AI In Quality: Practical Applications, Regulatory Perspectives, And Preparing QA Teams

Adopting artificial intelligence in regulated environments requires balancing rapid innovation with strict compliance. Navigating shifting global regulatory expectations—including the ten core principles for good AI practice established by the FDA and EMA—demands structured governance, human-centric design, and robust data integrity standards.

Organizations must deploy risk-based validation models across the software lifecycle to ensure safety, efficacy, and audit readiness. Implementing a seven-step risk assessment framework helps align model capabilities with their context of use, mitigating risks tied to algorithmic bias, data privacy, and changing standards. Staying proactive with adaptive validation strategies enables life sciences teams to safely harness AI to boost efficiency, enhance quality assurance, and maintain continuous regulatory alignment.

Explore strategies for deploying compliant AI in enterprise asset management in the full webinar.