B2B marketers struggle to manage brand visibility in AI

Agentcy has published research suggesting most B2B marketing teams lack a clear way to manage how their brands appear in AI-generated answers from tools such as ChatGPT, Gemini, Copilot, and Perplexity.

Its first Annual AI Visibility Index, based on a survey of 104 senior B2B marketing leaders, found that 81% consider AI visibility a blind spot in their marketing intelligence. Of those, 21% said it is a major blind spot.

The findings reflect a shift in how buyers research technology vendors. AI answer engines increasingly summarise categories, compare suppliers, and shape shortlists before a prospective customer visits a company website. Many marketing teams still rely on measurement models built around search rankings, web sessions, and last-touch attribution.

Only 10% of respondents said they can consistently connect AI-driven touchpoints to revenue. Just 12% reported having a dedicated AI visibility tool in live use.

Ownership Gaps

Responsibility for AI visibility appears fragmented across functions. Respondents most commonly placed ownership with Marketing Ops or Analytics (35%). A further 15% put it under SEO or Web, 11% under an AI Officer, and 9% under Brand or Communications. More than one in four (26%) reported no clear owner.

Tracking of AI-driven referrals is also inconsistent. Some 35% said they formally track AI-driven referrals in their analytics stack, while 26% track them manually or inconsistently. The rest do not track such referrals.

Tom Fry, Agentcy's chief technology officer, said the change is more than a shift in marketing channels.

"For twenty years, B2B marketing has been built around the click - rank, earn traffic, attribute impact. AI changes the loop. Buyers can now research, compare, and shortlist vendors without generating a single visit. The buying journey is being condensed into a single conversational interaction where the model generates the criteria and often the shortlist. That is not a channel shift. It is a structural one. And most organisations are not yet built for it."

Mispositioning Risk

The research also highlights what it calls "algorithmic mispositioning": cases in which a brand appears in AI-generated responses but is inaccurately framed. Brands may be associated with the wrong use cases or compared against the wrong competitors.

Two-thirds of respondents (66%) said they have checked how their brand appears in AI answers at least once. Regular monitoring is less common, with only 25% saying they check regularly.

Among those who have assessed their AI positioning, 46% found it mixed or inaccurate. The report links this to how AI systems synthesise publicly available information, including media coverage, analyst commentary, review platforms, and category language. When that external information is inconsistent, AI responses can vary.

"Visibility alone is insufficient if a brand is being misrepresented," Fry said. "Buyers may form early preferences based on how AI summarises strengths, weaknesses, and category fit - often before engaging directly with the brand. Invisibility is a problem you can see. Mispositioning compounds quietly. And most organisations have no consistent monitoring in place to detect it."

Pipeline Influence

Despite uncertainty about measurement, many marketing leaders believe AI tools already influence commercial outcomes. Some 45% said AI influences between 6% and 20% of their pipeline today. Another 43% expect that influence to rise quickly. Only 11% said AI is not relevant to their category.

Several respondents said low or unclear AI referral traffic does not indicate a limited impact. The survey found that 30% believe AI tools influence buying decisions even when clicks are rare. This aligns with a buying journey in which AI systems shape early-stage perception, while traditional attribution captures only later actions such as direct site visits or form fills.

When asked which AI visibility metrics would be most valuable, respondents prioritised qualitative measures over traffic. Positioning accuracy was cited by 37% as a priority metric for 2026, followed by share of voice in AI answers (36%). Another 30% pointed to citations and trusted sources used by AI systems.

Investment Decisions

The research describes the market as being in an "evaluation phase" for AI visibility. Respondents cited practical constraints on investment and execution: 43% pointed to a lack of time and internal resources, 31% to difficulty justifying return on investment, and 30% to not yet understanding which actions influence AI recommendations.

Agentcy plans to publish the AI Visibility Index quarterly and track changes in awareness, measurement, and attribution practices over time.