Welcome to Automating Quality, the life sciences–focused show exploring how quality, risk, and technology intersect to modernize regulated environments.
In Part 2 of this three-part series, Mandy and Philippe continue the conversation with Niyati Patel, Strategic Quality and Compliance Advisor, shifting from theory to execution: how organizations are operationalizing AI in GxP environments.
This episode dives into the practical realities of AI governance, focusing on lifecycle management, data boundaries, and the human role in processes involving AI. The discussion unpacks how organizations can structure AI frameworks around intended use, risk classification, data governance, and continuous monitoring, highlighting that AI success is driven less by the model and more by people, policies, and control systems.
The conversation also explores what can and cannot be shared with AI tools, outlining clear distinctions between acceptable, restricted, and prohibited use cases. From SOP generation to critical quality decisions, Niyati breaks down how leading organizations are defining guardrails to enable safe adoption.
Finally, the episode emphasizes that AI is an assistant, not a decision maker.
Key Takeaways
01:22 Looking back at part 1
02:00 Introducing today’s guest Niyati Patel
05:00 How do organizations use AI in a safe way right now?
07:55 Continuous monitoring is critical for systems that evolve over time
10:10 Which data must be protected when giving access to data to your AI?
11:30 How do you get comfortable using AI as an organization?
15:51 What are some good use cases for AI use in regulated industries?
Please contact us at solabs-podcast@solabs.com if you have questions or comments.
Mandy Gervasio
Niyati Patel
Philippe Gaudreau
Comments
0 comments
Please sign in to leave a comment.