Artificial intelligence is reshaping how regulated organizations approach submissions, compliance, post-market activities, quality management, and operational decision-making. Yet adoption remains uneven, largely unmeasured, and often misunderstood. While some sectors are perceived to be further ahead than others, there are few established benchmarks, limited standardized metrics, and little cross-industry data to validate those assumptions. In practice, AI adoption varies significantly based on organizational maturity, data readiness, governance models, regulatory expectations, and risk tolerance.
This panel brings together leaders from pharmaceutical, medical device, combination product, and regulatory technology organizations to examine AI adoption through a candid, cross-industry lens. As manufacturers of highly regulated products, these organizations face many of the same challenges: governing data, validating systems, managing risk, and scaling AI responsibly within complex operational environments.
Rather than focusing on success stories alone, panelists will discuss where their organizations actually are in their AI adoption journeys, what is working, where progress has stalled, and what lessons are emerging as AI moves from experimentation into governed use. The discussion will also explore perspectives from regulatory technology providers that support organizations across multiple sectors and maturity levels.
The session will focus on three key questions:
• How are organizations defining and measuring AI adoption, and if they are not, why?
• Where is AI delivering measurable value versus creating new governance, validation, or compliance challenges?
• What does a practical AI maturity model look like for regulated organizations seeking to scale AI responsibly?
Attendees will leave with a realistic view of AI adoption across regulated industries, a framework for assessing organizational maturity, and practical insights that can help advance AI initiatives in a compliant, sustainable, and business-focused manner.