Integrating AI into the GMP (Good Manufacturing Practice) Environment Training Course

Course overview

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the Good Manufacturing Practice (GMP) environment represents a profound shift in pharmaceutical quality management. No longer confined to theoretical or discovery applications, AI technologies are actively restructuring manufacturing, automated quality control, and predictive quality assurance. However, moving from traditional rule-based computerized systems to dynamic, data-driven AI systems introduces complex challenges to established GxP pillars: reproducibility, traceability, and absolute control. 

This intensive, masterclass-level training program is specifically engineered for senior quality assurance professionals, lead auditors, and regulatory compliance managers. It bridges the gap between advanced data science and practical GMP compliance, heavily grounded in the latest regulatory expectations, including the 2026 FDA-EMA Joint Guiding Principles of Good AI Practice in Drug Development, the evolving EU GMP Annex 11 revision concepts, and the EU AI Act.

Rather than treating AI as a ‘black box,’ this course provides a systematic framework to validate, audit, and govern AI technologies safely within a Risk-Based Quality Management System (QMS), shifting an organisation's capability from retroactive error finding to proactive error prevention.

By the end of this course, participants will be able to:

• Decode Regulatory Expectations: Interpret and apply the latest 2026 FDA/EMA joint principles and GxP expectations regarding AI system lifecycles
• Establish Robust Governance: Implement internal AI governance frameworks that define clear ‘Human-in-the-Loop’ (HITL) and ‘Human-on-the-Loop’ (HOTL) boundary controls
• Validate Non-Linear Systems: Adapt traditional Computerized System Validation (CSV) workflows into modern Software Validation / Learning Assurance frameworks suited for AI algorithms
• Audit with Confidence: Conduct targeted internal and vendor audits on data lineage, Retrieval-Augmented Generation (RAG) security, and algorithm drift
• Optimise Quality Workflows: Securely leverage Generative AI (GPT platforms) to accelerate deviation handling, Root Cause Analysis (RCA), and Supplier Notifications of Change (SNC)

This course is part of our GxP training (Good x Practice) course collection, which features a variety of topic areas to ensure you comply with the most recent quality guidelines.

Benefits of attending

• Understand the latest FDA/EMA AI principles, EU AI Act requirements, and evolving GMP expectations
• Gain practical strategies for integrating AI into regulated GMP environments with confidence
• Learn how to establish effective AI governance with HITL and HOTL oversight controls
• Adapt traditional CSV approaches to modern AI/ML validation and lifecycle management
• Enhance auditing capabilities for AI systems, vendors, data integrity, and model governance
• Discover secure applications of Generative AI for deviation handling, CAPA, and quality workflows
• Reduce compliance and quality risks through predictive, AI-driven risk management approaches
• Strengthen collaboration between QA, Validation, IT, and Regulatory Affairs teams
• Discuss real-world case studies, implementation challenges, and regulatory expectations
• Network with industry peers and senior professionals navigating AI adoption in GxP environments

Who should attend

This course is specifically designed for high-level technical professionals, decision-makers, and lead personnel who are responsible for designing, auditing, validating, or approving computerised systems within GxP-regulated environments. It is highly recommended for:

• Quality Assurance Directors, Managers & QMS Specialists: Professionals seeking to transition their QMS from reactive tracking to AI-driven predictive CAPA, automated deviation triaging, and proactive error prevention
• GMP & GDP Lead Auditors / Consultants: Independent or internal auditors who need to establish rigorous audit checklists for vetting AI software vendors, evaluating data science pipelines, and assessing data lineage integrity
• Computerised System Validation (CSV) & Computer Systems Quality (CSQ) Engineers: Specialists tasked with evolving traditional CSV methodologies into modern Software Validation and Learning Assurance frameworks capable of handling non-linear, adaptive algorithms
• Qualified Persons (QPs) and Quality Directors: Senior executives who ultimately carry the regulatory and legal responsibility for product release and must understand the boundaries of ‘Meaningful Human Oversight’ (HITL/HOTL)
• IT Lead Architects and Data Integrity Officers: Professionals responsible for the deployment of secure local LLMs, Retrieval-Augmented Generation (RAG) guardrails, and data infrastructure within manufacturing networks
• Regulatory Affairs Professionals: Experts responsible for documenting and defending AI-driven processes, automated visual inspections, or predictive modeling systems during health authority inspections (FDA, EMA)
  
  

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