How to Audit and Escape Google Ads Dependency with AI Prompts

Google gets away with it. A U.S. federal court ruled in 2024 that Google illegally maintained a monopoly in the search advertising market, and the remedies being discussed won't lower your CPCs next quarter. What happens in the meantime? Google raises prices, you pay them, and your competitors do the same — because none of you have anywhere else to go at scale. At least, that's the assumption most ad budgets are built on.

That assumption is worth testing. Most marketers have no idea what percentage of their total ad spend flows to Google because they only look at Google's own dashboard. The moment you calculate that number across every platform you run, the risk becomes visible. And once the risk is visible, you can do something about it.

This article gives you three copy-paste AI prompts that do exactly that: (1) audit your true Google dependency, (2) pull cross-platform performance into one executive-ready report, and (3) generate data-driven reallocation scenarios. No new software required. Works in Claude, ChatGPT, or any LLM with API access.

Why Google Can Keep Raising Prices (and What That Means for Your Budget)

The antitrust ruling established that Google controls a dominant share of the search ad market, and dominance in search ad distribution is not the same thing as dominance in social, retail media, or connected TV. Those other channels exist, and in many cases they're cheaper on a ROAS-adjusted basis. But most advertisers never find out because they never compare.

Hidden dependency is the actual problem. You might think you're diversified because you run Meta and TikTok. But if Google represents 70% of your total spend, you're not diversified — you're just running two side experiments while your main budget sits entirely in the hands of a company a federal court just called a monopolist.

The fix isn't panic-shifting budget to TikTok. It's measuring first. Here's the prompt that does it:

# Prompt: Calculate Google Dependency Score
You are an AI marketing analyst. Pull the total ad spend for the last 90 days from:
 • Google Ads (account ID: {{google_id}})
 • Meta Ads Manager ({{meta_id}})
 • TikTok Ads ({{tiktok_id}})
 • Amazon Advertising ({{amazon_id}})
 • LinkedIn Campaign Manager ({{linkedin_id}})
 • Pinterest Ads ({{pinterest_id}})
Return a table with each platform's spend, then compute:
 - Google % of total spend
 - "Dependency Score" = (Google % ÷ number of platforms used) × 100
Highlight if the Dependency Score > 60.

A Dependency Score above 60 means the majority of your paid acquisition is controlled by a single platform with no obligation to keep your costs stable. That's the number you need to see before any other decision gets made.

Prompt 1: Calculate Your True Google Dependency Score

The prompt above gives you the concept. This section shows you how to make it work with real API connections and why the setup details matter.

Why normalization isn't optional

If you're running campaigns across multiple currencies, or comparing a Google campaign with a 7-day attribution window against a Meta campaign using 28-day click attribution, the raw spend numbers will lie to you. Before the model calculates any ratio, it needs to convert everything to a common currency (USD is the standard) and you need to decide on one attribution window and stick to it across all platforms. Build that instruction into the prompt.

Here's the before/after that shows why prompt structure matters:

Before (generic request):

Get my ad spend from Google, Meta and TikTok.

After (structured prompt that actually returns a usable number):

You are an AI data-engineer with access to the following APIs:
 - Google Ads API (token: {{ga_token}})
 - Meta Marketing API (token: {{meta_token}})
 - TikTok Business API (token: {{tt_token}})
Retrieve total spend for each platform from 2024-01-01 to 2024-03-31,
convert all amounts to USD using mid-market rates, align to a 7-day
click attribution window, and output:

| Platform | Spend (USD) |
|----------|-------------|
| Google   | $XXX,XXX    |
| Meta     | $YY,YYY     |
| TikTok   | $ZZ,ZZZ     |

Then calculate:
 - Google % of total spend
 - Dependency Score = (Google % / 3) × 100
Flag any platform where spend is zero (possible missing API connection).

One prompt replaces three manual dashboard pulls, eliminates the unit-mismatch problem, and gives you a single risk metric you can actually act on. You still decide what to do with the number. The AI handles the grunt work of pulling and normalizing the data.

For teams building this into a repeatable workflow, the Google Sheets Canvas prompt templates on this blog show how to pipe AI output directly into a shared spreadsheet your whole team can track over time.

Prompt 2: Pull Cross-Platform Performance in One Shot

Knowing your Dependency Score tells you the risk. Knowing your cross-platform ROAS tells you the opportunity. These are two different questions and you need both before moving a dollar.

The unified KPI problem

Every ad platform defines metrics slightly differently. Google reports conversion value. Meta reports purchase value. Amazon reports attributed sales. Getting these into one comparable table manually takes a good analyst most of a morning. This prompt does it in one call:

# Prompt: Cross-Platform Performance Dashboard
You are an AI marketing analyst with read access to the ad APIs for
Google, Meta, TikTok, Amazon, LinkedIn, and Pinterest.

For each platform retrieve the following metrics for the last 30 days:
 • Spend (USD)
 • Revenue (USD) — use each platform's native revenue/conversion-value field
 • ROAS = Revenue / Spend
 • CPA (Cost per Acquisition)
 • CTR (%)

Output a markdown table sorted by ROAS descending. Then write a
two-sentence executive summary that identifies:
 - Which platform delivers the highest ROAS.
 - Any platform with CPA > $150 that needs immediate attention.

