SMEC Data: Unlocking AI’s True Performance Potential

To truly understand the impact of Google’s AI Max for Search campaigns, we must look beyond official benchmarks and examine real-world performance data. A recent analysis of over 250 active campaigns provides a clearer, more nuanced picture of how this feature operates within mature ecommerce accounts, revealing critical insights for advertisers seeking to balance growth with efficiency.

One of the most significant operational findings is how AI Max is frequently deployed alongside other automated campaign types. The data shows a surprising level of redundancy, with nearly half of the advertisers testing AI Max also running Dynamic Search Ads and Performance Max campaigns simultaneously. This creates a complex landscape where these expansion-focused campaigns can compete for the same queries, potentially fragmenting conversion data and complicating performance analysis for Smart Bidding algorithms. While Google advises focusing on overall business goals rather than overlap, advertisers still require clear campaign structures to maintain essential visibility into their conversion sources.

The research also sheds light on how AI Max interacts with keyword match types, challenging a common assumption. Contrary to the belief that it functions as a broad match extension, the data reveals that AI Max most often expands from existing Exact Match keywords, accounting for over 80% of its query expansion. This indicates the feature frequently takes a tightly defined keyword and broadens the set of relevant queries, aligning with Google’s shift toward intent-based matching. This behavior underscores the necessity for diligent search term monitoring to ensure the account isn’t matching against unintended queries outside the original strategy.

Regarding performance, the analysis offers a crucial reality check. While the median revenue uplift of 13% closely matches Google’s non-retail claims, it comes with an important caveat: a median 16% increase in cost per acquisition. This pattern suggests that incremental conversions generated through AI Max tend to cost more than those from a curated core keyword set. As high-intent queries are already captured, additional growth often comes from less predictable or lower-intent searches, following the principle of diminishing returns. Therefore, AI Max acts more as a volume expansion layer than a pure efficiency tool.

Performance outcomes vary dramatically from one account to another. The return on ad spend impact ranged from 42% above baseline to 35% below, with only about one-fifth of campaigns hitting their original ROAS targets. This wide distribution highlights that AI Max performance is highly dependent on individual account structure and configuration. Furthermore, the research uncovered that legacy keyword structures, particularly migrated Broad Match Modified terms, can cause unexpected cannibalization, with AI Max showing high overlap with existing Broad Match queries in some accounts.

The collective data reinforces a fundamental principle known to seasoned advertisers: expansion features can drive volume, but rarely at the same efficiency as a well-managed core campaign. For those testing AI Max, the practical takeaway is to treat it as a controlled expansion layer designed to capture incremental reach, not as a replacement for a solid foundational search strategy built on intentional keyword management and clear campaign structure.

(Source: Search Engine Journal)