AI in Marketing: Hype vs. Reality in Measurement

AI, particularly LLMs, has generated hype around marketing measurement but can deliver wrong, unvalidated analyses in media mix modeling (MMM). LLMs are not designed for causal inference, so attribution results from such models can mislead budget decisions despite confident outputs. Vendors marketing AI-powered MMM may obfuscate methodologies and skip essential validation, increasing risk for enterprise marketers. AI can accelerate model estimation and support peripheral tasks—summarizing outputs, clarifying assumptions, and flagging data anomalies—but it cannot replace core causal modeling or guarantee profit. The article urges using an independent validation framework that emphasizes experimentation, forecast reconciliation, and model-quality checks to confirm true causal signals. Alaska Airlines is cited as adopting an open-source MMM solution; with a nine-figure marketing budget, small forecast errors can cause multimillion-dollar misallocations if tooling is unvalidated. Overall, substantial progress comes from demonstrable ROI rather than AI magic, and firms should demand evidence of profit impact over hype.