Episode 67 - Leveraging AI for Proactive Quality with Niyati Patel – SOLABS Knowledge Base

Welcome to Automating Quality, the life sciences–focused show exploring how quality, risk, and technology intersect to modernize regulated environments.

In the third and final episode of this series, Philippe closes out the conversation with Niyati Patel, Strategic Quality and Compliance Advisor, returning to explore how life sciences organizations can shift from reactive to proactive quality as AI adoption matures.

Niyati distinguishes reactive quality, asking what went wrong after the fact, from proactive quality, asking what pattern signals a problem before it occurs. Drawing on a CDMO case, she shows how AI can connect deviations that look unrelated in isolation, such as operator error, equipment malfunction, and process inadequacy, into an actionable trend.

The conversation turns to the industry's move toward integrated architectures connecting ERP, EQMS, and LIMS. Niyati illustrates this with a supplier quality example, then explains how a CSA approach to system interfaces shifts validation focus from exhaustive test scripts toward risk-based confidence in a system's intended use.

Returning to AI, Niyati outlines five factors to weigh before moving a tool from experimentation to operational use: business value, risk, data quality, human oversight, and scalability. She walks through validating an AI tool built to accelerate investigation research, applying the same risk-based, CSA-style rigor used for any GxP system.
AI can accelerate the pace of quality work, but the judgment behind every decision remains with the quality professional.

Key Takeaways
00:22 Introducing today's topic: from reactive to proactive quality
01:20 Reintroducing guest Niyati Patel and catching up since Parts 1 and 2
05:34 What separates reactive quality from proactive quality
06:32 Case example: connecting unrelated deviations into a single trend
10:36 The shift toward enterprise-wide, integrated system architectures
14:55 Case example: integrating ERP and EQMS for supplier quality
18:29 How CSA shifts validation from test scripts to risk-based confidence
25:44 Five factors for moving an AI tool from experimentation to operation
26:46 Case example: using AI to accelerate investigation research
31:17 Why AI supports quality decisions rather than replacing them