Artificial intelligence is transforming every link in the precision medicine value chain. From drug discovery and manufacturing to supply chain optimization and laboratory operation, AI’s impact is both deep and wide-ranging.
While the pharmaceutical industry has enthusiastically embraced Artificial Intelligence (AI) to accelerate scientific research, its most promising potential may now lie in revolutionizing the processes behind drug production and delivery, cutting time, boosting efficiency, and minimizing waste.
Drug Discovery Gets an AI Boost
AI has already transformed how new drugs are found and designed, shrinking the timeline of target discovery, lead identification, and lead optimization by one to two years (Serrano et al., 2024). For example, high-throughput genome interpretation has become far more efficient thanks to AI. Similarly, pharmacogenomics, which is the study of how genes affect a patient’s response to drugs, has benefited, opening the doors to more personalized treatments (Taherdoost et al., 2024).
AI is also streamlining the drug screening process. Rather than manually testing thousands of compounds, AI models can now predict which candidates are most likely to succeed, narrowing the pool before any laboratory experiments begin. Generative models, including deep learning architectures and generative adversarial networks (GANs), are now being used for de novo design, not just identifying existing compounds but creating entirely new molecules optimized for potency and selectivity. This unlocks vast, previously unexplored chemical spaces (Serrano et al., 2024).
Additionally, tools like AlphaFold have been game-changers, enabling highly accurate predictions of protein structures, a critical piece in understanding disease mechanisms and developing targeted therapies (Serrano et al., 2024).
Accelerating Recruitment, Enhancing Monitoring, Improving Outcomes
AI is also proving its value in clinical trials. For example, recruitment is notoriously difficult and expensive; however, AI models can analyze patient records to identify candidates who meet complex inclusion criteria, as well as helping predict dropout risks. This helps to speed up enrollment, de-risking trial integrity while maintaining diversity. Once trials are underway, AI can help monitor patient data in real-time, flagging safety signals earlier and helping sponsors adjust protocols on the fly. These tools reduce costs, shorten timelines, and improve the odds of success (Sedano et al., 2025)
Smarter Manufacturing, Predictive Maintenance, and Agile Supply Chains
AI’s scientific contributions are widely recognized, and I believe its next significant impact will be in transforming the business operations within the pharma industry.
Areas like manufacturing, maintenance, and supply chain management are particularly ripe for AI-driven efficiency gains. For instance, Pfizer utilized AI to enhance the yield of its COVID-19 vaccine manufacturing process and reduce production times. By analyzing production line data, AI systems can spot inefficiencies and recommend process improvements, leading to time and cost savings (Serrano et al., 2024).
Predictive maintenance is another area where AI has demonstrated proven results. Manufacturing equipment generates vast amounts of data from sensors. AI models can analyze this data to predict potential machine failures, allowing companies to schedule interventions precisely when needed, thereby avoiding unplanned downtime. Pfizer again serves as an example here: the company has used AI to reduce maintenance costs and ensure smooth production operations (Serrano et al., 2024).
Supply chain management also holds significant promise. AI can forecast demand with greater accuracy, intelligently manage inventory levels, and optimize logistics in response to market trends. Novartis has effectively leveraged AI for supply chain management, resulting in improved inventory control and reduced costs. These capabilities are invaluable in an industry where both shortages and overstock can negatively impact patients and profits (Serrano et al., 2024).
From Data Chaos to Predictive Insights
AI is not just for big pharmaceutical companies anymore. Laboratories of all types and sizes are leveraging AI to gain new efficiencies and insights (Labguru, 2025).
Surveys show that over 60% of lab users want to use AI-powered data analysis for applications such as predictive insights generation. Lab managers want to move beyond retrospective reporting toward anticipating problems and opportunities in real time (Labguru, 2025).
This application is newer than AI in scientific applications, like drug discovery. While pharmaceutical R&D has had over a decade to integrate AI workflows, many labs are just starting to explore business-focused use cases.
Barriers to Adoption
Adopting AI goes beyond simply buying a software license. Many labs face challenges with the fundamental prerequisites for AI readiness.
