Many life sciences teams struggle with:
Interpreting evolving GMP expectations for AI adoption
Defining intended use and regulatory system boundaries
Building governance for digital systems and data integrity
Managing documentation across hybrid and computerized environments
Preparing quality systems for upcoming GMP revisions
Participants learn a structured, practical approach including:
Interpreting FDA and EMA digital GMP expectations
Applying risk-based qualification for AI-enabled systems
Defining intended use, context, and model explainability
Strengthening lifecycle, governance, and data controls
Building gap assessments and implementation readiness plans
This course helps professionals translate emerging GMP requirements into practical compliance actions across computerized systems, AI governance, and quality systems.
Many life sciences teams struggle with:
Interpreting evolving GMP expectations for AI adoption
Defining intended use and regulatory system boundaries
Building governance for digital systems and data integrity
Managing documentation across hybrid and computerized environments
Preparing quality systems for upcoming GMP revisions
Participants learn a structured, practical approach including:
Interpreting FDA and EMA digital GMP expectations
Applying risk-based qualification for AI-enabled systems
Defining intended use, context, and model explainability
Strengthening lifecycle, governance, and data controls
Building gap assessments and implementation readiness plans
This course helps professionals translate emerging GMP requirements into practical compliance actions across computerized systems, AI governance, and quality systems.
Apply risk-based AI model qualification principles
Clarify intended use and context of use
Address explainability, reliability, and model design
Strengthen documentation and data governance controls
Assess validation and lifecycle integrity impacts
Conduct gap analysis for AI readiness
Review draft EU Annex 11 requirements
Examine new Annex 22 for AI
Understand FDA and EMA ten point plan
Define computerized systems in GMP environments
Build governance models for digital operations
Align documentation and system oversight practices
Apply risk-based AI model qualification principles
Clarify intended use and context of use
Address explainability, reliability, and model design
Strengthen documentation and data governance controls
Assess validation and lifecycle integrity impacts
Conduct gap analysis for AI readiness
Strengthen digital GMP oversight and prepare compliant quality system updates.
Apply AI and computerized systems requirements across daily GMP and GDP operations
Build compliant governance, validation, and lifecycle controls for digital systems.
Understand regulatory expectations and support compliant AI-enabled operational decisions.
Strengthen digital GMP oversight and prepare compliant quality system updates.
Apply AI and computerized systems requirements across daily GMP and GDP operations.
Build compliant governance, validation, and lifecycle controls for digital systems.
Understand regulatory expectations and support compliant AI-enabled operational decisions.
Karen Taylor is a highly respected GMP and quality systems consultant with more than 30 years of industry experience supporting global pharma and biotech companies in inspection readiness, regulatory strategy, computerized systems compliance, and data governance. She is also an award-winning international speaker known for translating complex GMP changes into practical, real-world guidance.
Areas of expertise: • GMP • Quality Systems • Inspection Readiness • Computerised Systems • Data Governance • Regulatory Strategy • CMO Audits
More than 30 years in pharma and biotech consulting
Supported startups and global companies through inspections
Co-founded PDA interest group on outsourced operations
Multiple speaker of the year award recipient
Karen Taylor is a highly respected GMP and quality systems consultant with more than 30 years of industry experience supporting global pharma and biotech companies in inspection readiness, regulatory strategy, computerized systems compliance, and data governance. She is also an award-winning international speaker known for translating complex GMP changes into practical, real-world guidance.
Areas of expertise: • GMP • Quality Systems Inspection • Readiness • Computerised Systems • Data Governance • Regulatory Strategy • CMO Audits
More than 30 years in pharma and biotech consulting
Supported startups and global companies through inspections
Co-founded PDA interest group on outsourced operations
Multiple speaker of the year award recipient
Interactive sessions with real-time Q&A and expert guidance
Two intensive sessions: 10:00-13:30 EST each day
Three live Zoom sessions, three hours eachs
Review sessions and reference materials at your own pace
Yes. The course helps organizations prepare for upcoming GMP expectations before AI tools are broadly deployed. It is especially valuable for teams building future-ready quality and governance systems.
Yes. The program reviews draft EU GMP updates and the shared FDA-EMA ten point plan. Participants gain a practical understanding of how these expectations align and differ.
No. The course is designed for both experienced professionals and those newer to AI topics. It explains the compliance impact in clear GMP-focused language.
Yes. It covers the broader scope of computerized systems in GMP environments. This includes governance, documentation, lifecycle control, and data integrity.
