AI in Commercial Forecasting and BD&L: A Capability Gap or a Confidence Gap?

AI has generated substantial interest across the life sciences industry, yet its practical adoption in commercial forecasting and early asset assessments remains limited. Many organizations continue to rely on established models, largely manual workflows, and experience-based assumptions—even as more advanced analytical capabilities become increasingly accessible. The gap is particularly pronounced within the Business Development and Licensing (BD&L) segment, where forecasts directly influence high-value investment, partnership, and portfolio decisions. While AI has the potential to improve rigor, consistency, and scalability in these evaluations, its integration into BD&L decision-making has been modest at best. This raises a critical question: is the constraint one of capability, or of confidence? Are current AI-driven approaches not fit for real-world, high-stakes decisions—or are organizations hesitant to rely on them when outcomes materially impact capital allocation and corporate strategy? This panel will explore where AI can deliver tangible value today in commercial forecasting and BD&L, and where its limitations remain. Panelists will examine the practical barriers to adoption—including data readiness, systems integration, governance, and trust in model outputs—and discuss whether an incremental, use-case-driven approach is a more effective path forward than wholesale transformation. Ultimately, the session will challenge whether the industry is waiting for better technology—or simply greater conviction to act.