Two separate Reddit threads surfaced this week, one in r/googleads, one in r/PPC, both asking the same question: what does AI Google Ads automation actually look like in practice?
The engagement was high. Nearly a hundred upvotes combined and over fifty comments, mostly from practitioners sharing real workflows they have built. Not theoretical use cases. Actual things running in production, pulling real data, saving real hours.
The 98% upvote ratio tells you this is not a controversial topic. It is a genuine question from people trying to figure this out in real time. And the answers are more grounded than you might expect.
Here is what marketers are actually building, where the limits are, and why the hype around "AI running your ads" misses the point entirely.
The four automation categories emerging
Reading through both threads, four clear patterns stand out. These are not theoretical categories. They are the workflows that keep getting mentioned by different people, independently, across both discussions.
- 1. Product feed management (custom labels, ROAS segmentation, feed health)
- 2. Landing page auditing (broken links, speed, message match)
- 3. Budget forecasting and pacing (spend projections, cross-account rollups)
- 4. Anomaly detection (CPC spikes, conversion drops, impression share shifts)
Product feed management
If you run Shopping or Performance Max campaigns, you know that feed quality determines performance. And feed management is tedious, repetitive, error-prone work. Exactly the kind of thing that benefits from automation.
Marketers are using AI coding agents to build scripts that apply custom labels based on ROAS thresholds, segment products by margin or performance tier, and run automated feed health checks. The workflow looks like this: pull product performance data from the API, cross-reference it with the feed, apply rules, output a cleaned and labelled feed.
This is not glamorous work. But it is the kind of work that, when done consistently, separates good Shopping campaigns from mediocre ones. Most marketers never do it because the manual version takes hours. An automated script runs in minutes.
Landing page auditing
Several marketers described building workflows that check landing pages across their accounts. Broken links, slow load times, mismatched messaging between ad copy and landing page content.
The value here is obvious. A broken landing page wastes every click you pay for. A slow page kills conversion rate. Mismatched messaging confuses the user and tanks quality score. These are known problems. The issue was never awareness. It was capacity. Nobody has time to manually check hundreds of landing pages every week.
An automated audit catches these problems before they eat your budget. It runs on a schedule, flags issues, and sends an alert. You fix the problem. The script does not fix it for you. It just makes sure you know about it.
Budget forecasting and pacing
This one came up repeatedly. Marketers building pacing scripts that project end-of-month spend based on current run rate, flag accounts that are on track to overspend or underspend, and generate cross-account budget rollups.
For anyone managing more than a handful of accounts, this is a daily task that eats 30 to 60 minutes of checking, calculating, and updating spreadsheets. An automated pacing script does it in seconds and can run multiple times per day. If you want to understand the full scope of this problem, our guide on ad budget pacing covers the fundamentals.
The more sophisticated versions factor in day-of-week spend patterns and seasonal adjustments. But even a simple linear projection beats doing it manually, because it runs consistently and catches problems early.
Anomaly detection
This is probably the most common use case. Scripts that monitor key metrics and flag unusual changes. CPC spikes, conversion drops, impression share shifts, sudden changes in search term patterns. These are the same metrics covered in any thorough Google Ads optimization checklist.
The idea is simple: instead of checking every metric in every account every morning, you let a script do the checking and only surface the things that need your attention.
This is where AI coding agents shine. You describe the rules in plain language ("flag any campaign where CPC increased more than 25% week-over-week") and the agent generates a working script. The monitoring runs on autopilot. You get an alert in Slack or email when something needs your attention.
The catch, which we will get to, is that detection is easy. Diagnosis is hard.
Where AI Google Ads automation actually works
There is a clear pattern across both threads. The workflows that work well share three characteristics.
They are read-only. The script pulls data, analyses it, and reports findings. It does not modify anything in the account.
The cost of a mistake is low. If an anomaly detection script flags a false positive, the worst case is that you spend two minutes looking at a metric that turned out to be fine. If a script accidentally paused your best campaign, the cost is much higher.
The time savings are significant. These are tasks that take 30 minutes to an hour when done manually but run in seconds when automated. Over a week, that adds up fast. Over a month, it is transformative for a small team.
Monitoring and alerting layers. Audit and report generation. Data formatting and visualisation. Anything where you are moving data from one place to another, checking it against rules, and summarising the results.
The human still sets the rules. The human still interprets the results. The human still makes the decisions. The automation handles the tedious middle part: pulling, checking, formatting, alerting.
