300 enterprise marketers weigh in on AI search

Three quarters of enterprise marketing executives now treat AI search as a board-level priority, according to a survey of 300 of them published by Search Engine Journal in partnership with Branch. That figure ought to be read with a raised eyebrow. The same cohort, asked a year ago, would have said much the same about the metaverse.

Still, the underlying numbers are harder to dismiss than the enthusiasm around them. The report, drawn from senior marketers at companies with revenues above $250m, finds that budgets, measurement frameworks and org charts are quietly being rewired around generative answers rather than blue links. The interesting question is not whether enterprises believe AI search matters. It is what they are doing about it, and where the gap between conviction and competence is widest.

The conviction-competence gap

The survey's most useful finding is the mismatch between stated priority and operational readiness. A clear majority of respondents say AI search will materially affect their pipeline within twelve months. A much smaller minority can name the prompts their brand is cited in, the models that cite them, or the share of voice they hold against named competitors inside ChatGPT, Gemini, Perplexity or Copilot. Belief is universal; instrumentation is not.

This is the familiar shape of a new channel in its awkward adolescence. Paid search took roughly five years to acquire the measurement vocabulary marketers now take for granted. Generative answers have had eighteen months. The executives surveyed are buying tooling, hiring specialists and commissioning audits at a pace that suggests they know the lag is the risk, not the technology.

Where the budget is actually moving

Three reallocations stand out. First, SEO teams are being rebadged rather than cut, with remits extended to cover model citations and answer-engine optimisation. Second, PR and earned media budgets are being scrutinised through a new lens: not reach, but whether coverage in a given outlet demonstrably feeds the corpora that models draw from. Third, content production is being rebuilt around structured, citable assets (original data, methodology pages, named-expert commentary) rather than the volume plays that defined the content-marketing era.

For financial services firms, the implication is sharpest around regulated content. A bank whose explainer pages are cited by ChatGPT when a prospect asks about SOFR transition or Basel III endgame is, in effect, occupying the position once held by the first organic result. That position is now winner-takes-most: LLMs typically surface two to four sources, not ten. The long tail of "page two" visibility has collapsed.

For multilaterals and UN agencies, the stakes are different but larger. When a policymaker asks Gemini to summarise disaster-risk financing or digital public infrastructure, the institutions cited become the de facto authorities, regardless of who actually convenes the standard. Bodies that do not appear in those answers will find their convening power eroding in slow motion, and they will not see it in any dashboard they currently run.

For major industrial groups, the channel matters most in procurement-adjacent queries: specifications, certifications, sustainability claims. Buyers no longer compare five vendor PDFs. They ask a model to compare them, and the model relies on what it has been trained or retrieved to trust.

The five findings, read sceptically

Search Engine Journal's report groups its data into five headline findings: rising executive priority, budget reallocation toward AI search, measurement immaturity, organisational restructuring, and a shift in KPI definitions away from clicks and toward citations and inclusion rates. None of these is surprising on its own. Taken together, they describe a category in which the early movers are already building the measurement standards that laggards will eventually adopt, and pay a premium to license.

The historical analogue worth holding in mind is not search circa 2004. It is analyst-relations circa 1995, when a small number of firms worked out that being quoted in Gartner reports shaped enterprise buying more than any advertisement. The brands that invested early in the relationship with analysts compounded the advantage for two decades. LLMs are the new analysts, with two differences: they cite far more sources, and they update far more frequently. The compounding window is therefore shorter and the cost of waiting higher.

What the survey does not say

The report is quieter on two questions that ought to concern any CMO reading it. The first is attribution. None of the major models offers reliable, auditable data on which prompts cite which brands, leaving marketers to triangulate through third-party tools of varying quality. The second is durability. A citation pattern that holds in GPT-4o may not survive the next model refresh, and the survey offers no evidence that enterprises are stress-testing their visibility across versions.

Both gaps point to the same underlying truth. Enterprise marketers have correctly identified the channel. They have not yet built the muscles to defend a position in it. The firms that close that gap in 2026 will set the citation patterns that everyone else spends 2027 trying to dislodge.