Course Description:
Artificial intelligence has moved from hype to a practical tool for quality organizations. Yet in a GxP environment, you can't simply bolt it on. Rising product and supply chain complexity, exploding data volumes, talent constraints, and intensifying scrutiny of data integrity are all pushing quality leaders toward AI. The real question is no longer whether to use it, but how to do so responsibly.
This webinar offers a practical, risk-based framework for adopting AI across the quality management system. We'll demystify what "AI" actually means in a regulated setting, map concrete use cases, and show how to sequence them by value and risk. At the core is a six-step path to adoption: identify and prioritize use cases, classify their risk, apply proportionate controls, secure data integrity and quality, establish human oversight, and validate, monitor, and maintain over the lifecycle. We'll connect this to current regulatory thinking (the FDA's risk-based credibility framework, the EU AI Act, GAMP 5, and ALCOA+) and close with practical first steps and common pitfalls to avoid.
Attendees will be able to:
- Spot high-value, low-risk starting points; classify and control AI use proportionate to risk.
- Protect data integrity and human accountability.
- Stand up lightweight governance before you scale
Who should participate:
- Quality & Regulatory
- QA/QC Directors and Managers
- Compliance and Inspection Readiness Teams
- QA Specialists and Auditors
- Quality Leaders – all levels
Though this course is being made available on USP’s Education site, the course content was developed by Pharmatech Associates, a USP company. USP has not independently reviewed or verified the accuracy of the course content.