B2B Brands Must Become 'Buyable' as LLMs Reshape Supplier Discovery, Joint Study Finds

A new analysis from WARC, LinkedIn and LIONS Advisory warns that visibility alone is no longer sufficient for B2B brands competing in an environment shaped by large language models (LLMs). The report, which builds on earlier research by LinkedIn and Bain & Company, introduces the concept of "Buyability" — a framework designed to measure a brand's capacity to reduce perceived risk at the moment of purchase, not merely attract attention.

A Longer, More Complex Buying Cycle

The study cites data indicating that a B2B purchasing cycle can now last up to 272 days and involve as many as 22 stakeholders. According to the analysis, the greater the number of decision-makers involved, the more any supplier choice must be defensible internally — to senior management, procurement, IT and finance teams alike. Marketing, the report argues, increasingly operates as an "infrastructure of trust" rather than a demand-generation engine.

Three Core Risk-Reduction Signals

The Buyability framework centres on three categories of signals described as the three "Rs": Recommendations, Relationships and Relatability. Recommendations cover endorsements from customers, peers and credible experts. Relationships refer to signals of familiarity and the perception of continuity — particularly relevant given the operational costs of switching suppliers in B2B contexts. Relatability involves demonstrating that a brand has already solved problems comparable to those facing a prospective buyer. Together, these three dimensions form part of a broader framework of seven Buyability signals.

Campaigns Underuse Available Signals

The analysis examined 700 B2B campaigns held in the WARC database, spanning the period from 2010 to 2025 and drawn from sources including the Cannes Lions B2B awards, the Effies and the WARC Effectiveness Awards. The findings indicate that Buyability signals remain largely underutilised: the campaigns studied use an average of 1.6 signals. Nearly a third use only one, and 22 per cent use none at all, while fewer than a quarter combine three or more.

Comparing campaigns classified as "High Buyability" — incorporating between three and seven signals — against "Low Buyability" campaigns — incorporating between zero and two — the study found notable differences in reported outcomes. High-Buyability campaigns were 24 per cent more likely to report an improvement in brand awareness and 91 per cent more likely to show progress on intermediate metrics such as consideration, preference or purchase intent. On commercial measures, they were 63 per cent more likely to report an increase in ROI and 110 per cent more likely to report a rise in incremental revenue.

The report urges caution in interpreting these figures, noting that the study establishes a correlation between the presence of Buyability signals and reported campaign performance; it does not allow the conclusion that mechanically adding signals will automatically produce a specific increase in turnover.

LLMs as a New Layer in Supplier Discovery

The analysis argues that the importance of Buyability is heightened by the growing role of generative AI in the buying process. The document cites research suggesting that 94 per cent of B2B buyers will have used LLMs during their buying journey by 2025. Where supplier discovery once relied on successive website visits, it can now take place through an AI interface that synthesises information from multiple sources. The report suggests that brands capable of credibly demonstrating peer validation, relevance to specific customer scenarios and risk mitigation are more likely to appear on shortlists generated by LLMs — a prospect the authors describe as ensuring the "readability" of a company's reputation by AI systems.

From SEO to an Evidence Architecture

The study contends that this shift could change the design of B2B content strategy. Rather than serving purely to attract website traffic or feed a conversion funnel, editorial output would also need to build a body of evidence accessible to search engines, professional platforms, media, document databases and AI assistants. Detailed case studies, attributable recommendations and specific use-case examples are described as providing more actionable information than generic claims of market leadership or innovation. The authors frame this transition as a potential shift from a battle for visibility to a battle for verifiability.