This sponsored article by Circana explores AI’s role in marketing mix modeling.

For most of marketing’s history, measurement has answered a narrower question than the one marketers actually need answered. It’s told us that two things moved together—spend went up, sales went up—and left us to assume a connection. That’s correlation. What advertisers need, especially now that every dollar is scrutinized, is causation: proof that a specific action drove a specific result.

That shift is why marketing mix modeling (MMM) is having a moment. MMM is spreading across categories, geographies, and company sizes, well beyond the large-CPG, once-a-year exercise it used to be, and AI is the reason it can keep up with that demand.

I think about AI’s role in MMM in three layers, and each one solves a problem that used to slow modeling down.

The pipeline. Before a model can say anything useful, it needs clean, current data—and assembling that data has traditionally been the slowest part of the job. AI is compressing that timeline dramatically, automating acquisition and cleaning so measurement teams spend less time wrangling spreadsheets and more time interpreting results.

The model itself. Machine learning is changing how models are designed, calibrated, and fixed when they drift. This matters because a model without real-world grounding is just a hypothesis. Calibration is what turns it into evidence. Circana and Google have run thousands of experiments on YouTube alone to calibrate models against actual outcomes—refining accuracy and surfacing investment opportunities that a static, uncalibrated model would miss. Benchmarking against that scale of real experimentation is exactly the kind of remediation AI now handles at speed.

The output. A rigorous model buried in a 40-tab workbook doesn’t change anyone’s Monday. AI assistants, chatbots, and automated storytelling tools are turning model output into something a CMO can act on immediately, not decode over a week.

Google’s decision to open-source its Meridian MMM framework is a useful signal of where the category is headed: toward transparency, not black boxes. Adoption of Meridian has grown roughly 4x in the past year, which tells you the industry is hungry for measurement it can actually interrogate. But a model—open-source or not—is only as good as the data and calibration behind it. That’s where independent, third-party validation earns its place alongside open-source tools, and it’s a big part of why Circana recently integrated Meridian into our Liquid Mix offering, pairing Google’s transparent modeling approach with our own first-party data and 30-plus years of measurement expertise.

Triangulation (reading MMM, experiments, and attribution together) is what keeps organizations from getting three different answers to the same question. AI doesn’t replace that judgment. It removes the friction that used to keep measurement teams from getting to it fast enough to matter.

There’s more to unpack on each of these fronts—the pipeline, the calibration, the reporting layer—than fits here. If you’re building or buying into an MMM practice right now, this is the version of it worth building toward.