Generative Engine Optimization (GEO): How GenAI Is Rewiring B2B Buying Journeys

Pharmaceuticals: When Clinical AI Intermediates the Physician Relationship

The pharmaceutical industry offers the clearest and most consequential illustration of AI-driven channel displacement. Major pharma companies collectively invest tens of billions annually in physician engagement — medical science liaisons, sales representatives, continuing medical education, and speaker bureaus. These investments exist to ensure that clinical evidence reaches prescribers accurately and completely.

That relationship is now being intermediated at scale. OpenEvidence, a clinical decision-support AI assistant for physicians, handled over 20 million physician queries in January 2026 — up from approximately 2.6 million in December 2024. More than 40% of U.S. physicians use it daily. OpenAI and Anthropic both deepened their healthcare commitments in early 2026, positioning ChatGPT for Healthcare and Claude for Life Sciences at the center of clinical workflows.

The implications are direct. A drug with superior clinical evidence but poorly structured, paywalled, or machine-unreadable publications will underperform in AI-synthesized treatment recommendations relative to a competitor whose content is open, structured, and LLM-accessible. A $2 billion development program and $200 million in annual physician engagement can be outflanked by a competitor’s content architecture.

GSK’s audit of a COPD treatment brand in 2025–26 illustrates the precision required. Testing 6,000 prompts across nine nodes in the healthcare practitioner’s decision-making process, the brand ranked first for broad treatment queries but dropped to fourth place for pharmacotherapy-naïve patients — precisely the segment it was positioned to lead. The disconnect between brand strategy and AI visibility was not a messaging failure. It was a structural content failure.

Industrial Manufacturing: The Specification Economy Shifts

In industrial procurement, AI-powered platforms such as Arkestro, Fairmarkit, and Keelvar are already analyzing technical specifications, comparing suppliers, and recommending sourcing strategies. The procurement conversation is increasingly shaped before a supplier representative enters it.

IMI, a UK-based engineering company specializing in fluid and motion control technology, identified a consequential behavioral shift in its residential building business. HVAC installers had stopped using Google Search for product research. They were querying ChatGPT and Gemini instead — comparing features, prices, and alternatives with a fluency that previously required deep domain expertise.

As IMI’s marketing and commercial excellence director noted, the barrier to informed pushback in supplier negotiations has effectively disappeared. A buyer equipped with AI-synthesized specifications is a structurally different negotiating counterpart than one relying on a sales representative’s framing.

Banking: Operational AI Versus Commercial AI

Banking presents a different pattern. Financial institutions have been early and enthusiastic adopters of generative AI, but most deployments remain internal. JPMorgan Chase’s AI-driven payment validation has reduced fraud and lowered account validation rejection rates by roughly 20%. A Hong Kong Institute for Monetary and Financial Research study found that 75% of financial institutions primarily view generative AI as a productivity and efficiency tool.

The commercial opportunity — and the competitive exposure — lies elsewhere. In commercial banking and wealth management, customers are increasingly using AI tools to analyze cash management options, compare borrowing structures, and optimize treasury decisions. The high-touch, relationship-led model anchored by relationship managers and branch networks is being quietly eroded by AI-mediated self-service discovery. Banks that treat GEO as a marketing concern rather than a go-to-market imperative will find their positioning shaped by others.

Coordination: Aligning the Cross-Functional Narrative

Most organizations manage content through functional silos. Marketing owns promotional materials. Research owns scientific communications. Legal owns disclosures. Corporate communications handles press releases. These separations exist for legitimate reasons — regulatory compliance, scientific integrity, promotional risk management.

GEO, however, depends on consistent cross-functional signals. An LLM does not distinguish between a peer-reviewed abstract, a corporate press release, and a Reddit thread when constructing a synthesized answer. It weights them by retrievability, consistency, and corroboration. Inconsistent framing across functions creates noise that degrades AI visibility.

Coordination means establishing shared accountability for three outcomes: accuracy (correct, consistent, complete information across the content corpus), consistency (aligned language and framing across all published materials), and governance (clear accountability when AI outputs contain errors or dangerous misrepresentations). Every function producing digitally consumed content must consider how it will be parsed by a machine learning system.

Citability: Engineering Content for LLM Ingestion

Traditional content strategy optimized for human readers and search engine crawlers. GEO requires a third optimization layer: machine readability for LLM ingestion.

For pharmaceutical companies, this creates a genuine tension. Principal investigators have historically prioritized top-tier peer-reviewed journals — the New England Journal of Medicine, The Lancet — where much content sits behind strict paywalls. Open-source LLMs cannot access paywalled content. A landmark trial result published exclusively in a paywalled journal may be invisible to the AI assistant an oncologist queries at the point of care.

GSK’s publication head for specialty and general medicines has noted that publishers are aware of this dynamic and increasingly concerned that their platforms are being bypassed — driving discussion around model context protocol linkages and copyright licensing. The resolution is not yet clear, but the structural pressure is real and growing.

For industrial manufacturers, citability means something more operational. IMI restructured its product knowledge architecture around LLM ingestion: implementing product schema markup with standardized specifications and use cases, developing “AI snackable” micro-answers that reflect the questions installers and consultants actually ask LLMs, and building comparison tables and step-by-step guides with explicit structure. The governing principle is precise: control the input to control the output.

Credibility: Winning the Authority Signal

In a fragmented LLM landscape with multiple competing models, credibility hinges on the authority of the sources a model can reference. This is not a uniform standard across industries — it is category-specific.

In pharmaceuticals, high-tier journals and clinical guidelines function as the primary authority signals. GSK’s engagement with OpenEvidence was oriented not toward influencing outputs but toward understanding what publication standards must evolve to ensure that AI systems surface accurate, guideline-aligned clinical information. The GOLD guideline finding from GSK’s COPD audit is instructive: LLMs were consistently citing the 2024 version of the guidelines because the 2025 update had changed its publication format from embedded PDFs to downloadable files — making it machine-unreadable. A formatting decision by a guideline publisher propagated outdated clinical recommendations at scale across multiple LLMs.

In B2B fintech, a Digitas UK pilot study found that more than 80% of sources leveraged by LLMs for payment solution queries originated directly from the brands themselves — Stripe, Adyen, PayPal, Visa. These brands have invested heavily in structured, comparison-oriented content that supports B2B buying journeys. Their content architecture is, effectively, their credibility infrastructure.

For industrial manufacturers, credibility means ensuring product specifications appear in industry databases, certifications are registered with relevant standards bodies, and case studies include verifiable customer references. IMI also identified an unexpected credibility channel: YouTube. Short, authoritative video formats — a CFO explaining quarterly results, an R&D leader outlining innovation priorities — proved more likely to surface in generative AI outputs than traditional corporate reports. The implication is that credibility signals are broader and less predictable than most organizations assume.

Calibration: Generative Listening as a Strategic Function

The final component is the most operationally demanding. Generative listening systems monitor how and how often a company’s content appears in AI-synthesized answers across relevant use cases — effectively auditing the dark funnel.

GSK’s COPD audit tested 6,000 prompts across nine decision nodes. IMI’s LLM audit identified that users tend to accept AI-generated answers as accurate without probing them — a finding with significant implications for misinformation risk and brand positioning. IMI’s response was structured around three imperatives: ensuring its products appeared in GEO results, enhancing the quality and framing of those recommendations, and ensuring that brand influence came from sources external to the brand itself.

Generative listening is not a one-time audit. It is an ongoing intelligence function — the equivalent of brand tracking, but for AI-mediated visibility. Organizations that build this capability early will have a structural advantage in understanding how their positioning is being constructed, distorted, or displaced in the models their buyers are using.