One significant hurdle is the lack of digitalization. Paper-based processes are still prevalent, which not only slows down operations and also makes it difficult to generate the clean, structured data necessary for AI.
Even in labs that use electronic systems, data silos are a major obstacle. Information is often scattered across incompatible platforms or departments, blocking the integrated analysis that AI relies on.
Finally, security, privacy, and trust remain critical concerns (Labguru, 2025). Laboratories handle sensitive data, including patient information and proprietary research results. Applying AI responsibly requires strong governance to ensure data is used ethically, securely, and in compliance with regulations.
Will Labs Modernize to Match AI’s Potential?
Precision medicine holds the promise of delivering highly individualized treatments accessible to a broader population. To fully realize this potential, laboratories must continue to evolve.
While scientific advancements are well under way, with AI accelerating biological research and new drug design, it’s crucial that operational aspects also modernize.
“The efficiency of AI, therefore, needs to extend to laboratory and manufacturing processes to ensure these treatments can reach the market quickly, safely, and affordably.”
The key question: Will labs be able to take advantage of AI’s efficiencies and insights to replace their inefficient, paper-based processes?
Successfully doing so would not only unlock scientific discovery but also streamline business operations, from self-optimizing manufacturing lines to predictive supply chains.
Ultimately, this evolution is about more than just cutting costs. It’s about improving patient access to critical lifesaving therapies, reducing waste in healthcare systems, and establishing precision medicine as the standard of care.
The Real AI Revolution: Efficiency, Equity, and the Future of Care
AI’s role in precision medicine is no longer a futuristic vision. AI is already transforming precision medicine, driving breakthroughs in drug discovery, genomics, pharmacology, and molecular design.
Yet the greatest impact, however, may come from applying AI to the business operations of pharmaceutical companies and laboratories. By addressing inefficiency in manufacturing, maintenance, supply chains, and daily lab operations, AI can help deliver treatments faster and more affordably. Realizing this vision, however, requires addressing real challenges, such as digitalization gaps, data silos, and the ethical stewardship of sensitive information.
Labs and pharmaceutical companies that embrace and integrate AI throughout their entire value chain will lead the next era of precision medicine, making it more effective, accessible, and sustainable for everyone.
References
Labguru. The future of digital lab operations [White paper]. Labguru. 2025. Available from: https://www.labguru.com/the-future-of-digital-lab-operations-white-paper
Sedano R, Solitano V, Vuyyuru SK, Yuan Y, Hanžel J, Ma C, Nardone OM, Jairath V. Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review. Therap Adv Gastroenterol. 2025 Feb 23;18:17562848251321915. doi: 10.1177/17562848251321915. PMID: 39996136; PMCID: PMC11848901.
Serrano DR, Luciano FC, Anaya BJ, Ongoren B, Kara A, Molina G, Ramirez BI, Sánchez-Guirales SA, Simon JA, Tomietto G, Rapti C, Ruiz HK, Rawat S, Kumar D, Lalatsa A. Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics. 2024 Oct 14;16(10):1328. doi: 10.3390/pharmaceutics16101328. PMID: 39458657; PMCID: PMC11510778.
Taherdoost H, Ghofrani A. AI’s role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy. Intell Pharm. 2024;2(5):643-650. doi: 10.1016/j.ipha.2024.08.005.
Oana I. Lungu – enlightenbio guest blogger
Oana I. Lungu, PhD, is a life sciences and informatics expert driving digital transformation across research, diagnostics, and manufacturing. With a background that spans scientific research, bioinformatics, and enterprise software, she specializes in bridging the gap between science and technology. At L7 Informatics, she led product management for standardized workflows and integrations, and partnered with organizations to advance their digitalization and AI-readiness journeys through future-ready architectures in data-rich platform implementations. With a unique ability to translate scientific complexity into business value, Oana is passionate about shaping the next generation of digital laboratories. She holds a Ph.D. in Biochemistry and Biophysics from the University of North Carolina at Chapel Hill.
