What SMEC’s Data Reveals About AI Max Performance via @Sejournal, @Brookeosmundson

Why It Matters

The findings show AI Max can boost sales, yet it adds cost and operational complexity, making it a strategic expansion tool rather than a pure efficiency solution for advertisers.

Key Takeaways

• Half of accounts run AI Max with DSA/Performance Max
• AI Max expands primarily from Exact Match keywords
• Median conversion value rises 13% with AI Max
• Cost per acquisition climbs roughly 16% under AI Max
• ROAS outcomes vary widely; only 22% stay on target

Pulse Analysis

Google’s AI Max represents the latest push toward intent‑driven search advertising, moving beyond traditional keyword reliance. While the platform promises automated expansion, independent data has been scarce. SMEC’s analysis of more than 250 campaigns provides the first real‑world benchmark, confirming that AI Max can generate a modest 13% uplift in conversion value. However, the higher cost per acquisition—about 16% above baseline—highlights the trade‑off between volume and efficiency that advertisers must weigh when scaling e‑commerce spend.

A notable operational challenge uncovered is the frequent overlap of AI Max with other automation layers such as Dynamic Search Ads and Performance Max. Roughly 50% of the sampled accounts ran all three simultaneously, leading to query competition and fragmented conversion data. This redundancy can dilute the learning signals for Smart Bidding algorithms, making performance attribution more opaque. Moreover, AI Max’s expansion behavior leans heavily on Exact Match keywords (over 80% of impressions), contradicting the assumption that it functions like Broad Match. Marketers therefore need rigorous search‑term monitoring to prevent unintended query capture and to maintain control over budget allocation.

Strategically, AI Max should be treated as a controlled volume‑expansion tier rather than a replacement for a well‑structured keyword foundation. Cleaning up legacy Broad Match Modified keywords and clearly separating campaign objectives can reduce cannibalization and improve reporting clarity. Advertisers should track CPA and ROAS closely, recognizing that while median revenue gains are achievable, the distribution of outcomes is wide—only about a fifth of campaigns meet original ROAS targets. Ongoing testing and incremental rollout will be essential to harness AI Max’s upside without eroding overall campaign efficiency.