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Why do AI projects fail in drug development and pharma? Insights from multi-year Pistoia Alliance studies
Vladimir A Makarov1, Mario Sänger2, Roman Saiz3
1Pistoia Alliance Inc., 401 Edgewater Place, Suite 600, Wakefield, MA, 01880, USA. vladimir.makarov@pistoiaalliance.org.
Journal of Computer-Aided Molecular Design
|August 7, 2026
Summary
Successful artificial intelligence (AI) adoption in life sciences hinges on organizational maturity, not just technical factors. Enhancing project readiness and trustworthiness is key for sustained business value in AI drug discovery.
Area of Science:
- Life Sciences
- Pharmaceutical Industry
- Drug Discovery
Background:
- Artificial intelligence (AI) is increasingly vital in drug discovery and pharmaceuticals.
- Many AI pilots succeed technically but fail to deliver sustained business value.
- Limited production deployment success despite high demand for AI.
Purpose of the Study:
- Analyze determinants of successful AI adoption in life sciences.
- Identify factors correlating with sustained business value from AI initiatives.
- Provide actionable recommendations for AI implementation in the pharmaceutical sector.
Main Methods:
- Brainstorming workshops with senior industry professionals.
- Global executive survey on AI adoption.
- Statistical analysis of AI/Machine Learning (ML) use cases from the Pistoia Alliance.
Main Results:
- High strategic AI importance contrasts with low organizational maturity, limiting production success.
- Business success correlates with organizational capability maturity, especially project maturity.
- Technical factors like data sources or model types showed no independent association with success.
Conclusions:
- AI project success drivers mirror other complex engineering initiatives.
- Organizational readiness, change management, and stakeholder alignment are critical.
- Trustworthiness (reproducibility, explainability, governance) is a key adoption barrier, especially in high-risk areas.
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