AI visibility is now a board-level concern for major U.S. brands, but the tools to measure it are not keeping up. A new TransUnion study, surfaced by Search Engine Journal, found that 89% of senior marketing leaders expect their AI-enabled marketing investment to grow over the next 12 to 24 months, yet fewer than half (48%) say they have enough visibility into platform-level AI to make optimization decisions with confidence.
What Did TransUnion’s Study Actually Find?
TransUnion commissioned United Talent Agency’s brand advisory division to survey 100 senior marketing and technology leaders at major U.S. brands. The headline finding is what TransUnion calls a “confidence-readiness paradox”: brands are betting bigger on AI while simultaneously lacking the infrastructure to track whether those bets are working.
- 89% of respondents expect their AI-enabled marketing investment to grow over the next 12 to 24 months.
- 64% say they are confident they will hit their AI goals.
- Only 42% rate their organization’s people readiness as high.
- Only 36% say the same about data and process readiness.
- Fewer than 48% have enough visibility into platform-level AI to make confident optimization decisions.
Matt Spiegel, Executive Vice President of TruAudience Growth Strategy at TransUnion, told Search Engine Journal that if he had to rank the three gaps the study identifies, data comes first. “AI can compensate for a lot of things, but it can’t compensate for incomplete or disconnected data,” Spiegel said. “If you’re feeding AI incomplete information, you’re going to get outcomes that are less predictive than you hoped.” His argument is that people and process problems are downstream of the data problem: once customer records, transaction history, and behavioral signals are connected, governance and skills follow. Without that foundation, everything else gets harder.
Is AI Search Part of This Measurement Problem?
Yes, and the data makes the case clearly. According to the TransUnion study, 69% of respondents said “walled garden” blind spots (proprietary platforms that do not share internal data with outside measurement tools) 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 SEJ that AI search fits squarely inside this pattern, not outside it. “AI search is another example of why independent measurement is becoming more important,” he said. As consumers increasingly discover brands through AI-generated answers rather than traditional search results, marketers lose visibility into how those recommendations are formed and what influenced them. He described this as a natural extension of the walled garden problem, not a separate one.
The implication, as SEJ’s Greg Jarboe frames it, is that every AI citation and GEO (Generative Engine Optimization, the practice of optimizing content so AI systems cite or recommend a brand) study currently being published is attempting to reverse-engineer visibility into a system that was never built to be measured from the outside. The TransUnion data, drawn from 100 senior leaders with no stake in the SEO industry’s internal arguments, provides independent confirmation that this structural blindness appears wherever AI mediates a customer decision.
What This Means for AI-Search Visibility
This section reflects Hingewise’s analysis and is separate from the TransUnion study findings.
Here is the simplest way to frame it: knowing your brand gets cited by ChatGPT or Gemini is not the same as knowing why it got cited, or whether it drove any business outcome. That gap is currently nearly universal, and the TransUnion data is the clearest third-party evidence to date.
Think of it this way. A retailer runs ads across paid social, connected TV, and Google Search simultaneously. A customer then buys through an AI-generated product recommendation. Which channel gets credit? No existing analytics system answers that cleanly. AI search adds one more unmeasured zone to a customer journey that was already fragmented across platforms.
What the TransUnion data suggests is that this is not a niche SEO tooling problem waiting for the right dashboard. It is the same measurement failure that CMOs are already naming in budget meetings, applied to a new channel. That distinction matters for how teams position the work internally. Jarboe’s point in SEJ is well-taken: SEO teams that frame GEO as part of the company-wide measurement gap the CMO already recognizes are more likely to get funded than those asking for a citation-tracking tool in isolation.
Spiegel’s advice, as reported by SEJ, cuts against a common instinct: spend the first dollar on data quality and identity resolution (the process of connecting the same customer’s records across different systems), not on another application. A citation-tracking tool built on top of fragmented analytics will report activity, not impact.
The harder and more durable question is whether any given AI citation is influencing a decision that would not have happened otherwise. That requires incrementality testing (comparing outcomes for audiences who encountered AI citations versus those who did not), which only a minority of marketers currently run, per the TransUnion findings. Teams that build this measurement capability now, before it becomes the industry standard, will be better positioned when executive scrutiny arrives.
Spiegel’s closing observation, as reported by SEJ, is worth holding onto: the biggest mistake he sees is leaders asking “what’s our AI strategy” instead of “what business problem are we solving.” The same reframe applies directly to AI visibility work. Citation volume is a metric. The business outcome those citations are supposed to move is the actual question, and most teams have not formally answered it yet.
What to Check Before Investing in AI Search Measurement
- Confirm whether your customer data (CRM, transaction records, behavioral signals) is unified enough to attribute any channel, including AI search, to measurable outcomes.
- Audit existing cross-channel tracking to understand where blind spots already exist before adding AI search to the mix.
- Define which specific business outcome AI citations are supposed to influence (traffic, leads, revenue, brand consideration) before selecting a measurement tool.
- Check whether your team currently runs incrementality testing for any other channel. If not, AI search is a difficult place to start cleanly.
- Identify who owns AI search measurement across your organization: SEO, brand, paid media, or analytics. The TransUnion data suggests this ownership is often unassigned.
- Decide whether citation volume or citation-to-conversion lift is the KPI being reported to leadership, since these require different infrastructure and tell very different stories.
What to Watch Next
The methodological gap in AI citation research is unlikely to close quickly. Platforms including Google, OpenAI, and Perplexity do not expose citation logic through standard analytics integrations, which means third-party measurement will remain indirect for the foreseeable future. Whether the industry develops shared incrementality frameworks, or whether platforms begin releasing structured citation data, is the fork in the road worth watching over the next 12 to 24 months.
Lam Nguyen · Hingewise
Sources: Search Engine Journal, Greg Jarboe | TransUnion/United Talent Agency survey, 100 senior U.S. marketing and technology leaders