AI in pharma 2025: The future is here

The pharmaceutical industry is experiencing rapid transformation, fueled by artificial intelligence (AI) and machine learning (ML).

These technologies are reshaping how we discover, develop, and deliver vital treatments while driving efficiency, innovation, and personalized care.

AI-driven drug discovery

AI is accelerating early-stage research by analyzing vast datasets to uncover novel drug targets, enabling faster identification of candidates and reducing time to market.

The AI-driven drug discovery market is projected to grow from $1.48 billion in 2025 to $15.5 billion by 2032 (CAGR ~30%) [1].

Machine learning is fundamentally changing the traditional trial-and-error model by simulating compound-target interactions and prioritizing the most promising candidates [2].

Precision medicine

AI’s integration with genetic data is enabling more personalized treatments and better outcomes through AI-powered biomarker discovery and real-time patient monitoring [2]. This is accelerating progress in tailoring therapies to individual patient profiles.

Optimized clinical trials

AI is revolutionizing clinical trial operations by enhancing patient recruitment, optimizing study design, and enabling real-time adjustments.

AI-driven site selection has been shown to improve enrollment rates by up to 20% and cut trial timelines by six months [3]. Innovative trial designs, including AI-driven genetic algorithms, have also reduced blood sampling needs in pediatric studies by over 50% while maintaining data integrity [4].

Manufacturing

AI is driving significant advancements in pharmaceutical manufacturing, from improving efficiency and reducing waste to ensuring product consistency. Regulatory agencies such as the U.S. Food and Drug Administration (FDA) are increasingly encouraging the use of AI to optimize production processes [5,6].

Regulatory compliance

As AI becomes integral to drug development, regulatory frameworks are evolving to support its responsible use. AI is helping companies automate regulatory submissions and maintain compliance, reducing the burden on internal teams.

The FDA’s recent guidance emphasizes the need for credible, transparent AI models, including risk-based assessments [6].

Similarly, the European Medicines Agency (EMA)’s reflection paper on AI, aligned with the EU AI Act, underscores the importance of ethical oversight, transparency, and adherence to Good Machine Learning Practices [7].

What does this mean for industry leaders?

Pharma leaders who embrace AI today are likely to gain a competitive edge in an increasingly complex and fast-moving landscape.

However, challenges such as data transparency, model bias, and regulatory alignment remain critical. Collaboration with regulators and a thoughtful, risk-based approach to AI adoption will be essential for long-term success.

If you would like to discuss how AI may impact your regulatory strategy, drug development, or manufacturing processes, get in touch directly or visit www.gandlscientific.com to explore how we can support you in navigating these changes and driving your business forward.

Paul Kuiken is Vice President of Advisory Services at G&L Scientific.

References

1. AI in Drug Discovery Market Size 2024-32, SNS Insider, Jan 2025

2. How AI will reshape pharma in 2025, Drug Target Review, Dec 2024

3. Unlocking peak operational performance, McKinsey, Jan 2025

4. Computational design of clinical trials, Tsuchiwata S., Tsuji Y., CPT Pharmacometrics Syst Pharmacol., Mar 2023

5. FDA first recommendations on AI, Eckford C., European Pharmaceutical Review, Jan 2025

6. AI and ML in Software as a Medical Device, FDA, Jan 2025

7. Reflection paper on the use of AI in the medicinal product lifecycle, EMA, Sep 2024 (EMA/CHMP/CVMP/83833/2023)