How TransUnion’s AI Findings Should Change the Way SEOs Pitch GEO via @sejournal, @gregjarboe

Marketers are more confident about AI than they have ever been, and less able to prove it’s working. That’s the real story buried in a new TransUnion study, and it lines up almost exactly with what I’ve been finding while digging into the methodological gaps in B2B AI citation research this month. The same blind spot showing up in how brands measure AI-driven marketing is showing up in how they measure AI search visibility.

Marketers Are Confident in AI, but Not Ready to Measure It

TransUnion commissioned United Talent Agency’s brand advisory division to survey 100 senior marketing and technology leaders at major U.S. brands. The company is calling the result a “confidence-readiness paradox.” Eighty-nine percent of marketers expect their AI-enabled marketing investment to grow over the next 12 to 24 months, and 64% say they’re confident they’ll hit their AI goals. But only 42% rate their organization’s people readiness as high, and just 36% say the same about data and process readiness. Fewer than half, 48%, say they have enough visibility into platform-level AI to make optimization decisions with confidence.

I asked Matt Spiegel, executive vice-president of TruAudience Growth Strategy at TransUnion, to rank the three gaps the study identifies.

“If I had to rank them, I’d put data first,” Spiegel told me. “AI can compensate for a lot of things, but it can’t compensate for incomplete or disconnected data. If you’re feeding AI incomplete information, you’re going to get outcomes that are less predictive than you hoped.”

Spiegel’s argument is that people and process problems are downstream of the data problem. Once customer records, transaction history, and behavioral signals are actually connected, governance and skills follow. Without that foundation, he said, everything else gets harder.

AI Search Is Part of a Larger Measurement Blind Spot

Where this stops being a general marketing-ops story and starts being an SEO story is in what’s driving the visibility gap. Sixty-nine percent of respondents said walled garden blind spots limit their ability to evaluate AI’s effectiveness, and 70% said cross-channel blind spots make it hard to track AI’s impact across the customer journey. Spiegel told me the pain isn’t concentrated in any single platform. Marketers are running paid social, retail media, connected TV, search, and owned channels simultaneously, and no single environment shows the whole customer journey.

I pushed him on whether AI search sits inside that same problem or outside it, since that’s the exact fight I’ve been having with the current wave of AI citation studies. His answer confirmed what I suspected. “AI search is another example of why independent measurement is becoming more important,” he said. As consumers increasingly discover brands through AI-generated answers instead of traditional search results, marketers lose visibility into how those recommendations get created and what influenced them. He called it a natural extension of the walled garden problem, not a separate one.

Every AI citation and GEO study getting published right now, including ones that I’ve written about, is trying to reverse-engineer visibility into a system that was never built to be measured from the outside. TransUnion just handed me independent confirmation, from 100 marketing leaders with no stake in the SEO industry’s internal arguments, that the same structural blindness is showing up everywhere AI mediates a customer decision. If you can’t see inside a retail media auction, you can’t see inside an AI Overview citation either. It’s the same missing instrumentation, just pointed at a different channel.

Treat GEO as a Business Measurement Problem

This should change how SEO practitioners talk about GEO internally. Too much of the current conversation treats AI search visibility as its own isolated discipline with its own tooling problem. Spiegel’s data suggests the opposite. It’s a specific instance of a company-wide measurement failure that CMOs are already naming in board meetings, which means SEO teams that frame GEO as part of that broader conversation are going to get funded faster than ones asking for a citation-tracking tool in isolation.

What SEO Teams Should Do Next

Here’s how to apply that this quarter.

First, stop pitching AI search visibility as a standalone SEO line item. Bring the TransUnion numbers, or your own version of them, into the next budget conversation and frame AI citation tracking as the search-specific piece of a measurement gap the CMO already knows exists. That reframing alone changes who signs off on the spend.

Second, before buying another AI visibility tool, audit whether your existing customer data is connected enough to even use one well. Spiegel’s advice to a team with a fresh AI budget was to spend the first dollar on data quality and identity resolution, not another application. The same logic applies to a GEO tool bought on top of fragmented analytics. It will report activity, not impact.

Third, build incrementality testing into your AI search measurement now, before it becomes the standard everyone else adopts later. Spiegel pointed out that only a minority of marketers currently use marketing mix modeling or incrementality testing for AI, mostly because time and cost savings are easier to report. SEO teams that can show incremental traffic or conversion lift tied specifically to AI search citations, rather than just citation counts, will be the ones with credibility when this gets scrutinized at the executive level.

Spiegel closed our conversation with a line I think is the real headline, more than anything in TransUnion’s press release. The biggest mistake he sees isn’t a bad AI strategy. It’s leaders asking “what’s our AI strategy” instead of “what business problem are we solving.” I’d extend that straight into SEO. The teams still asking “how do we win more AI citations” without first asking what business outcome those citations are supposed to produce are going to end up with a lot of visibility and no way to prove it mattered.

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