Agenda for Day 1 - Monday 9 November, 2026Monday, 9 November 2026

The economics of pharma’s R&D model are under increasing strain. Rising costs, timelines and competition – felt across the entire industry – are offset by the outsized successes of the few winners. This plenary takes a data-led view of pharma productivity, from developing new medicines, making them available to patients, and delivering a sustainable return on investment. Over the last five years, R&D budgets are coming under increasing pressure, which raises the importance of external innovation. This has direct implications for investment, corporate strategy, and relationships with regulators. And as AI embeds into pharma’s workflows and decision-making processes, it remains unclear how far it will shift underlying productivity. Leaders from pharma, investment, and regulation will offer their views on the current state of play and how organizations should position themselves to be among the few that consistently turn scientific progress into real patient impact.

The AI Scientist is not coming. It is already here. Systems are now capable of analysing data, generating hypotheses, and guiding experiments at a level that starts to resemble an experienced scientist, just faster, cheaper, and scalable. And this is not limited to early discovery. The same capabilities are reshaping how development decisions are made, from clinical strategy and trial design to translational evidence and regulatory planning.

The real impact is not replacement. It is multiplication.

As scientific reasoning becomes more accessible, Jevons Paradox kicks in. We do not produce fewer ideas. We produce far more. More targets, more molecules, more programmes reaching the clinic. The constraint shifts from generating options to making smarter choices about which ones to advance and how.

So what happens when biopharma moves from scarcity to abundance, across the entire value chain?

This conversation explores how that shift is already reshaping the industry. How programmes get prioritised. How development paths are designed. How capital is allocated and partnering decisions are made. And where the bottlenecks move next, into clinical capacity, execution, and infrastructure.

With perspectives from technology, pharma, and investing, this is a candid take on what is real, what is hype, and what actually matters when AI compresses both discovery and development timelines at the same time.

Because if we can build and advance far more drugs than ever before, the real question becomes: which ones are actually worth pursuing, and how fast can we prove it?