Scaling AI in Pharma: When Pilots Work but Nothing Scales

Pharma commercial organizations are not short on AI ambition. Most have identified dozens—sometimes hundreds—of potential use cases across field operations, marketing, market access, and commercial analytics. Some have invested heavily in building a modern commercial data foundation. The problem is rarely ideation or data readiness in isolation. It is the absence of an enterprise operating model to systematically evaluate, prioritize, build, govern, and scale AI initiatives from isolated pilots into production workflows that compound value across the commercial organization.

The pattern is familiar: a successful proof-of-concept in call planning optimization or next-best-action for the field force, followed by months of stalled deployment because the organization lacks the governance framework, integration pathway, change management playbook, or value measurement infrastructure to move it into the hands of commercial teams at scale. Meanwhile, the backlog of promising ideas keeps growing, pilot fatigue sets in, and the gap between AI investment and AI impact widens.

This roundtable brings together pharma commercial CIOs and digital leaders for a conversation on what it takes to close that gap; not by launching more pilots, but by building the organizational machinery that treats AI as an enterprise commercial capability. The discussion will move through the key barriers to scaling AI in pharma commercial operations from operating model gaps and data-to-AI translation challenges to governance friction, adoption failure, portfolio economics, and ultimately, what each leader sees as the single biggest unlock for their organization.