At the end of 2025, we were closely monitoring three trends we believed would shape the next phase of biopharma’s operating model changes in 2026: data pipelines, the role of AI in due diligence, and the next-generation value chain.
The J.P. Morgan Healthcare Conference (JPM) offered the first opportunity to pressure-test those assumptions. The conversations on the ground signaled less hype and more recalibration around how the industry’s next gains will come from rethinking how intelligence connects strategy to execution.
1. Integrating data pipelines into agentic models
Despite growing AI ambition, most pharma leaders still make high-stakes asset and commercial decisions using fragmented, static insight. The promise of agentic systems has always depended less on the models themselves and more on whether organizations are willing to invest in the infrastructure required to keep them governed and trustworthy.
At JPM, that dependency became clearer. Announcements such as Anthropic’s Claude for Healthcare emphasized secure ingestion of scientific data, recognizing that AI cannot function in isolation. The growing focus on AI in clinical operations and regulatory workflows reinforced the same point: without continuously refreshed data streams, agentic decision support remains theoretical. Even discussions around manufacturing resilience and supply-chain control pointed back to the same underlying requirement for real-time operational intelligence, whether framed as AI or not.
JPM validated the infrastructure problem itself and the emergence of proactive industry challengers already investing to solve it, even as fully agentic systems are still emerging.
2. AI-powered due diligence and decision making
Traditional diligence remains point-in-time and consensus-driven, even as competitive pressure intensifies. We anticipated movement toward AI systems capable of evaluating assets dynamically across the value chain. And, critically, generating opposing perspectives that help leadership teams test conviction rather than reinforce bias.
Across investor and executive conversations, selectivity, speed, and confidence repeatedly surfaced as competitive differentiators. Therapeutic focus areas, including oncology, neuroscience, immunology and inflammation (I&I), and GLP-1 are increasingly crowded. Consequently, decision windows remain compressed, and the cost of misjudgment has skyrocketed.
The continued acceleration of the weight management market, rising licensing activity in I&I, and growing innovation coming out of China all add layers of complexity. Rather than simply automating processes, AI is becoming more about enabling better decisions under uncertainty in an environment where many business development teams have yet to fully operationalize agentic decision infrastructure.
3. Orchestrating the next-generation biopharma value chain
As AI begins to connect stakeholders across development, regulatory, manufacturing, and commercialization, the opportunity shifts from isolated functional gains to coordinated enterprise decision-making.
Here, JPM offered directional signals, not conclusions. Fewer megadeals and more partnerships and licensing arrangements point to an Alliance Economy, a more modular, ecosystem-based model. AI platforms are beginning to act as connective tissue across functions, but orchestration remains aspirational.
Notably, even if it requires building tools for external stakeholders, sponsor companies increasingly want to own that orchestration layer to operate effectively in a tightening regulatory and compliance environment.
Taken together, JPM confirmed the direction of travel without overhyping maturity. Biopharma is moving toward AI as enterprise infrastructure, but the gap between ambition and execution remains wide.
Looking ahead to SCOPE
That gap is where SCOPE becomes critical. Where JPM validated strategy, SCOPE is likely to expose whether data pipelines are usable at the moment decisions are made, whether AI-assisted judgment has penetrated trial execution and governance, and whether organizations are truly prepared to operate across a multi-stakeholder ecosystem.
Moving forward, several signals warrant continued attention. The AI competitive landscape is shifting quickly as the industry becomes clearer on which components of AI systems are commoditized and where durable differentiation truly lies. Building agents, trust and governance frameworks, and deployable accelerators are quickly becoming table stakes. Increasingly, the real value is emerging in the space between data and agents. At the same time, multi-stakeholder engagement is more critical than ever. As biopharma’s core challenges grow more multidisciplinary, success in 2026 will go beyond dependence on isolated excellence and require organizations to effectively coordinate partners, sites, and regulators across the ecosystem.
As the year unfolds, the defining question is no longer whether these shifts are happening. It’s who is prepared to operationalize them.
