The pharmaceutical and biotechnology industry faces persistent challenges in research and development (R&D): high drug development costs, elevated clinical failure rates, and declining returns on investment. These hurdles have made it increasingly difficult to bring successful new drugs to market.

Artificial intelligence (AI) is changing that. The technology is emerging as a powerful catalyst for breakthroughs in biopharma R&D – streamlining discovery, optimizing trial design, and enabling predictive insights – and it’s now poised to bring biopharma R&D more agile, data-driven, and outcome-oriented processes.

The latest report from the Capgemini Research Institute, Smart bet, only option, or both?: Biopharma R&D turns to AI, explores how advances in biology, physics, and computational power are converging to enable this transformation now. Biopharma organizations are recognizing this potential; our global survey of 500 senior executives across eight countries reveals:

• 82% believe AI will fundamentally transform biopharma R&D
• 63% agree that companies failing to scale AI will fall behind in innovation and market relevance
• 63% anticipate that most new molecular entities (NMEs) will originate from AI-driven platforms within the next decade.

The research also shows that organizations are already realizing benefits related to:

• Drug discovery: 74% see significant potential in generative AI. Target identification is the most widely adopted use case, with 43% implementing it and reporting an average 28% time savings.
• Clinical trials: Over 60% affirm that Gen AI can substantially improve trial efficiency and outcomes.
• Regulatory submissions: 73% agree Gen AI can fundamentally transform regulatory workflows. Among adopters, productivity gains average 19% time savings.

Yet despite this progress, challenges remain. Having established foundational data capabilities, many organizations still lack data readiness and operational maturity to scale AI effectively. To unlock AI’s full potential, biopharma leaders must address these gaps by:

• Securing senior leadership buy-in
• Defining clear goals and risk profiles
• Balancing in-house capabilities with strategic partnerships
• Building a data- and digital-savvy workforce
• Advocating for industry-wide data standards.

Smart bet, only option, or both?: Biopharma R&D turns to AI is intended for C-suite executives and senior leaders in global pharmaceutical and biotechnology organizations, offering clear recommendations to help them understand the benefits that AI can bring to the drug discovery and development process.

To discover how large and mid-sized biopharma organizations can implement AI and scale their AI use cases, download the full report today.