AI Max vs DSA: Advertisers Question Control as Google Responds

Why It Matters

Reduced landing‑page control can hurt relevance and conversion rates for large, structured sites, making the AI Max rollout a pivotal risk‑reward decision for advertisers.

Key Takeaways

• Advertisers miss DSA’s granular URL targeting in AI Max.
• AI Max offers URL rules, page feeds, but lacks “page contains” filters.
• Unsupported DSA rules become read‑only after migration.
• Google plans account‑level content exclusions later this year.
• Shift pushes marketers toward structured feeds and AI‑interpreted labels.

Pulse Analysis

The migration from Dynamic Search Ads to AI Max reflects Google’s broader push for automation, but the trade‑off is evident for advertisers who rely on precise URL targeting. DSA allowed marketers to map campaigns directly onto a site’s hierarchy, using path‑based rules to steer traffic to the most relevant pages. In AI Max, that granular control is replaced by higher‑level inputs such as page feeds and custom labels, which the AI interprets to match inventory. While this reduces manual rule‑building, it also introduces uncertainty about how well the system will replicate the nuanced targeting that many large e‑commerce sites depend on.

Current AI Max capabilities include URL rules, combination logic, and the ability to set inclusions at the ad‑group level and exclusions at the campaign level. However, key DSA features—particularly “page contains” conditions—remain unsupported. For advertisers transitioning now, existing DSA rules are imported as read‑only, meaning they continue to function but cannot be edited. This bridge solution preserves legacy performance in the short term but forces marketers to redesign their targeting strategy around structured data inputs, potentially impacting click‑through and conversion metrics during the adjustment period.

Looking ahead, Google has signaled that more granular controls will return, with plans to add content and title‑based exclusions at the account level later this year. This incremental roadmap suggests a hybrid model where AI‑driven automation coexists with manual precision. Marketers should therefore adopt a phased approach: leverage AI Max’s inventory‑aware features for out‑of‑stock handling while gradually building robust page feeds and label taxonomies. By aligning their site architecture with these structured inputs, advertisers can mitigate performance dips and position themselves to benefit from the promised future enhancements.