Webinar
Join PwC and Kneat for a practical look at how life sciences companies are rethinking validation
Release Date
August 07, 2025
Webinar length
60 minutes
AI-enabled systems are entering regulated life sciences environments faster than most validation programs can keep up. In this partner webinar featuring PwC and Kneat, validation leaders explore how Computer System Assurance (CSA) and artificial intelligence reshape computer system validation (CSV) — and what quality and compliance teams must do now to stay audit-ready, efficient, and prepared.
30–50%
Potential reduction in documentation preparation and review cycles with AI-driven approaches
50–70%
Reduction in undetected issues when drift detection and monitoring practices are in place
20–30%
Reduction in script writing time reported in a multi-site QMS validation case study
The current state of AI adoption readiness across life sciences validation organizations
Evolving regulatory landscape: FDA, ISPE GAMP, and EU AI Act alignment on AI validation expectations
Three ways AI will reshape validation teams — readiness, efficiency, and governance
AI-driven solutions for real-time monitoring, drift detection, and regression testing
Natural language processing (NLP) for document generation and smart SOP management
Case study: validating an AI-enabled QMS across approximately 20 sites using a CSA-aligned approach
Data protection, vendor oversight, and controlled AI use in GxP environments
How to validate AI tools that generate validation artifacts such as test scripts
ALCOA+ considerations for digital execution versus paper-based documentation practices
This webinar is essential for validation managers, quality assurance directors, CSV leads, compliance officers, and IT quality professionals working in pharmaceutical, biotechnology, and medical device organizations. It is especially relevant for teams evaluating or already encountering AI-enabled vendor platforms — including QMS, ERP, and other enterprise systems — and for organizations that have not yet adopted CSA principles or established formal AI governance SOPs. Leaders responsible for multi-site validation harmonization and digital transformation strategy will find the case study and practical frameworks immediately actionable.
Senior Process Engineer, Kneat
Amy is a highly experienced validation engineer with a demonstrated history of excellence in the biotechnology industry. She is an expert in laboratory operations, bioprocess manufacturing, and equipment/facility/utility CQV.
Senior Manager, Digital Quality & Validations, PwC US
Anirudh is a New Jersey-based senior manager at PwC with over eleven years of experience in digital quality transformations across the Pharmaceutical and Life Sciences sector. He specializes in digital quality transformations and validation strategy, having led multiple ERP, QMS and other enterprise-wide application validations.
Director, Digital Quality & Validation, PwC US
Sid is Philadelphia-based director at PwC, specializing in digital quality transformations within the Pharmaceutical and Life Sciences sector. With ten years of experience, he has led validation workstreams for multiple ERP, QMS, and other enterprise-wide digital transformations, managed end-to-end validation activities for clients, and led multiple process transformations to help clients maintain system compliance in a changing regulatory landscape.
CSA applies a risk-based, fit-for-purpose approach to validation rather than relying on exhaustive, document-heavy testing for every system component. For AI-enabled systems, this means scaling validation rigor based on intended use, context of use, and GxP impact. The webinar explains how CSA principles align with emerging regulatory expectations from FDA, ISPE, and the EU AI Act — making CSA the foundational mindset for validating modern, dynamic systems.
Yes, but only with the right boundaries. The speakers emphasize that AI can accelerate tasks like test script drafting and document generation when organizations maintain human-in-the-loop controls, clear SOPs governing AI use, and validation rigor proportional to risk. Agentic workflows where AI performs end-to-end tasks without human review remain a higher-scrutiny frontier that most organizations should approach cautiously.
The panel recommends a risk-based approach that considers the tool’s level of autonomy, the organization’s control and visibility into its design, and the GxP impact of its intended use. Because AI outputs can be variable — a draft test script may not have a single correct form — validation focuses on performance criteria, constraints, and governance rather than deterministic output matching. Human review and approval remain critical safeguards.
PwC shared a case study involving a client migrating to an AI-enabled QMS across approximately 20 sites. The team used a GPT-enabled solution to accelerate test script drafting and reported an approximately 20–30% reduction in script writing time. They drafted scripts for approximately 100 business processes and over 500 requirements, achieving improved consistency that helped reduce errors and enforce Good Documentation Practice (GDP) across large script libraries.
The webinar highlights several practical concerns: data exposure risks with cloud-hosted models, vendor troubleshooting access to sensitive data, and the importance of tying AI usage to intended use with validation rigor proportional to risk. Organizations must also watch for model drift and data drift, which can degrade AI system performance over time without continuous monitoring — a challenge that traditional static validation approaches do not address.
The speakers recommend starting by adopting and scaling CSA as the foundational validation mindset. From there, define AI governance — including SOPs, roles, allowed use cases, and review expectations. Begin with low-risk, human-in-the-loop AI accelerators such as drafting support before attempting autonomous execution. Build lifecycle controls for monitoring, drift detection, retraining, and change management designed for dynamic systems.
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