Make AI Max work for your business with new testing and planning tools.

Summary

Google Ads is enhancing its experimentation and planning capabilities, providing ad ops decision-makers with powerful new tools to optimize campaign performance. The upcoming A/B testing feature, rolling out in September, allows advertisers to test different budgets and ROI targets across multiple Search campaigns in a single experiment. This simplifies the process of understanding how scaling impacts overall performance and bottom-line results, enabling data-driven decisions about resource allocation.

Additionally, AI Max experiments now support brand and location controls, addressing a key limitation for advertisers who rely on specific targeting parameters. This update ensures that tests can be run without compromising these guardrails, giving confidence in the results while maintaining brand safety and geographic relevance. Performance Planner has also been upgraded to provide clearer visibility into how changes, such as bidding strategies or budget adjustments, may affect existing campaign performance.

The new one-click apply feature streamlines the process, allowing advertisers to implement suggested changes directly from the planner, reducing friction and accelerating optimization cycles. These advancements underscore Google's commitment to providing robust, actionable insights through AI-driven solutions. For those seeking to scale performance with Google AI and stay abreast of industry best practices, the Rethink 2026 event offers a virtual front-row seat to learn from experts and gain strategic guidance.

Ad ops leaders should leverage these tools to enhance campaign agility, improve ROI, and maintain a competitive edge in an evolving digital landscape.

Analyst Note

What's notable here is Google’s push to close the loop between planning and experimentation. For years, budget scaling and ROI targets were treated as top-down decisions, rarely subjected to rigorous testing because of the operational complexity. Allowing A/B tests specifically on budget and ROI across Search campaigns introduces a more scientific approach to one of the most sensitive levers in paid media.

The key implication for UA and monetization teams is that budget allocation can now be validated with the same statistical rigor as creative or audience tests, potentially reshaping how incremental spend is justified. Equally important is the removal of a long-standing limitation: running experiments while preserving brand and location controls. This suggests Google is responding to advertiser concerns that testing AI-driven strategies from a constrained baseline diluted results.

The Performance Planner one-click application further reduces friction, hinting at a broader industry shift toward automated, closed-loop optimization where insights directly translate into campaign changes. Worth watching is whether this signals a wider trend of platforms making experimentation infrastructure more native, rather than relying on third-party testing tools. As privacy constraints limit traditional measurement, this type of integrated experimentation may become a competitive differentiator.