Agentcy this week published the first edition of its Annual AI Visibility Index, a survey of 104 senior B2B marketing leaders conducted in February 2026 that documents a widening gap between what organisations believe AI is doing to their pipeline and what they can actually prove. The London-based influence intelligence platform, working with PR agency Resonance, released the findings on 5 March 2026 alongside a press release sent directly to marketing media.

The headline number is stark. According to the report, 81% of B2B marketing leaders consider AI visibility a blind spot in their organisation, with 21% describing it as a major one. Despite that near-universal acknowledgement, only 10% of respondents can consistently connect AI-driven touchpoints to revenue. A further 12% have a dedicated AI visibility tool in live use.

For a marketing community that has spent years refining attribution models for paid search, social media and programmatic display, the admission carries weight. The infrastructure problem is not theoretical - it is operational, and it is happening now.

The buying journey is compressing

Tom Fry, CTO of Agentcy, frames the underlying shift in structural terms. "For twenty years, B2B marketing has been built around the click - rank, earn traffic, attribute impact," Fry said in the report's foreword. "AI changes the loop. Buyers can now research, compare, and shortlist vendors without generating a single visit."

The statement captures something that LinkedIn's own marketing team documented in January 2026, when the platform revealed that non-brand awareness traffic had declined up to 60% across a subset of B2B topics. LinkedIn's response was to form a cross-functional AI Search Taskforce and abandon its traditional click-based measurement model. The Agentcy research suggests most other B2B organisations have not moved as fast.

According to the index, buyers are now using ChatGPT, Gemini, Microsoft Copilot, and Perplexity to define categories, compare vendors, and shape shortlists - often before visiting a company website. The discovery process, which once unfolded across dozens of search queries and website visits, is condensing into a single conversational interaction. The AI model generates the criteria. Often, it also generates the shortlist.

That compression has a direct consequence for marketers. Traditional attribution models rely on clicks, sessions, and last-touch data. When the decision is already half-formed before any click occurs, those models become structurally incomplete. The research finds that 26% of respondents believe AI influences decisions without generating any clicks at all.

Ownership is the core problem

One of the more revealing sections of the index concerns who, inside a B2B organisation, is actually responsible for AI visibility. The answers are fragmented. According to the research, 35% of respondents place it under Marketing Ops or Analytics. Fifteen percent say SEO or Web. Nine percent attribute it to Brand or Communications, and 11% to an AI Officer. But 26% - more than one in four - report there is no clear owner at all.

The structural explanation matters. AI visibility sits between functions. According to the report, it is influenced by technical site structure, third-party authority signals, media coverage, category language, and competitive positioning. It touches SEO, PR, brand, and revenue operations simultaneously, but rarely sits cleanly inside any single one of them. Without a designated owner, monitoring becomes inconsistent and positioning issues drift undetected.

That fragmentation has a direct effect on measurement. Measurement confidence across marketing more broadly has been under strain, with over half of marketers reporting unchanged confidence in measurement accuracy year-over-year despite growing data volumes. The Agentcy index adds a specific dimension to that problem. According to the research, only 35% of respondents formally track AI-driven referrals within their analytics stack. A further 26% track manually or inconsistently. The remainder either have no tracking in place, are unsure, or are only planning to implement it.

Sixty percent are attempting some form of measurement, according to the findings. But only a third have embedded it formally. Twenty percent of respondents say they do not know what percentage of their website sessions originate from AI tools at all. Even among those who do track, reported AI traffic shares vary widely - from negligible to more than 10% of sessions.

This creates what the report describes as a guessing game. If AI referral traffic appears low, is that because the brand is genuinely invisible in AI answers? Or because tracking is immature? Without consistent ownership, it is difficult to separate the two.

ChatGPT began appending UTM parameters to links in its "More" section in June 2025, a technical change that improved attribution for some marketers. But the Agentcy data suggests that tool-level fixes have not resolved the underlying governance problem. Attribution infrastructure and ownership accountability are distinct challenges.

Belief far outpaces proof on pipeline

Despite the measurement gaps, confidence in AI's commercial influence is substantial. According to the index, 45% of leaders believe AI already influences between 6% and 20% of their pipeline today. A further cohort believes the share sits between 21% and 50%.

