Artificial Intelligence in Pharmaceutical Commercialization: Transforming Market Access, Customer Engagement, and Commercial Strategy
Abstract
Background. Artificial intelligence (AI) entered pharmaceutical research and development more than a decade ago, but its migration into the commercial function has been slower and less well documented. 2025 is widely described in industry literature as the year commercial AI moved from isolated pilots toward embedded infrastructure, driven by the convergence of generative and agentic model capability with acute commercial pressure from pricing reform, patent expiry, and constricting physician access. Objective. This paper reviews and synthesizes the evidence on AI adoption across three commercial domains: market access and reimbursement, customer engagement, and enterprise commercial strategy. It assesses claimed value, the governance environment, and the structural reasons that reported deployment consistently outpaces reported return. Methods. An integrative, narrative review of peer-reviewed literature, regulatory guidance documents, industry benchmarking surveys, consultancy analyses, and vendor product disclosures published through December 2025, with 2025 as the focal period. Findings. Three findings recur across sources. First, the highest-confidence value in 2025 was realized not in autonomous decision-making but in compression of documentation-heavy workflows: medical, legal, and regulatory content review, prior authorization case preparation, evidence dossier drafting, and field force administrative burden. Second, market access is undergoing a structural change that has attracted little scholarly attention: as payers themselves deploy AI in coverage policy development and utilization management, manufacturer evidence must increasingly be legible to algorithmic as well as human reviewers. Third, the binding constraint on value capture is not model capability but data infrastructure, particularly entity resolution across customer identifiers, and the absence of a governed semantic layer that encodes commercial business logic. Conclusions. The regulatory perimeter for commercial AI in pharmaceuticals remains notably thin. The U.S. Food and Drug Administration's January 2025 draft guidance explicitly excludes operational and commercial uses, while horizontal instruments such as the EU AI Act impose transparency and risk obligations that most commercial organizations have not yet operationalized. This creates a governance gap in exactly the domain where deployment velocity is highest. A research agenda is proposed around measurement, algorithmic payer interaction, equity effects of predictive targeting, and the accountability architecture for agentic systems.
Keywords
artificial intelligence; generative AI; agentic AI; pharmaceutical commercialization; market access; health economics and outcomes research; next best action; omnichannel engagement; prior authorizati
References
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