Automating Quality

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 Contact us at [email protected]. Get in touch with Niyati Patel here: https://www.linkedin.com/in/niyatipatel/.

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 safely use AI 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 [email protected] if you have questions or comments. Mandy Gervasio Niyati Patel Philippe Gaudreau

Welcome to Automating Quality, the life sciences–focused show exploring how quality, risk, and technology intersect to modernize regulated environments. In this episode, Mandy and Philippe are joined by Niyati Patel, Strategic Quality and Compliance Advisor with 20+ years of experience across pharma, biotech, medtech, and regulated digital implementations. Niyati specializes in the intersection of quality, validation, and digital transformation. Together, they unpack how validation is evolving in the transition from Computer System Validation (CSV) to Computer Software Assurance (CSA). The discussion challenges common misconceptions, emphasizing that the real shift is toward risk-based thinking, lifecycle control, and deeper system understanding rather than documentation-heavy compliance. The conversation then moves into AI. Niyati outlines how organizations should rethink validation frameworks to focus on boundaries, governance, and human oversight, positioning AI as decision support rather than decision-making. Through practical examples and real-world use cases, this episode sets the foundation for understanding how quality leaders can responsibly adopt AI while maintaining compliance, control, and trust.   Key Takeaways 01:09: Introducing today's topic 03:24: Niyati's background 05:43: What is CSV (and its limitations) 09:06: What CSA actually changes 10:41: Validation rigor is NOT reduced 14:00 Why AI breaks traditional validation 16:23: Why AI must be used for decision support, not autonomous decision making 19:13: Example of AI validation framework 23:40: What validation now proves Please contact us at [email protected] if you have questions or comments. Mandy Gervasio Niyati Patel Philippe Gaudreau

Welcome to Automating Quality, the life sciences–focused show that bridges the gap between automation and quality management. In this episode, our host Philippe welcomes Paul Michel, Senior Consultant at SkillPad, with over 27 years of experience in the pharmaceutical and biopharmaceutical industries, including more than two decades in manufacturing. Paul specializes in GMP training, compliance readiness, and supporting organizations through the complexities of product development and commercial manufacturing. Together, they explore the realities of GxP compliance in biopharma manufacturing — from the scientific complexity of biologics and evolving regulatory expectations to the growing demand for specialized quality skills and the expanding role of CDMOs. The conversation highlights how automation, digital maturity, and strong quality foundations are becoming essential to sustain growth in this fast-evolving sector.   Key Takeaways 02:11 Why biologics manufacturing is fundamentally more complex than small molecule production 04:10 How living cell systems introduce variability and demand tight process control 05:29 Why scale-up in biomanufacturing is scientifically challenging and risk-prone 10:00 The role of ICH Q5 guidelines and comparability studies in biologics compliance 13:06 The growing demand for advanced quality skills in biologics and digital environments 17:18 How modern CDMOs enable faster development from DNA to IND through platform approaches 20:47 Why automation and digitalization are critical to closing the CDMO capacity gap   Contact Paul Michel on LinkedIn here: Paul Michel (He/Him) | LinkedIn Contact us at [email protected] for questions or feedback!

Welcome to Automating Quality, the life sciences–focused show that bridges the gap between automation and quality management. In this episode, our host Philippe welcomes Masha Ivankovic, President and Owner of Monbel Consultants. With more than 15 years of experience supporting pharmaceutical and biotech companies, Masha has led complex engineering, validation, and regulatory projects across highly regulated environments. Together, they explore the increasingly critical topic of qualifying software providers, why it matters, when it should start, and how proper qualification streamlines software validation and long-term compliance. Masha breaks down practical risk-based strategies, key questions to ask vendors, and how to align expectations early to ensure smoother implementations.   Key Takeaways 00:41 Introducing today's guest, Masha Ivankovic from Monbel Consultants 01:25 Masha's background and the services Monbel Consultants provides 02:37 Why qualifying a software provider is now essential in regulated industries 04:47 When vendor qualification should begin vs. when it usually does 06:30 How risk-based thinking shapes software provider qualification 07:40 Examples of risk criticality 09:19 Key qualification criteria and questions to assess provider maturity 11:10 How strong vendor qualification improves software validation success 13:13 What triggers re-qualification and typical lifecycle expectations 15:10 Closing thoughts and where to learn more about Monbel Consultants   Contact [email protected] for suggestions or inquiries. For direct inquiries, Masha can be reached at: [email protected]