Since Google introduced AI Max for Search campaigns, most of the discussion has focused on Google’s own benchmarks.
Those benchmarks suggest advertisers can expect meaningful conversion growth without major efficiency changes. But like many platform statistics, they leave open questions about how the feature behaves inside mature accounts.
To get a clearer view, Mike Ryan, Head of Ecommerce Insights at Smarter Ecommerce (SMEC), analyzed performance data from more than 250 Search campaigns using AI Max.
The findings provide a useful reality check for advertisers testing the feature, especially for e-commerce accounts where Google hasn’t published official performance benchmarks.
AI Max Often Runs Alongside Other Automation
One of the first patterns SMEC identified is how AI Max is being deployed in real accounts.
Nearly half of advertisers testing AI Max are also running Dynamic Search Ads (DSA) and Performance Max campaigns at the same time.
That overlap creates a surprising amount of redundancy.
In the dataset analyzed by SMEC:
- 1 in 6 advertisers used AI Max together with DSA
- 1 in 4 advertisers used AI Max alongside Performance Max
- Nearly 50% of accounts ran all three simultaneously
This raises an important operational challenge.
Each of these campaign types is designed to expand reach beyond existing keywords. When they run in parallel, they can compete for the same queries or split conversion data across multiple campaigns.
That fragmentation can make performance analysis harder and may interfere with how Smart Bidding models learn.
Google’s official position is that advertisers should worry less about overlap and focus on business goals. In theory, ad rank determines which campaign ultimately serves the ad.
In practice, though, advertisers still need clear campaign structures to maintain visibility into where conversions are coming from.
Most AI Max Query Expansion Still Comes From Exact Match Keywords
Another interesting finding from Ryan’s research was how AI Max interacts with keyword match types.
After analyzing one million AI Max impressions, the study found the following distribution:
- Exact Match: 80.11%
- Phrase Match: 19.52%
- Broad Match: 0.38%
Many advertisers assume AI Max operates primarily as an extension of Broad Match. Instead, the data shows it most often expands outward from existing Exact Match keywords.
In other words, AI Max frequently takes a tightly defined keyword and broadens the set of queries considered relevant.
That behavior aligns with Google’s broader push toward intent matching rather than strict keyword matching.
However, it also means advertisers need strong visibility into the queries being captured through these expansions.
Without active search term monitoring, accounts may begin matching against queries that were never part of the original keyword strategy.
AI Max Drives More Revenue, But At A Higher Cost Per Conversion
Google’s official messaging around AI Max claims advertisers can expect around a 14% increase in conversions or conversion value at similar efficiency levels.
SMEC’s data provides the first meaningful benchmark for how that claim holds up in ecommerce campaigns.
Across the 250 campaigns analyzed, AI Max generated:
- Median revenue uplift: +13% conversion value
- Median CPA increase: +16%
The conversion value increase lands remarkably close to Google’s non-retail claim.
However, the cost side tells a more nuanced story.
Incremental conversions generated through AI Max tend to cost more than baseline keyword traffic.
As Ginny Marvin explained in response to advertiser questions, incremental volume typically follows the law of diminishing returns. Once high-intent queries are already covered by curated keyword sets, additional growth comes from less predictable or less efficient queries.
In other words, the next marginal conversion will often cost more than the first.
For advertisers, the key takeaway is that AI Max behaves more like a volume expansion layer than a pure efficiency optimization.
ROAS Outcomes Vary Dramatically Across Accounts
While the median ROAS impact of AI Max appears neutral overall, the distribution of outcomes across accounts is unusually wide.
SMEC found performance ranged from:
- 42% above baseline ROAS
- 35% below baseline ROAS
Only 22% of campaigns landed close to their original ROAS targets.
The remaining 78% either overperformed or underperformed significantly.
That suggests AI Max performance is highly dependent on individual account structure, keyword coverage, and campaign configuration.
Legacy Keyword Structures Can Cause AI Max Cannibalization
Another pattern uncovered in the research involves AI Max interacting unexpectedly with existing Broad Match keywords.
In some accounts, AI Max matched against Broad Match queries far more frequently than expected.
Examples included:
- 49% overlap with Broad Match queries in one account
- 63% overlap in another account
SMEC found the root cause often comes from legacy Broad Match Modified (BMM) keywords.
When Google migrated BMM to Broad Match several years ago, many of those keywords continued behaving more like Phrase Match. AI Max then expands on those matches, creating the appearance of overlap.
Cleaning up legacy keyword structures can significantly clarify reporting and reduce confusion when evaluating AI Max performance.
Final Thoughts on AI Max Study
The SMEC data reinforces something most experienced advertisers already understand.
Expansion layers can drive more volume. But that volume rarely comes at the same efficiency as your core keyword set.
AI Max appears to follow that same pattern. The campaigns analyzed saw a median 13% lift in conversion value, but those incremental conversions came at a higher cost.
For advertisers testing the feature, the takeaway is fairly straightforward. Treat AI Max as a controlled expansion layer, not a replacement for the foundation of your Search campaigns.
Those interested in the full analysis can explore SMEC’s complete AI Max guide, which breaks down the methodology and additional findings in more detail.
