Forget The Shiny Objects And Focus On Fundamentals

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Havas Media Network's Chief Data and Product Officer, Sharona Sankar-King, sat down with AdExchanger's Allison Schiff to argue that agencies should stop chasing AI hype and get their measurement fundamentals right. Her pitch for what comes next: "large causal models" that go beyond traditional marketing mix modeling (MMM). MMM is the statistical method advertisers use to figure out how much each channel (TV, search, social) actually drove sales.

The real question for operators: is the MMM ceiling Sankar-King describes a genuine procurement shift that measurement vendors need to prepare for, or is this a holdco talking its own book? This is a Type 2 (easily reversible) read. Nobody's signing anything today. The forcing function is soft: the next measurement RFP cycle, not a deadline.

The Market Analyst: This episode is a positioning signal, not a news event. Havas is drawing a line in the sand against the acquisition-heavy platform playbooks at the bigger holdcos. Publicis has Epsilon, Omnicom has Flywheel. Havas is selling "modular, we-partner-not-buy" as a virtue. In plain terms: Havas can't out-spend its larger rivals on data acquisitions, so it's reframing not-buying as principled. That's smart narrative for a #4-ish network. For measurement vendors, the useful read is that a holdco data chief is publicly saying nested MMM has hit its ceiling. That's a demand signal for incrementality and causal-inference products, regardless of whether Havas's own tool is as mature as she claims.

The Skeptic: The whole pitch rests on "large causal models" being a real, deployable category, and Sankar-King undercuts it herself. She calls them "tried and true" with the "biggest logos" in one breath and admits "a lot of causal AI players are not that good" in the next. Those don't reconcile. She's the Chief Data and Product Officer selling this methodology as Havas's differentiator; treat "nine out of ten clients are bullish" as a sales anecdote, not a market stat. The most honest thing she said cuts against her own pitch: only ~7% of corporations run fully autonomous AI (a Bain figure the transcript doesn't source). The hype gap is real, but so is the fog around what's actually shipping.

The Operator: Strip the strategy and ask what a planner does Tuesday. The concrete win she cited ("saved over a month of time for one planner") is a workflow automation story, not a causal-modeling breakthrough. That's the tell: the value showing up today is agentic tooling clearing manual grunt work, while large causal models remain aspirational. In plain English: the robot is doing the boring spreadsheet work, not rewriting the science of measurement. The universal "Prof AI" training mandate is the more durable signal. When non-technical staff are required to be AI-literate, vendors whose products need a specialist to operate get displaced faster. Build for the planner, not the data scientist.

The Customer / End User: Two customers here. The advertiser wants transparency and a straight answer on what worked; Havas's "black book, not black box" pitch speaks directly to CMOs burned by opaque systems. But those same advertisers pour budget into Google's Performance Max and Meta's Advantage Plus. Those are the most opaque AI black boxes going, and Sankar-King's answer is essentially "treat them as one slice of the funnel." That's a dodge dressed as strategy. The client's real problem is unchanged: they can't see inside the walled gardens, and no causal model fixes that. The measurement vendor customer, meanwhile, hears a higher methodology bar coming. A good thing if your incrementality math is real.

Where they part ways: The Market Analyst sees a savvy demand signal worth acting on; the Skeptic sees a vendor conflating a real workflow win with an unproven modeling category to sell a differentiator. The Operator splits the difference. The automation is real and shipping; the causal-model revolution isn't. And the Customer tension is the starkest of all: you can't sell "full transparency" while your clients' biggest spend sits inside black boxes you don't control.

What this actually hinges on: whether "large causal models" become a named line item in real measurement RFPs, or stay a boutique differentiator that a handful of holdcos market. The council leans skeptical on the category maturity but takes the underlying demand signal seriously: sophisticated advertisers genuinely are frustrated with nested MMM's limits, and that frustration will pull budget toward whoever can credibly claim causal, incremental measurement.

For vendors: don't rebuild your pitch around "causal AI" as a buzzword. The buyers can smell it. Do tighten your incrementality methodology and your explainability, because the bar in the next RFP cycle is rising. For publishers and data partners pitching holdcos: know whether you're walking into an acquisition shop or a partner-integration shop, and tailor the integration story.

Impact of this specific episode is moderate and indirect. No M&A, no earnings, no regulation. It's calibration, not a trigger.

No high-conviction prediction this week.

The episode is a philosophy conversation, not an event with an observable outcome. "Large causal models enter mainstream RFPs" is directionally plausible but has no clean anchor date or falsifiable threshold I'd stake a call on. The source is a vendor talking her own book, which is exactly the kind of signal the framework tells us not to launder into a confident prediction.