The shift is significant because much of a company's most useful search information may never appear on its website. Answers can remain buried in sales conversations, technical consultations, customer support interactions and product specialists' knowledge.

Brzica, founder of Occam Digital and a 2026 brightonSEO speaker, argues that SEO is increasingly becoming a bridge between that internal expertise and the questions customers ask in AI-powered search.

“Before AI can retrieve a company’s expertise, the SEO team has to retrieve it from the company,” Brzica said.

His position comes as Google AI Overviews and generative search platforms such as ChatGPT, Gemini, Microsoft Copilot and Perplexity change how users discover and evaluate information. Gartner reported in 2026 that 51% of surveyed U.S. consumers had changed their research habits because of GenAI, with many using more specific or question-based queries.

Brzica's latest analysis draws on three anonymized Occam Digital client cases covering industrial B2B, consumer legal services and premium architectural products. The businesses differ substantially, but the SEO challenge was similar: customers were asking incomplete questions while the organizations possessed relevant expertise that was not sufficiently structured or discoverable.

According to the analysis, an industrial electrical-equipment client saw Google AI Overview query inclusion rise approximately 700% to 850% between early 2026 and mid-September, alongside a 65% increase in organic conversions during the cited commercial comparison period. A consumer legal-services client recorded an estimated 170% to 200% increase in AI Overview query inclusion year over year, while its intake team reported more informed callers. The architectural-products business saw estimated AI Overview inclusion rise 300% to 400%.

These figures are Occam Digital case-study measurements, rather than independently audited benchmarks, and AI Overview visibility does not necessarily translate directly into traffic or conversions.

The broader market trend, however, is well established. McKinsey reported that about half of consumers surveyed were already using AI-powered search and estimated that AI-powered search could influence $750 billion in U.S. revenue by 2028. The firm also found that brands' own websites can represent only a small share of the sources used by AI-powered search systems.

That creates a different SEO workflow. Technical SEO remains important: sites still need crawlable architecture, coherent internal linking, indexable content and credible external signals. But teams also need to identify what makes a company's expertise distinct.

That can mean interviewing sales teams about recurring objections, asking engineers to explain technical differences, documenting how consultants make recommendations or restructuring product taxonomies around real buyer decisions.

Brzica describes this as a broader six-part SEO responsibility covering technical enablement, customer-demand interpretation, business-expertise discovery, information architecture, authority building and commercial measurement.

The approach also changes the definition of original content. Simply rewriting existing information with different wording does not necessarily create useful expertise for AI search. Instead, organizations need to expose evidence, judgment and knowledge that competitors cannot accurately reproduce without access to the underlying business.

For enterprise marketing teams, that places SEO closer to knowledge management, content strategy, customer experience and marketing analytics. The teams most equipped for AI search may therefore be those capable of connecting search demand with information distributed throughout the organization.

Market Landscape

AI search is not replacing conventional SEO. Gartner's research indicates that consumers continue to use traditional search while increasingly incorporating GenAI into research journeys. In one 2026 survey, only about one-third of respondents considered GenAI chatbots as effective as search engines for learning new information.

Trust is another constraint. Gartner previously found that 53% of surveyed consumers distrusted or lacked confidence in the reliability and impartiality of AI-powered search results.

That makes authoritative, well-structured information increasingly important. For enterprise marketers, the competitive issue is no longer simply whether a page ranks. It is whether a company can become a credible source that search engines and AI systems can understand, associate with relevant entities and use when constructing answers.

Brzica's analysis points toward an emerging SEO model in which technical optimization, business expertise, content architecture and external authority work together. The practical implication is that SEO teams may need deeper access to subject-matter experts, customer-facing teams and product organizations than they traditionally have.

Strategic Outlook

The next phase of enterprise SEO is likely to involve measurement beyond rankings and organic sessions. AI Overview inclusion, brand mentions, citations, entity associations, assisted conversions and qualified customer interactions can provide additional signals about how visibility translates into business value.

That does not make traditional SEO obsolete. Instead, it expands the operating model. Companies using platforms from Google, Microsoft, Salesforce, Adobe and other enterprise technology ecosystems will increasingly need their structured content, customer data and organizational expertise to work together across conventional search and AI-mediated discovery.

For marketing leaders, the central question is becoming less “What keyword should this page target?” and more “What does our organization know that customers need, and can search systems understand it?”

Top Insights

• AI search is broadening SEO: Search teams increasingly need to uncover internal expertise and connect it with customer questions before content reaches the web.
• Enterprise knowledge matters: Sales conversations, technical advice and product expertise can become valuable search assets when accurately documented and structurally connected.
• Visibility needs broader measurement: AI Overview inclusion, mentions, citations and commercial outcomes can complement traditional rankings, traffic and conversion metrics.
• Traditional SEO remains foundational: Crawlability, information architecture, internal linking and authority signals still provide infrastructure for visibility across evolving search experiences.
• AI search favors useful specificity: Companies with distinctive evidence, expertise and product relationships have more opportunities to provide information that generic content cannot accurately reproduce.

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