Only 11% believe AI is not relevant to their category at all. That near-universal belief in relevance makes the attribution gap more striking - organisations are convinced the influence is real, but most cannot trace where it enters the funnel.

The research explores how leaders interpret low or unclear AI referral traffic. Thirty percent believe AI tools influence decisions even when clicks are rare. Forty-three percent think it is simply early, and that impact will rise quickly. Just 11% consider AI irrelevant to their category.

For pipeline attribution, this creates what Fry describes as a structural challenge. Influence can precede measurable engagement. By the time a potential buyer clicks through to a website or registers for a demo, their preferences may already have been shaped by an AI-generated summary that compared five vendors, ranked them against stated criteria, and produced a shortlist. That upstream influence will not appear in any last-touch report.

The problem is not unlike the one B2B marketers have faced for years with dark social and offline word-of-mouth, though the scale and speed are different. LinkedIn's Company Intelligence API, launched in September 2025, was designed partly to address similar upstream influence gaps - showing how paid and organic LinkedIn touchpoints contribute to pipeline even when the path is non-linear. The AI layer presents a harder version of the same problem, because the touchpoint itself occurs inside a third-party system with no pixel, no UTM parameter, and no click trail.

A new commercial risk: algorithmic mispositioning

Beyond the challenge of being invisible in AI answers, the Agentcy report introduces a more specific and commercially significant risk it terms algorithmic mispositioning. This occurs when a brand does appear in AI-generated answers but is framed inaccurately - associated with the wrong use cases, positioned against an incorrect competitive set, or described with attributes that distort buyer perception.

The mechanism is important to understand. AI systems do not invent narratives independently. According to the research, they synthesise signals that already exist in the public domain: media coverage, analyst commentary, review platforms, structured content, competitive comparisons, and category language. Where those signals are fragmented or inconsistent, AI outputs reflect that fragmentation.

According to the index, 66% of respondents have checked how their brand appears in AI answers at least once. But only 25% do so regularly - meaning three-quarters of organisations are not consistently monitoring how they are described inside AI systems. Of those who have assessed their positioning, 46% found it mixed or inaccurate.

Inaccurate positioning is not a new problem for marketers. But the speed and scale at which AI systems can propagate a misframing across multiple platforms simultaneously makes it qualitatively different from a single inaccurate analyst report or a mistaken press mention. A buyer querying three different AI platforms may receive a consistently misframed description of a brand - consistently, because all three systems drew from the same fragmented public signals.

Meltwater launched its GenAI Lens in July 2025 specifically to address monitoring across ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek. Amplitude introduced an AI Visibility tool in October 2025 that connects brand presence in AI responses to traffic and conversion data. Adobe launched its LLM Optimizer in October 2025 for enterprise customers seeking to monitor and improve discoverability across generative AI interfaces. The Agentcy data suggests adoption of such tools remains thin: only 12% have a dedicated AI visibility tool in live use.

According to Fry: "Invisibility is a problem you can see. Mispositioning compounds quietly. And most organisations have no consistent monitoring in place to detect it."

What leaders want to measure in 2026

When the index asked respondents what AI visibility metrics would be most valuable to measure in 2026, traffic volume did not top the list. The most selected metric was context and positioning accuracy, chosen by 37% of respondents. Share of voice in AI answers came second at 36%. Citations and trusted sources used by AI systems were selected by 30%, downstream pipeline influence by 24%, and category presence in shortlist-style queries by 22%.

The preference for positioning accuracy over traffic reflects a maturation in thinking. Volume metrics suited an environment where the goal was to attract as many visitors as possible. In an AI-mediated discovery environment, the goal shifts. What matters is whether a brand appears in shortlist-style answers and whether it is described accurately when it does. The measurement problem is less about counting impressions and more about monitoring narrative quality.

According to the report, this is why integrated PR and AI visibility monitoring are becoming strategically connected. Press coverage, analyst validation, expert commentary, structured comparison content, and clear category language all feed into the signals that AI systems draw on when constructing answers. If narrative authority is inconsistent across those channels, AI visibility will be inconsistent too.

