A lot of wasted spend is painfully obvious when you look at it this way. The issue is usually that nobody wants to do the boring pass consistently.

The more interesting one is tracking.

I built a custom tracking skill for this because tracking is where a lot of PPC work secretly lives. It checks GA4, GTM, Google Ads conversions, forms, CRM status changes, offline conversion uploads, etc. That has been much more useful than I expected because so many "Google Ads problems" are actually tracking/funnel/CRM problems.

I do not think any of this replaces senior PPC people. You still need someone who knows what the business is actually trying to get, what a good lead looks like, what not to touch, when Google recommendations are nonsense, and when the model is being too confident.

But I do think it replaces a lot of junior analyst work.

Pulling reports. Checking search terms. Finding tracking issues. Drafting RSAs. Comparing campaign structure to landing pages. Making weekly notes. Flagging obvious waste. Running the same playbook every week without forgetting half of it because everyone is busy or because the person is managing 40 accounts.

It also changes the economics of smaller accounts. A small account usually does not get deep weekly analysis because the time does not justify it. But if Codex can do the first pass across Ads, CRM, tracking, website, Meta, and landing pages, then the human spends time reviewing decisions instead of digging for the obvious stuff.

Big minus: hallucinations.

If you just ask it "what happened in this account?" "make a giga comprehensive google ads analysis. Make no mistakes." it will 100% invent the answer. The only way I trust it is when it runs scripts and saves outputs.

One script pulls search terms. One pulls campaign/ad group spend. One pulls CRM outcomes. One checks conversion actions. One checks tracking. Then it analyzes the files and cites the actual rows/summaries. Then I ask another model to go through the findings, and keep iterating between two models until it's there.

Basically I treat it less like a smart chatbot and more like an operator that has to work from files, logs, APIs, and scripts.

Same with write access. I will let it write changes, but I want staged actions, change logs, and a reason for each change. Especially negatives, budgets, bids, and conversion settings. No "just go optimize it" nonsense.

My current opinion:

Agencies that do not build this into operations are going to get squeezed. Not overnight, and not because the model magically understands PPC. More because the cost of doing thorough account work is dropping, and clients will eventually expect more depth than a monthly PDF and a few generic recommendations.

Curious who else is already doing this. Are you using Claude Code/Codex with Google Ads API? Keeping it read-only? Letting it write? Connecting CRM/offline conversions/Meta too? I am mostly interested in how far people are letting the system go.

I have been using Claude Code and Codex for Google Ads/PPC work beyond reporting. Not just "summarize performance" or "write RSA ideas." Actual account, pull data, inspect tracking, find wasted spend, create negative keyword suggestions, write RSAs, restructure campaigns, and in some cases push changes back.

The stack is basically Google Ads API, GA4, Search Console, CRM, offline conversions, website/CMS access when available, and Meta as well for accounts that run it. The main thing I have learned is that Google Ads alone is not enough context.

Google can tell you a keyword converted. It cannot tell you whether that lead was useless in the CRM, whether sales marked it unqualified, whether the landing page created the wrong expectation, or whether the conversion event itself is broken. So if the model only sees Google Ads, it can optimize the wrong thing very confidently.

Codex has been much better for the data/account side. Search terms, overspending keywords, weird campaign/ad group patterns, wasted spend, conversion action checks, CRM comparison, that kind of analysis.

Claude Code has been better when the task gets closer to language and structure. RSAs, landing page copy, offer angles, ad group-specific messaging, turning a messy campaign into something that matches intent better.

Most boring but useful example: search terms.

Have it pull the search term report through the API, compare spend/conversions against CRM lead quality, and produce negative keyword candidates with the reason. A lot of wasted spend is painfully obvious when you look at it this way. The issue is usually that nobody wants to do the boring pass consistently.

The more interesting one is tracking.

I built a custom tracking skill for this because tracking is where a lot of PPC work secretly lives. It checks GA4, GTM, Google Ads conversions, forms, CRM status changes, offline conversion uploads, etc. That has been much more useful than I expected because so many "Google Ads problems" are actually tracking/funnel/CRM problems.

I do not think any of this replaces senior PPC people. You still need someone who knows what the business is actually trying to get, what a good lead looks like, what not to touch, when Google recommendations are nonsense, and when the model is being too confident.

But I do think it replaces a lot of junior analyst work.

Pulling reports. Checking search terms. Finding tracking issues. Drafting RSAs. Comparing campaign structure to landing pages. Making weekly notes. Flagging obvious waste. Running the same playbook every week without forgetting half of it because everyone is busy or because the person is managing 40 accounts.

It also changes the economics of smaller accounts. A small account usually does not get deep weekly analysis because the time does not justify it. But if Codex can do the first pass across Ads, CRM, tracking, website, Meta, and landing pages, then the human spends time reviewing decisions instead of digging for the obvious stuff.

