Why Do AI Projects Fail in Drug Development and Pharma?

Insights from Multi-Year Pistoia Alliance Studies

This is a peer-reviewed article from the Pistoia Alliance’s LLM and NLP Use Case Database project, published in the Journal of Computer-Aided Molecular Design (2026).

Artificial intelligence is playing an increasingly central role in drug discovery and across the pharmaceutical industry. Yet despite a steady stream of high-profile success stories, many technically successful pilots never translate into sustained business value. Understanding why and what distinguishes the initiatives that scale from those that quietly stall is one of the most important questions facing R&D leaders investing in AI today.

This paper tackles that question with rigor. It brings together three complementary streams of evidence gathered over several years of Pistoia Alliance work:

• Industry brainstorming workshops with senior professionals, from scientists to executive directors, held across biotech and pharma since 2017.
• A global executive survey and interviews, conducted with Zühlke Engineering, capturing where demand is highest, where organizational maturity lags, and where collaboration potential is strongest.
• A statistical analysis of the Pistoia Alliance’s collection of real-world AI, ML, and NLP use cases, one of the largest and most methodically documented collections of its kind, which captures both successes and candid failures across R&D, pharmacovigilance, manufacturing, and beyond.

Read together, these sources tell a remarkably consistent story and point to a conclusion that may be uncomfortable for teams focused primarily on models and algorithms: the factors that most reliably predict whether an AI project succeeds are not the ones most organizations spend the most time on.

What you’ll find in the paper:

• Which project characteristics actually correlate with business success, and which widely assumed “success factors” are not predictive on their own.
• Why reproducibility, explainability, and governance are the real limiting factors for adoption, particularly in high-risk domains.
• How the barriers to AI success mirror those of earlier waves of digital transformation, and what that means for how AI projects should be planned and led.
• A concise set of practical, evidence-based recommendations for improving the odds that an AI initiative delivers lasting value.

The paper offers a clear and refreshingly hype-free perspective for AI practitioners, R&D leaders, and decision-makers working to turn AI’s promise into real, scalable impact in life sciences.

DOI: 10.1007/s10822-026-00894-3