Marketing mix modeling (MMM) is approaching an inflection point and the cracks in its foundation are becoming harder to ignore.

Traditional models, built for a simpler media environment, are struggling to keep up with a fragmented ecosystem and inconsistent data inputs. At the same time, marketers are pushing MMM to do more: Move beyond correlation, incorporate a wider range of signals, and deliver faster, more actionable insights.

While AI could help with these challenges, unlocking the full potential of MMM will depend on how the industry addresses its underlying data and measurement issues.

MMM is missing the moment

Historically, MMM has been used to identify correlations between spend and outcomes, but that’s no longer sufficient in a more complex environment.

“I think we need to move away from correlation and more causation,” said Angelina Eng, IAB vice president, measurement center and COE operations, during a recent IAB webinar.

That means incorporating a broader set of signals and influences, including economic conditions, cultural trends, weather, and creator-driven dynamics.

It also requires aligning MMM with other measurement approaches, such as incrementality testing and attribution, while evolving models to meet today’s marketing demands.

“Most [traditional MMM] models were built for a simpler media landscape,” said Eng. “But as we know, today’s ecosystem is far more fragmented.”

That fragmentation directly affects the data feeding MMM, where emerging channels aren’t consistently or cleanly represented in models.

• 61% of marketers say their current MMM approach doesn’t capture performance across all channels, according to IAB’s 2026 State of Data report.
• Gaming, commerce media, and creator/influencer marketing are the top three underrepresented channels in MMM today.

Over time, that creates a cycle where investment flows toward what can be measured, rather than what actually drives results.

AI enables faster, smarter MMM

Advancements in AI will significantly improve MMM capabilities.

“Marketers expect that AI will be able to automate more processes, enable more sophisticated approaches,” said Meredith Guiness, director, research and insights at the IAB.

One of the biggest shifts is speed. AI is turning a traditionally slow, resource-intensive process into a more agile one.

• A quarter of buy-side marketers believe that AI-driven improvements will enable them to run MMM models monthly, with 17% saying it will help them run models more than once a month, per the IAB report
• More frequent updates will help MMM align with planning cycles, allowing for dynamic scenario planning, faster optimization, and responsive budget allocation.

AI-driven improvements could also translate into increased ad spend.

• Buy-side planners said they’d increase spend in underrepresented media channels by an average of 5.6% over the next 1 to 2 years if AI-enhanced MMM became more accurate and trusted, found the IAB report.
• This could represent about $14.5 billion in digital investment and $26.3 billion in total ad spend.

But AI isn’t a fix for everything; it still depends on having the right data.

“AI is only as good as the data that it receives,” Eng said, noting that to fully unlock the value of MMM, marketers must first determine how to organize their data. “We have lots and lots of data. But as an industry, we do not speak the same language. We are not structuring our data sets the same way.”

That’s why 39% of marketers say the industry needs standardized definitions and taxonomies for using AI in advanced measurement and 26% want interoperability standards, per the IAB.

This was originally featured in the EMARKETER Daily newsletter. For more marketing insights, statistics, and trends, subscribe here.