LinkedIn's December 2025 research on owned prominence made a related argument: that B2B brands must shift investment toward building brand memory and distinctive assets that occupy mental space before buyers begin active research. The Agentcy index reinforces that logic from a different angle - the signals that feed AI-generated answers are largely the same signals that build organic brand authority.

Investment is cautious and practical barriers dominate

Despite broad acknowledgement that AI visibility is a blind spot, investment in dedicated measurement and tooling remains in an early phase. The index characterises the current moment as a structured evaluation phase - not a hype cycle and not mass adoption. Organisations are weighing AI visibility carefully.

The barriers are practical rather than ideological. Forty-three percent cite lack of time and internal resources. Thirty-one percent struggle to justify ROI. Thirty percent say they do not yet understand which actions influence AI recommendations. Twenty-six percent point to the absence of a clear internal owner.

That final barrier connects back to the ownership fragmentation documented earlier in the report. Without a clear owner, investment cases stall because accountability is unclear. And without investment, measurement maturity does not improve. The cycle reinforces itself.

The index estimates an overall conversion rate of 38.71% among those actively evaluating or piloting AI visibility tools. That figure covers organisations that are actively evaluating vendors, planning to invest later in 2026, or running proof-of-concept pilots. Only a small share - the 12% with a dedicated tool live - have moved past evaluation into operational deployment.

For the marketing community, the ROI justification challenge is familiar. It mirrors earlier debates about content marketing, brand measurement, and, more recently, the difficulty of connecting programmatic display impressions to business outcomes. The challenge of proving marketing measurement has been persistent, with 60% of marketers facing internal skepticism that puts budgets at risk. The Agentcy report suggests the same dynamic is playing out with AI visibility, where belief in the channel's influence is not yet supported by the attribution infrastructure needed to defend budget allocation decisions in front of a CFO.

Context within PPC Land's coverage

The Agentcy findings arrive within a broader shift documented across PPC Land's coverage since mid-2025. The four-layer SEO framework published in June 2025 introduced the terminology of Answer Engine Optimisation (AEO) and Generative Engine Optimisation (GEO) to distinguish AI-specific optimisation from traditional search. The iPullRank AI Search Manual, released in August 2025, provided technical implementation detail for marketers attempting to optimise content for AI retrieval. The NP Digital study from July 2025 found that 56% of marketers reported traffic increases since the introduction of AI Overviews, complicating narratives about uniform traffic decline.

What the Agentcy index adds is measurement data at the leadership level - not about tools or tactics but about whether B2B organisations have the governance structures in place to manage AI visibility as a strategic capability. The answer, for most, is that they do not yet.

The index will be published quarterly. Agentcy said it intends to track the market's progression from awareness to measurement to attribution maturity. That arc - from recognising a blind spot to building infrastructure around it - will be visible in successive editions. The first edition establishes the baseline. It is not a comfortable one.

Timeline

Summary

Who: Agentcy, an influence intelligence platform for B2B technology brands based in London, working with PR agency Resonance. The research surveyed 104 senior B2B marketing leaders holding VP, Director, CMO, or equivalent roles at B2B technology organisations.

What: The First Annual AI Visibility Index, titled "The New Rules of Visibility 2026," documenting a gap between B2B organisations' belief in AI's pipeline influence and the measurement infrastructure required to act on it. Key findings include: 81% consider AI visibility a blind spot, only 10% can connect AI touchpoints to revenue, 46% who assessed their AI positioning found it mixed or inaccurate, and 26% have no clear owner for AI visibility.

When: The survey was conducted in February 2026. The report was published on 5 March 2026.

Where: The research covers B2B technology organisations broadly. Agentcy is headquartered in London. The full report is available at agentcy.com.pr/resources/whitepaper/the-new-rules-of-visibility-2026.

Why: AI answer engines - including ChatGPT, Gemini, Microsoft Copilot, and Perplexity - are increasingly used by B2B buyers to research, compare, and shortlist vendors before visiting a company website. This compresses the discovery journey in ways that traditional click-based attribution cannot measure. The index is intended to track the market's evolution from awareness to measurement to attribution maturity, with subsequent editions planned quarterly.

Share this article
The link has been copied!