The line most marketers draw
This was the most consistent theme across both threads. Marketers are comfortable with AI reading their accounts. They are not comfortable with AI changing their accounts.
The distinction sounds simple, but it is important. "I want it to tell me what to do, not do it for me." That quote, or some version of it, appeared in multiple comments.
And the reasoning is sound. A monitoring script that misidentifies an anomaly wastes a few minutes of your time. A script that pauses campaigns, adjusts bids, or reallocates budget based on a flawed rule can cost real money before you notice.
Experienced marketers understand that context matters. A 40% CPC spike might be a problem. Or it might be the expected result of entering a new auction segment. A conversion drop might signal a tracking issue, a landing page problem, or just a slow Tuesday. The data alone does not tell you which one.
This is why the "AI managing your campaigns" narrative misses the point. The value is not in replacing the marketer. It is in giving the marketer better information, faster. The strategic layer, knowing what the numbers mean and what to do about them, still requires someone who understands the account, the business, and the market.
Most marketers in these threads are using AI to build the automation, not to run the campaigns. They describe what they want, the AI generates the code, they review it, test it, and deploy it. The AI is the builder. The marketer is the architect.
Who benefits most
The threads made one thing very clear. Solo marketers and small teams get disproportionate value from these workflows.
If you are a solo practitioner managing ten Google Ads accounts, you simply do not have the hours in the day to manually monitor every metric, audit every landing page, and run budget projections for every account. You prioritise. You check the big accounts daily and the small ones weekly. Things slip through the cracks because they have to.
Automated monitoring changes that equation. One person can now run alerting workflows across all ten accounts, get notified when something needs attention, and focus their time on the accounts and issues that actually matter today. Not the ones they happened to check first.
This used to require a dedicated analyst or an expensive third-party PPC tool. Now a solo marketer can build a custom monitoring layer with an AI coding agent in an afternoon.
For agencies, the calculus is similar. Junior team members spend less time pulling data and more time learning strategy. Account managers get early warnings instead of discovering problems during the weekly review.
The pattern is the same: compress the operational overhead so humans can focus on the work that actually requires human judgement.
What to watch out for
The threads were not all enthusiasm. Experienced practitioners flagged several real risks.
Garbage-in-garbage-out. If your conversion tracking is broken, an AI-generated monitoring script will faithfully report broken data. If your attribution model is flawed, your automated ROAS calculations will be flawed too. Automation amplifies whatever you feed it, good data and bad data alike.
Hallucinated API calls. AI coding agents sometimes generate code that references API endpoints, fields, or parameters that do not exist. The Google Ads API is well-documented but complex. An AI might confidently write a query using a field name that looks plausible but is not actually available. If you do not review the code carefully, you end up debugging phantom errors.
Over-reliance on alerts without understanding root causes. This is the subtler risk. You build an anomaly detection system that flags CPC spikes. It works well. You start relying on it. Over time, you stop looking at the underlying data yourself. When the system flags something unusual, you react to the alert without digging into why it happened. You become reactive instead of strategic.
The "why" still requires a human. A script can tell you that something changed. It cannot tell you whether the change matters, what caused it, or what you should do about it. If you treat AI-generated alerts as answers rather than prompts for investigation, you are building a fragile operation.
The copy-paste trap. Some marketers mentioned grabbing scripts from forums or AI-generated code and running them without understanding what they do. This works until it does not. When something breaks, you need to know how to fix it. When the Google Ads API changes, you need to know how to update the script. Treating automation as a black box is a short-term convenience and a long-term liability.
How aubado fits in
Not every marketer wants to build and maintain custom scripts. If you recognize the problems in this article but would rather have them solved out of the box, that is exactly why aubado exists.
aubado is a modular platform of small, focused apps that handle the operational side of performance marketing. Each one solves a specific problem.
Budget forecasting and pacing? Budget Control gives you real-time spend visibility across all your channels. You will know if you are on track in 90 seconds, without a single line of code or a custom spreadsheet.
Performance anomalies and campaign monitoring? The Google Ads Optimization Specialist surfaces what matters: conversion drops, impression share changes, CPC shifts. It is the kind of monitoring these scripts aim to replicate, already built and connected.
Reporting automation? Analytics Brief and Performance Report pull insights from your connected accounts and present them in a clean, readable format. No API wrangling, no maintaining fragile integrations.
The philosophy behind aubado is simple: check once a day, stay in control, then close the tab and do your real work. The admin is handled. Your morning is yours.
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