We’re expanding beta access to text guidelines for all advertisers globally in AI Max.

Summary

Google is expanding beta access for text guidelines to all advertisers globally across AI Max for Search and Performance Max campaigns, now with full language and vertical support. This feature allows advertisers to define specific terms or concepts to avoid in their own words, ensuring brand standards are maintained. For example, rules like "don't imply our products are cheap" help steer Google AI.

Brands like BYD have seen success, increasing leads by 24% at 26% lower cost while safeguarding brand standards. High-quality creatives drive performance by pairing unique insights with Google AI.

Analyst Note

The expansion of text guidelines to all advertisers globally marks a notable shift in how Google positions its AI capabilities. What's notable here is that Google is responding to a persistent tension in automated creative: the trade-off between scale and brand safety. Until now, advertisers using Performance Max and AI Max often felt they had limited control over how their brand voice was represented in AI-generated copy.

By allowing natural-language rules like 'don't imply our products are cheap,' Google is essentially democratizing the ability to apply nuanced brand guardrails without requiring technical expertise. The key implication for UA and monetization teams is that this feature reduces the risk of brand dilution at scale, which has been a major barrier to fully trusting AI-driven campaigns. In the context of increasing regulatory and internal scrutiny on ad content, especially in verticals like finance and healthcare, this could accelerate adoption of Performance Max among brand-conscious advertisers.

The timing aligns with a broader industry trend where platforms are ceding more creative control to advertisers, even as they push for automation. For ad ops professionals, this means reevaluating creative workflows—rather than relying solely on manual review or keyword blacklists, teams can now embed brand constraints directly into the AI's optimization loop.