Session (CTI)

Issue: AI governance in evidence synthesis usually focuses on what could go wrong if teams adopt AI too quickly. That caution is necessary, but incomplete. The field also faces a guidance gap: evidence teams are already experimenting with AI, while formal recommendations often lag behind current workflows using retrieval, agents, audit trails, local validation sets, and structured human adjudication. Without practical internal and community-developed frameworks, the risk is not simply slower adoption. It is inconsistent, poorly documented, or inappropriate AI use spreading before the field has agreed how to govern it. HEOR, medical information, and market-access teams still face pressure for faster, more current evidence, especially under JCA and other timelines. The debate is therefore not acceleration versus caution. It is how organizations can use controlled, risk-tiered AI governance to prepare for formal guidance, reduce poor AI use, and keep evidence workflows transparent, auditable, and reviewable. Overview: This panel will examine the cost of waiting for formal AI guidance and the governance models organizations can use while the field catches up. The discussion will focus on practical decision-making: which evidence tasks can move now, which require stronger controls, and what documentation makes AI-supported work defensible to medical, HTA, regulatory, and market-access reviewers. The panel combines perspectives from a technology builder, pharma medical information, evidence-production operations, and an HTA-methods moderator. The aim is to help attendees distinguish unmanaged adoption from controlled, risk-tiered use of AI in evidence workflows.