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AI in CMC: Foundations, Readiness & Regulatory Landscape

Available for Group Training

Build the foundations for AI in CMC—data, systems and regulatory understanding—before moving to implementation

This course is currently not scheduled however it can be delivered for your team. To register your interest, complete the form below

We Can Deliver This Course for Your Team

 

No scheduled dates for this course right now — but we can deliver it exclusively for your team.

 

Follow the link below and complete the short form to receive a tailored course outline and clear, transparent pricing. Share your focus areas, objectives, timelines, and group size, and we’ll come back to you with a draft programme, estimated investment, and recommended next steps.

Learn more about the course by toggling through the tabs below. Scroll down to view the agenda, trainer info and who should attend.

Course Overview

 

Artificial intelligence is rapidly gaining traction across CMC functions, but many organisations are not yet equipped to implement it effectively. Challenges around data quality, infrastructure, governance and regulatory expectations often prevent AI initiatives from progressing beyond early exploration.

 

This course provides a clear, structured foundation for understanding how AI can be introduced into CMC environments in a practical and compliant way. It focuses on the prerequisites for successful AI adoption, including data readiness, digital infrastructure, and integration with existing laboratory and knowledge systems.

 

Participants will explore how AI fits within different organisational models, including biotech companies and CDMOs, and gain insight into the evolving global regulatory landscape. Through real-world examples and case studies, the course highlights both the opportunities and limitations of AI in CMC, helping teams set realistic expectations and avoid common pitfalls.

 

By the end of the course, attendees will have a practical roadmap for preparing their organisation for AI, ensuring initiatives are built on strong technical, operational and regulatory foundations.

 

Learn more about how we deliver live online training.

Key Learning Objectives

 

  • Explain core AI concepts and their relevance to CMC environments
  • Identify where AI can realistically add value across CMC functions
  • Assess organisational readiness for AI, including data, infrastructure and workflows
  • Understand the principles of data harmonisation, governance and AI-ready data
  • Evaluate the role of lab automation and digital integration in enabling AI
  • Understand how retrieval-augmented systems (RAG) support knowledge management
  • Recognise the differences between sponsor and CDMO AI implementation models
  • Interpret the evolving regulatory landscape for AI in pharma (FDA, EU, global)
  • Identify risks, limitations and compliance considerations when introducing AI

Who Should Attend?

 

This course is designed for professionals involved in CMC, manufacturing and technical operations who are exploring or planning AI adoption.

CMC & Technical Leadership

  • Heads of CMC, Technical Directors, Manufacturing Leads

Process, Analytical & Development Teams

  • Process Development Scientists, Analytical Development, MSAT

Quality & Regulatory

  • QA professionals, CMC Regulatory Affairs, Compliance Leads

Digital & Transformation Roles

  • Data, digital and transformation leads supporting CMC functions

 

Course Outline

Course Information

  • The course begins at the time stated below
  • The course is broken up into modules outlined below
  • There will be breaks between modules
Day 1

Introduction to AI

  • What is AI? Machine learning, deep learning, generative AI—a taxonomy
  • Brief history: why the sudden acceleration (data, compute, algorithms)
  • Types of AI applications: prediction, classification, generation, optimisation
  • Why AI is touching everything in pharma now
  • Setting realistic expectations: what AI can and cannot do

 

AI Readiness & Digital Infrastructure

The foundational capabilities required before implementing AI applications.

Data Harmonisation

  • Why CMC data is messy: siloed systems, legacy formats, inconsistent naming
  • What ML-ready data looks like: structure, labelling, metadata
  • Data lakes, governance frameworks, and FAIR principles
  • Case study: Cross-site data harmonisation for ML model training

Lab Automation & Instrument Integration

  • The automation spectrum: scripting to robotic work cells
  • Instrument communication protocols and data capture
  • ELN connectivity and structured data output
  • Case study: Automated analytical data capture pipeline

RAG & Enterprise Knowledge Systems

  • What is RAG (retrieval-augmented generation)?
  • Semantic search across documents, SOPs, and historical submissions
  • Building queryable institutional knowledge
  • Case study: AI-powered search across regulatory correspondence

 

AI for Companies vs. Contract Organisations

Different business models require different AI implementation approaches.

  • Data ownership and permissions: whose data is it?
  • The temptation to learn across clients—and the risks
  • Platform vs. bespoke: internal efficiency tools vs. client-facing services
  • Confidentiality in the age of LLMs: what happens when client data meets AI?
  • Competitive positioning: AI as a differentiator for CDMOs
  • Case study: CDMO AI implementation—what worked, what was off-limits

 

Regulatory Landscape for AI in Pharma

Understanding regulatory constraints before diving into applications.

  • Regulatory philosophy: risk-based thinking across jurisdictions
  • US/FDA: January 2025 draft guidance and the 7-step credibility framework
  • EU: AI Act timeline (August 2025–2027), Annex 22 (GMP), AI literacy requirements
  • APAC/Singapore: HSA SaMD guidelines, AI Verify Toolkit, regional convergence
  • FDA–EMA joint principles for AI in drug development (January 2026)
  • Practical implications: documentation expectations and regulatory engagement
  • Note: This module provides an informed overview, not regulatory consulting

Discover more CMC courses and Pharmaceutical Training courses

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Available for Group Training

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