Artificial intelligence has moved well beyond the experimental phase in the life sciences industry, and is now embedded across the value chain from early-stage drug discovery through manufacturing and commercial operations, and the pace of adoption is only accelerating. Understanding where AI delivers real value, and where challenges remain, is essential for informed strategic planning.
The global AI in life sciences market was valued at approximately $17 billion in 2025 and is projected to reach $69 billion by 2031, reflecting a compound annual growth rate of 26.3%.1 This growth is fueled by the maturation of AI into clinically validated products and accelerating regulatory acceptance. By 2025, the U.S. FDA had authorized more than 1,450 AI- and machine-learning-enabled medical devices, with nearly 300 approvals granted in that year alone.2
In drug discovery specifically, AI is reshaping how molecules are designed, how targets are identified, and how clinical candidates advance. AI-enabled workflows have been shown to reduce time and cost at the preclinical candidate stage by up to 40% and 30%, respectively.3 The average cost of bringing a new drug to market now exceeds $2 billion4, and 41% of biopharma executives cited improving R&D productivity as their top cost-management priority.5 Major pharmaceutical companies (including Eli Lilly, Takeda, and others) are building reusable AI platforms that allow models and insights to compound across programs rather than being rebuilt from.6
Despite the enthusiasm, scaling AI remains a significant challenge. Only 22% of life sciences leaders reported having successfully scaled AI within their organizations, and just 9% said they had achieved significant returns on those efforts.7 A 2025 MIT study8 found that nearly 95% of enterprise generative AI pilots failed to deliver measurable business impact, with one industry commentator noting this is “most often because systems remained disconnected from real workflows, data foundations, and organizational ownership.”9 This underscores a key insight for executives: AI maturity depends less on the sophistication of any given model and more on the strength of data infrastructure, governance, and integration into day-to-day operations.
The regulatory environment is also evolving rapidly. The FDA published draft guidance in January 202510 establishing a risk-based credibility assessment framework for AI models used to support regulatory decision-making.11 Meanwhile, the EU AI Act (adopted in 2024)12 also imposes risk-based rules on AI development and deployment, with direct implications for life sciences teams around logging, traceability, and governance. Leaders who treat compliance and documentation as core capabilities, rather than afterthoughts, will be best positioned to scale AI responsibly.
For business leaders, the strategic takeaway is clear: AI in the life sciences is no longer optional, but success requires more than investment in technology. Organizations must build robust data foundations, implement strong governance frameworks, and integrate AI into validated workflows to realize durable competitive advantage.
Footnotes
1. “AI in the Life Science Market,” MarketsandMarkets.com (report code HIT 10487), May 2026.
2. Ibid.
3. “Artificial Intelligence in Pharmaceuticals and Biotechnology: Current Trends and Innovations,” Coherent Solutions, April 24, 2026.
4. Ibid.
5. “2026 Life Sciences Outlook,” Deloitte Center for Health Solutions, December 9, 2025.
6. AI in Biotech: Lessons from 2025 and the Trends Shaping Drug Discovery in 2026,” Ardigen Blog, January 7, 2026.
7. “Ibid.
8. “AI in Biotech: Lessons from 2025 and the Trends Shaping Drug Discovery in 2026,” Ardigen Blog, January 7, 2026.
9. “AI in Biotech: Lessons from 2025 and the Trends Shaping Drug Discovery in 2026,” Ardigen Blog, January 7, 2026.
10. “Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-making for Drug and Biological Products,” U.S. Food & Drug Administration, January 2025.
11. “AI in Biotech: Lessons from 2025 and the Trends Shaping Drug Discovery in 2026,” Ardigen Blog, January 7, 2026.
12.Regulation (EU) 2024/1689, the EU Artificial Intelligence Act.
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