AI Safety and Guardrails Shape Commerce Platform Policy
GoogleAI Max SearchDemand GenGoogle AdsPerformance Max

Google Ads AI requires guardrails to control targeting and assets

Google Ads AI can dynamically create ad copy and select landing pages, but advertisers must use text guidelines, asset optimization, and account-level controls to prevent the system from targeting unintended audiences or displaying unwanted content. Commerce teams need these guardrails to align AI-driven campaigns with brand positioning and avoid budget waste on misaligned audience segments.

Google Ads AI capabilities can better target audiences and unlock new opportunities when paired with first-party data and conversion tracking. However, advertisers must actively instruct the AI who to avoid, not just what to target. Traditional negative keywords alone are insufficient in intent-driven search—a searcher looking for a cheap laptop case might search only for "laptop cases," bypassing keyword exclusions.

Advertisers can establish AI guardrails through text guidelines and asset optimization, both available in Performance Max and AI Max Search campaigns. Text guidelines inform Google's AI about target prospects and messaging restrictions, such as excluding price-sensitive language or competitive comparisons. Asset optimization allows advertisers to disable automated image and video enhancements and set text and URL exclusions to prevent landing on clearance pages for premium items. Additionally, account-level automated assets—including sitelinks, callouts, images, and automated promotions—can be toggled on or off to prevent inaccurate branding or budget leakage. Optimized targeting and audience expansion features are enabled by default in Search, Demand Gen, and Video campaigns but should only be activated after gathering sufficient conversion data to train the AI properly.

For commerce practitioners, these controls are essential to prevent AI from diluting campaign performance through unintended audience expansion or brand-misaligned messaging. The key strategy is to train the AI first with guardrails in place, then gradually enable broader targeting options once conversion patterns are established.

Sources:1 report