Where AI is creating value today
Artificial intelligence is rapidly reshaping how pharmaceutical organisations operate across the life sciences value chain. Generative AI could generate $60–110bn in value annually for the pharma industry. But its impact is uneven, unfolding at different speeds across research, clinical operations, manufacturing and commercial strategy.
The realities of AI in pharma
Insights from senior industry leaders across pharma reveal a more nuanced picture.
Value is Here
AI is delivering measurable impact in commercial and operational use cases, particularly where results can be realised quickly.
Impact is Uneven
Progress in early-stage research does not yet translate into end-to-end success, and outcomes vary significantly across functions.
Barriers are Organisational
Data quality, regulation and adoption, not algorithms, are the primary constraints.
AI in drug discovery vs development
Early discovery
AI is delivering its clearest impact in early-stage research, from molecule design to candidate generation and large-scale data analysis. These approaches are improving selectivity and early safety profiles.
Clinical development
This momentum does not carry through to later stages. Clinical success rates remain low, and the cost, complexity and timelines of trials make impact harder to realise and measure.
What this Means
AI is accelerating discovery, but it is not yet transforming the full drug development lifecycle.
Data & Infrastructure
AI is only as good as the data behind it. Fragmented, inaccessible or poorly governed data is a structural disadvantage.
Regulatory Complexity
AI must be explainable, auditable and aligned with strict regulatory frameworks. This extends into manufacturing and supply chain, where optimisation operates within tightly controlled environments.
Organisational Adoption
The greatest resistance sits in the middle of organisations, where AI challenges established expertise and decision-making authority.
AI in Preclinical Research
Accelerating discovery and identifying weak candidates earlier through data-driven modelling.
AI in Clinical Operations
Improving trial design and patient recruitment, while navigating regulatory complexity and uncertainty.
AI in Commercial Strategy
Enhancing targeting, segmentation and engagement to drive more efficient go-to-market performance.
A business school perspective on AI in pharma
The implications of AI are strategic, not just technical. Realising its full value depends on how it is integrated into decision-making, organisational design and management systems. London Business School's Data Science and AI Initiative brings an evidence-based, whole-system lens to this challenge. With 1,300+ executives trained and 44 faculty engaged, it is one of Europe's most active business school AI institutes. Read Prof. Nicos Savva's reflections from a recent industry roundtable.
Key questions
How should organisations prioritise AI investments across short- and long-term value?
How can companies build cultures that support experimentation while maintaining control?
What happens to capability development when entry-level tasks are automated?
AI in pharma: research & perspectives