Big minus: hallucinations.

If you just ask it "what happened in this account?" "make a giga comprehensive google ads analysis. Make no mistakes." it will 100% invent the answer. The only way I trust it is when it runs scripts and saves outputs.

One script pulls search terms. One pulls campaign/ad group spend. One pulls CRM outcomes. One checks conversion actions. One checks tracking. Then it analyzes the files and cites the actual rows/summaries. Then I ask another model to go through the findings, and keep iterating between two models until it's there.

Basically I treat it less like a smart chatbot and more like an operator that has to work from files, logs, APIs, and scripts.

Same with write access. I will let it write changes, but I want staged actions, change logs, and a reason for each change. Especially negatives, budgets, bids, and conversion settings. No "just go optimize it" nonsense.

My current opinion:

Agencies that do not build this into operations are going to get squeezed. Not overnight, and not because the model magically understands PPC. More because the cost of doing thorough account work is dropping, and clients will eventually expect more depth than a monthly PDF and a few generic recommendations.

Curious who else is already doing this. Are you using Claude Code/Codex with Google Ads API? Keeping it read-only? Letting it write? Connecting CRM/offline conversions/Meta too? I am mostly interested in how far people are letting the system go.

I have been using Claude Code and Codex for Google Ads/PPC work beyond reporting. Not just "summarize performance" or "write RSA ideas." Actual account, pull data, inspect tracking, find wasted spend, create negative keyword suggestions, write RSAs, restructure campaigns, and in some cases push changes back.

The stack is basically Google Ads API, GA4, Search Console, CRM, offline conversions, website/CMS access when available, and Meta as well for accounts that run it. The main thing I have learned is that Google Ads alone is not enough context.

Google can tell you a keyword converted. It cannot tell you whether that lead was useless in the CRM, whether sales marked it unqualified, whether the landing page created the wrong expectation, or whether the conversion event itself is broken. So if the model only sees Google Ads, it can optimize the wrong thing very confidently.

Codex has been much better for the data/account side. Search terms, overspending keywords, weird campaign/ad group patterns, wasted spend, conversion action checks, CRM comparison, that kind of analysis.

Claude Code has been better when the task gets closer to language and structure. RSAs, landing page copy, offer angles, ad group-specific messaging, turning a messy campaign into something that matches intent better.

Most boring but useful example: search terms.

Have it pull the search term report through the API, compare spend/conversions against CRM lead quality, and produce negative keyword candidates with the reason. A lot of wasted spend is painfully obvious when you look at it this way. The issue is usually that nobody wants to do the boring pass consistently.

The more interesting one is tracking.

I built a custom tracking skill for this because tracking is where a lot of PPC work secretly lives. It checks GA4, GTM, Google Ads conversions, forms, CRM status changes, offline conversion uploads, etc. That has been much more useful than I expected because so many "Google Ads problems" are actually tracking/funnel/CRM problems.

I do not think any of this replaces senior PPC people. You still need someone who knows what the business is actually trying to get, what a good lead looks like, what not to touch, when Google recommendations are nonsense, and when the model is being too confident.

But I do think it replaces a lot of junior analyst work.

Pulling reports. Checking search terms. Finding tracking issues. Drafting RSAs. Comparing campaign structure to landing pages. Making weekly notes. Flagging obvious waste. Running the same playbook every week without forgetting half of it because everyone is busy or because the person is managing 40 accounts.

It also changes the economics of smaller accounts. A small account usually does not get deep weekly analysis because the time does not justify it. But if Codex can do the first pass across Ads, CRM, tracking, website, Meta, and landing pages, then the human spends time reviewing decisions instead of digging for the obvious stuff.

Big minus: hallucinations.

If you just ask it "what happened in this account?" "make a giga comprehensive google ads analysis. Make no mistakes." it will 100% invent the answer. The only way I trust it is when it runs scripts and saves outputs.

One script pulls search terms. One pulls campaign/ad group spend. One pulls CRM outcomes. One checks conversion actions. One checks tracking. Then it analyzes the files and cites the actual rows/summaries. Then I ask another model to go through the findings, and keep iterating between two models until it's there.

Basically I treat it less like a smart chatbot and more like an operator that has to work from files, logs, APIs, and scripts.

Same with write access. I will let it write changes, but I want staged actions, change logs, and a reason for each change. Especially negatives, budgets, bids, and conversion settings. No "just go optimize it" nonsense.

My current opinion:

Agencies that do not build this into operations are going to get squeezed. Not overnight, and not because the model magically understands PPC. More because the cost of doing thorough account work is dropping, and clients will eventually expect more depth than a monthly PDF and a few generic recommendations.

Curious who else is already doing this. Are you using Claude Code/Codex with Google Ads API? Keeping it read-only? Letting it write? Connecting CRM/offline conversions/Meta too? I am mostly interested in how far people are letting the system go.