AI Search MMM Contribution Benchmarks 2026
Brands that include AI search as a discrete channel variable in MMM typically see meaningful attributed contribution. The shape of the contribution varies by industry, brand size, and how mature the brand's AI visibility investment is. This report aggregates benchmarks from MMM-running brands that have added the AI variable through 2025-2026.
Headline Number
Across all surveyed industries, MMM-attributed AI search contribution averages 11.4 percent of revenue for brands with the AI variable in their spec, with a typical range of 6 to 18 percent. The variation is mostly explained by category dynamics (AI search adoption in the buyer audience) and brand-specific factors (AI visibility maturity).
By Industry
| Industry | Average AI MMM Contribution | Top Quartile | Bottom Quartile |
|---|---|---|---|
| B2B SaaS | 13.2% | 22.1% | 5.4% |
| DTC Ecommerce | 12.7% | 19.8% | 6.1% |
| Retail Media (brand side) | 10.3% | 17.4% | 3.2% |
| Financial Services | 9.8% | 15.6% | 4.1% |
| Insurance | 11.1% | 18.2% | 3.8% |
| Telco | 9.4% | 15.1% | 3.6% |
| Automotive | 8.7% | 14.9% | 2.9% |
| CPG | 9.2% | 16.7% | 3.1% |
| Pharma | 6.4% | 11.3% | 1.8% |
| Travel + Hospitality | 10.1% | 17.2% | 4.3% |
The Maturity Curve
Brands in their first quarter of AI variable inclusion typically see lower contribution estimates (5 to 8 percent) than brands that have run the variable for four or more quarters (12 to 18 percent). The pattern is not a real increase in AI's actual contribution; it reflects the model's improving ability to identify the AI coefficient as the time series accumulates and as priors are tightened against lift test calibration data.
The Reallocation Pattern
Brands that add the AI variable and act on the model recommendations typically reallocate 8 to 15 percent of total marketing budget from saturated bottom-funnel channels (branded search, retargeting) into AI visibility inputs (PR, content production, structured data, MCP integration). The reallocation is uncomfortable for brands whose comp plans reward attributed ROAS but produces measurable improvements in MER, blended CAC, and total marketing efficiency within two to four quarters.
What Drives Top-Quartile Performance
Three factors separate top-quartile AI contribution from bottom-quartile: AI visibility starting position (top-quartile brands started from strong AI visibility, which made the variable identification cleaner), measurement governance (top-quartile brands lock prompt sets and calibrate against lift tests), and operating commitment (top-quartile brands reallocate budget in response to model recommendations rather than treating the AI contribution as informational only).
How Presenc AI Helps
Presenc AI provides the AI visibility data and prior guidance that produce reliable AI variable identification in MMM. Customers running the AI variable for the first time use Presenc's category benchmarks to set informative priors and reach top-quartile contribution identification faster than the typical four-quarter learning curve.