Only 22% of marketers have fully integrated AI search and SEO into their work, according to a new study from Semrush. That small group is already pulling ahead of everyone else.

The number is the story. More than three quarters of marketing teams have not completed the integration of AI-driven search behavior into their SEO operations. Meanwhile, the minority that has done it reports a measurable gap opening up — the study's own framing is blunt: "They're pulling ahead."

For a trade that spent two decades optimizing for classic blue links, that is an uncomfortable split. AI-powered search — answer engines, chat interfaces, and AI summaries that sit between the user and the traditional results page — changes what "ranking" means. The 22% figure suggests most teams are still working out how, or whether, to retool.

What does the 22% figure actually measure?

The Semrush study draws a line between marketers who have fully integrated AI search considerations into their SEO practice and everyone else. "Fully integrated" is the operative phrase.

The distinction matters because partial adoption is easy to mistake for real adoption:

• Running the old keyword-and-backlink playbook while occasionally checking AI chat outputs is not integration.
• Treating AI search as a side experiment owned by one person is not integration.
• Rebuilding content strategy, measurement, and workflows around how AI systems source and cite answers is.

The study's headline finding is that the group on the right side of that line is small — roughly one marketer in five — and that it is gaining ground on the rest.

Why is the gap widening?

Search behavior has shifted faster than most marketing orgs can restructure. When users ask a question and an AI system answers it directly, the click never happens. Content that feeds those answers still earns visibility, but of a different kind: cited, summarized, or absorbed rather than clicked.

Teams inside the 22% have reorganized around that reality. Teams outside it are still measuring success in traffic terms that understate what they are losing. That measurement mismatch, more than any single tactic, explains why the gap compounds — the early movers can see what is working, and everyone else is watching a dashboard that lags the market.

There is also a skills and tooling dimension. Integration requires new audit practices, new content formats, and new assumptions about how visibility is earned. Organizations that treat this as a routine update to existing SEO checklists tend to stall; the ones that treat it as an operational change move.

Who should care most?

The finding lands hardest on three groups:

• In-house SEO and content teams at brands that depend on organic search acquisition, where AI answers now intercept demand upstream of the site.
• Agencies whose clients will ask, increasingly loudly, why their AI search presence looks thinner than a competitor's.
• Martech buyers evaluating tooling, since vendor claims around "AI search readiness" are proliferating faster than evidence.

On that last point, a note of caution. A single 22% statistic does not tell you which specific tactics produce the pull-ahead effect, how large the gap is in revenue terms, or how it varies by industry. Buyers should ask vendors for the study's underlying methodology before rewriting a budget around it.

What comes next?

The practical read for operators is straightforward. If your team sits in the 78%, the study implies the cost of waiting is no longer hypothetical — competitors who moved early are consolidating advantage while integration is still cheap relative to what it may cost later.

The reasonable first steps are unglamorous: audit how AI systems currently represent your brand and content, identify which queries in your category get answered without a click, and assign clear ownership for AI search within the existing SEO function.

The 22% will not stay at 22%. The question the study leaves open is whether the rest close the gap by integrating, or by losing the ground first and reacting after.