Empowering next-generation pharmaceutical development and quality assurance with AI: a strategy to encourage adoption

We propose that all capacities to emulate human intelligence by a computer system can be considered as AI. With this broad definition, many applications of AI are already in place to enhance control strategies in pharmaceutical CMC (chemistry, manufacturing, and controls). However, further adoption of AI faces formidable challenges including trust, interpretability, talent and culture, and digital readiness. We discuss strategies to overcome these challenges and encourage adoption.

AI encompasses all capacities to emulate human intelligence by a computer system – from rule-based expert systems and predictive models to large language models. Many applications are already enhancing pharmaceutical CMC control strategies, but trust, interpretability, talent, culture, and digital readiness remain formidable barriers to broader adoption.

What you will learn

01

Which AI technologies – from Bayesian optimisation to LLMs – are already applicable to vaccine CMC and how

02

Why pharmaceutical AI adoption lags other industries and what the four key challenges are

03

How expert-in-the-loop governance, interpretability tools, and digital infrastructure can accelerate adoption

Strategy for AI adoption in pharmaceutical CMC

1

Expert in the loop: confirm & verify AI recommendations

2

Interpretability via apps, visualisations & explainable models

3

Talent & culture: empower domain experts, not just data scientists

4

Digital readiness: accessible, structured, AI-compatible data infrastructure

Key findings

AI in CMC spans far beyond machine learning – rule-based expert systems, Bayesian optimisation, digital twins, reinforcement learning, and LLMs are all in scope, with many already applied to visual inspection, batch prediction, and deviation management

Trust is the primary adoption barrier – the black-box nature of data-driven models is analogous to self-driving vehicles; an expert-in-the-loop safeguard mirrors the design–confirm–verify validation framework already established in pharmaceutical practice

Domain experts, not data scientists, often build the most successful AI tools – AI-assisted coding has democratised development; leadership must cultivate communities of practice and cross-functional sharing rather than relying solely on specialist hires

Data silos are the biggest technical barrier – quality, supply chain, clinical, and manufacturing data typically remain in departmental silos; integrating these via AI-compatible infrastructure and protocols such as Model Context Protocol could unlock significant competitive advantage

Key interests

Artificial Intelligence

Pharmaceutical CMC

Machine Learning

Digital Twins

Process Control

Quality Assurance

Industry 4.0