• Google is pushing advertisers to simplify complex campaign structures and rely more on AI-driven automation.
  • The move prioritizes automated bidding, audience mixing and consolidated campaign types over granular ad groups.
  • Advertisers worry about loss of control, reduced measurement granularity and transparency concerns.
  • Best practices: test gradually, preserve experiments, and maintain independent measurement before fully migrating.

What Google is pushing — and why it matters

Google Ads is increasingly nudging marketers away from highly segmented, granular campaign setups and toward AI-led automation. That means fewer micro ad groups, merged budgets and letting Google’s automated systems handle targeting, bidding and creative combinations.

Why this is happening: automation promises simpler management and the ability to surface incremental conversions across channels. For many advertisers, the pitch is clear — spend less time “managing” campaigns and more time on strategy. But the shift also changes where control and signal live: from human-crafted structures to machine-driven optimization.

Advertiser concerns: control, transparency and measurement

Marketers and ad tech experts are raising predictable warnings. Granular setups give advertisers fine control over bids, audiences and messaging. Moving to broad, AI-optimized campaigns can blur those lines and make it harder to explain exactly why performance changes.

Measurement becomes harder too. Consolidated campaigns can mix conversion sources, making it difficult to isolate which audience or creative drove results. That complicates optimization decisions and makes accurate incrementality testing more important.

How advertisers should respond

Here are practical steps advertisers can take to protect performance while experimenting with AI-driven campaigns:

1. Test gradually

Split budgets and run automated campaigns alongside your existing granular setups. Compare results for a clear before/after, and avoid “big bang” migrations.

2. Maintain experiments and controls

Use holdout groups or Google’s experiments tools to measure incremental lift. Keep at least some campaigns in a granular setup as a control group.

3. Preserve signal and audience inputs

Feeding audience signals, first‑party data, and high‑quality creative into automated campaigns helps the AI learn faster and aligns automation with your business priorities.

4. Protect measurement and attribution

Invest in independent measurement — incrementality tests, analytics pipelines, or server‑side data — so you’re not solely reliant on platform‑reported metrics.

Bottom line

Google’s push toward simplified, AI‑driven campaigns reflects a broader industry trend: automation is becoming the default. That can unlock efficiency for many advertisers, but it also introduces real trade‑offs in control and transparency. Advertisers who test carefully, keep control groups, and preserve robust measurement will be best positioned to capture AI’s benefits without losing sight of what actually drives their results.

Image Referance: https://www.techbuzz.ai/articles/google-tells-advertisers-to-dump-granular-campaigns-for-ai