Note any platform where revenue data is unavailable due to API
limitations — do not estimate or fill in missing figures.

That last instruction matters. You want the model to flag gaps, not invent numbers. If Amazon's attribution API isn't returning revenue data, you need to know that — not get a plausible-looking figure that skews your reallocation decision.

The output gives you a board-ready snapshot. The markdown table drops straight into a PowerPoint or a Notion page. The two-sentence summary is already written for whoever doesn't have time to read the table.

Prompt 3: Generate Smart Reallocation Scenarios

This is where the audit becomes a decision. You have your Dependency Score. You have your cross-platform ROAS. Now you ask the model to show you what happens if you shift budget.

The key design choice here is to model three scenarios, not one. One scenario is a recommendation. Three scenarios are a decision framework, and it keeps you in the driver's seat — you choose which projection matches your actual business priorities (growth vs. margin vs. risk reduction).

# Prompt: Budget Reallocation Scenarios
You have the following current spend data (from Prompt 1 and Prompt 2):
 • Google: $120,000 – ROAS 4.2
 • Meta: $80,000 – ROAS 5.0
 • TikTok: $30,000 – ROAS 6.3
 • Amazon: $20,000 – ROAS 3.8
 • LinkedIn: $10,000 – ROAS 4.9
 • Pinterest: $5,000 – ROAS 5.7

Create three reallocation scenarios:
 1. Shift 10% of Google spend to TikTok.
 2. Shift 15% of Google spend evenly across Meta, LinkedIn, and Pinterest.
 3. Reduce Google by 20% and distribute equally among all non-Google platforms.

For each scenario calculate:
 - New spend per platform
 - Projected revenue (new spend × historical ROAS)
 - Incremental profit vs. current baseline

Assume ROAS remains constant at historical levels (this is a projection,
not a guarantee). Flag any platform where scaling spend by more than
50% above its current level may compress ROAS due to audience saturation.

Summarize the best-performing scenario in one bullet point.

That saturation flag is the piece most marketers miss. TikTok at $30k/month might have a 6.3 ROAS. TikTok at $90k/month probably doesn't — the algorithm has to work harder to find incremental conversions. The prompt asks the model to flag this honestly rather than project linear returns into unrealistic territory.

The AI tells you which scenario maximizes incremental profit on paper. You decide whether the business can absorb the transition period, whether your creative team can scale TikTok output fast enough, and whether the timing makes sense against your promotional calendar. That's the part only you can do.

If you're thinking about platform risk more broadly, the post on building an AI stack without single-vendor lock-in covers the same principle applied to your AI tooling — worth reading alongside this workflow.

The Full Workflow: Five Steps from Audit to Action

All three prompts are available as saved, reusable templates in Ultra Prompt's Marketing and Advertising category. You attach your API credentials once; the workflow runs without copy-pasting. For agencies managing multiple clients, the business verticals page shows how to run the same audit across separate accounts in parallel.

Frequently Asked Questions

How do I know if I'm spending too much on Google Ads?

Use Prompt 1 to calculate your Google Dependency Score. A score above 60 means Google is consuming the majority of your paid budget relative to the number of platforms you're running. That concentration is a cost-risk indicator, not necessarily a performance problem — but it means a Google price increase or policy change hits you much harder than a diversified account.

What AI tools can pull ad data from multiple platforms at once?

Claude and ChatGPT (with appropriate plugins or API access) can query Google Ads, Meta, TikTok, Amazon, LinkedIn, and Pinterest in a single prompt when you supply API tokens. Ultra Prompt's native connectors let you store those credentials once and reuse them across saved prompt packs without re-entering them each session.

Can I use ChatGPT or Claude to compare Google Ads performance with Meta, TikTok, and Amazon?

Yes. Prompt 2 requests spend, revenue, ROAS, CPA, and CTR for each platform and returns a markdown table sorted by ROAS plus a plain-language executive summary — all in one LLM call. The key is instructing the model to flag missing data rather than estimate it.

How do I automate cross-platform ad reporting without expensive software?

Save Prompt 2 as a scheduled Ultra Prompt workflow. Set it to run on a weekly cadence, route the output to your email or a shared Notion doc, and you have a reporting layer that costs a fraction of a dedicated analytics SaaS. Historical runs stack automatically, so you can spot trends without building a data warehouse.

What prompts help me reallocate budget away from Google Ads?

Prompt 3 generates three concrete reallocation scenarios, applies each platform's historical ROAS to project incremental revenue, and summarizes the highest-performing option in a single bullet. It also flags where scaling assumptions may be unrealistic due to audience saturation — which is the gap most manual budget models miss entirely.

The Bottom Line

Google gets away with it because most advertisers never measure how dependent they actually are. Three prompts change that: one to measure the risk, one to see the full performance picture, one to model the exit. You still make the call. The AI just makes sure you're making it with real numbers instead of guesses.

If you're ready to run the audit, Ultra Prompt's Cross-Platform Reporting prompt pack has all three prompts pre-built and ready to connect to your accounts.