August 2026 Google Ads Update – New AI Max Testing Tools

Granular analysis and first look

Google is making it easier for advertisers to evaluate AI Max before committing more budget to it. For businesses focused on measurable growth, these updates add useful testing capabilities to an increasingly automated Search environment. Granular’s PPC management services focus on using tools like these to connect campaign changes to meaningful business outcomes—not just additional clicks.

Test Budget and ROI Changes Across Multiple Search Campaigns

Beginning in September, Google will roll out a new experiment option that lets advertisers test different budgets and ROI targets across multiple Search campaigns within a single A/B test.

This could make scaling decisions easier to evaluate. Instead of increasing budgets or changing targets campaign by campaign, advertisers will be able to test a broader strategy and measure how those changes affect overall performance.

For PPC teams, the biggest opportunity is to use these experiments to answer a more valuable question: Does spending more actually generate profitable incremental growth?

Before starting a test, establish the metrics that determine success, such as conversion value, ROAS, CPA, lead quality or downstream revenue.

AI Max Experiments Add Brand and Location Controls

Google is also expanding AI Max experiments so advertisers can test the feature while continuing to use certain brand and location controls.

That matters for advertisers who may have been hesitant to experiment with AI Max because they depend on tighter campaign guardrails. Google’s existing AI Max controls include brand controls and location-of-interest capabilities designed to give advertisers more influence over where and how automated matching expands reach.

The additional experiment flexibility should make it easier to isolate AI Max’s impact without abandoning those controls during the test.

Performance Planner Recommendations Are Easier to Apply

Performance Planner can now show how changes to bidding or budget targets may affect existing campaign performance and allow advertisers to apply recommended changes directly to campaigns.

The streamlined workflow can save time, but advertisers should still review projections in the context of their own business goals. Forecasted improvements inside Google Ads should be evaluated alongside profitability, lead quality, inventory constraints and other metrics that may exist outside the platform.

Key Takeaways

• Multi-campaign testing is coming: Advertisers will be able to test budget and ROI target changes across multiple Search campaigns beginning in September.
• AI Max testing gains more control: Brand and location settings can remain in place while advertisers evaluate AI Max.
• Scaling can become more deliberate: The new experiments provide a better framework for measuring incremental returns before making broader changes.
• Performance Planner is more actionable: Recommended bidding and budget changes can now be applied directly from the planning workflow.
• Business outcomes still matter most: Automation and forecasts should be judged against profitability and other meaningful performance metrics—not platform recommendations alone.

What This Means for PPC Advertisers

Google continues to reduce the friction between forecasting, experimentation and implementation. That can help advertisers move faster, but it also raises the importance of having a disciplined testing framework.

For most advertisers, the value of these updates will not come from simply turning on AI Max or accepting a larger budget recommendation. It will come from using the new testing tools to understand whether automation and additional investment produce meaningful incremental returns.

As these capabilities roll out, advertisers should treat AI Max experiments as an opportunity to validate performance with their own data before scaling.