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AI is being used across pharmaceutical research, clinical operations, manufacturing, supply chains and regulatory work—but “deployment” can mean anything from a company-reported workflow to a proof of concept. These 11 examples show what organizations say they are doing, how mature each effort is, and what the available evidence does—and does not—establish.
What counts as an AI deployment in pharma and biotech?
The examples below are not all equivalent. Some describe ongoing company workflows or development programs; others are partnerships, internal pilots or proof-of-concept work. Company annual reports and filings are useful records of what a company says it does, but their performance claims are not independent measurements. FDA examples concern the agency’s regulatory work, not a pharmaceutical company discovering a medicine.
AI can help prioritize data, candidates or tasks, but the cited cases do not show it replacing laboratory experiments, clinical evaluation or expert judgment. The distinctions in maturity and evidence matter as much as the technology label.
How is AI being used in drug discovery and development?
1. Pfizer: AI-supported search for oncology compounds
Pfizer’s 2024 annual review describes OncoScout, an AI-powered platform that supports the search for compounds with the potential to become cancer medicines. This is a discovery workflow, not evidence that OncoScout has produced an approved medicine.
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2. Recursion: a connected experimental and computational platform
Recursion describes Recursion OS as combining proprietary experimental data with computational models across target discovery and development. The company’s 2024 Form 10-K presents the platform as linking wet-lab experiments and computational cycles. That is the company’s account of its platform, rather than an independent assessment of its performance.
3. Recursion: AI-supported clinical-stage programs
The same filing describes clinical-stage programs and says REC-617 was optimized using Recursion’s AI platform. It also reports clinical updates for multiple candidates. These disclosures establish that the company connects its platform to drug-development work; they do not show that AI alone caused clinical progress or demonstrated efficacy.
4. Recursion with Roche and Genentech: perturbation maps
Recursion reports producing whole-genome and chemical perturbation maps in gastrointestinal oncology, as well as a neuroscience phenomap, under its work with Roche and Genentech. The filing says the neuroscience phenomap triggered a milestone. Both the milestone and the platform’s contribution are reported by Recursion; this is a partnership and discovery-stage result, not a clinical outcome.
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5. Recursion with Sanofi: targets advanced toward lead optimization
Recursion reports milestones for advancing immunology and oncology targets into lead optimization in its collaboration with Sanofi. This places the reported work in discovery and preclinical development. It does not establish that the targets became clinical candidates or treatments.
6. Recursion with Bayer: oncology data packages and workflow software
Recursion says it delivered 25 multimodal oncology data packages to Bayer and provided LOWE, an LLM-orchestrated workflow tool. The filing does not quantify downstream clinical benefit, so the reported deliverables should not be mistaken for evidence of improved patient outcomes.
7. Recursion with Merck KGaA: target identification partnership
Recursion describes its alliance with Merck KGaA as a collaboration to identify potential first-in-class or best-in-class targets in oncology and immunology. This is a discovery partnership: the stated aim is target identification, not a report of a resulting medicine.
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How are AI and data being applied to clinical trials and evidence?
8. Takeda: data- and AI-assisted recruitment and protocol optimization
Takeda says it can use data and AI to identify potential trial participants, facilitate recruitment and optimize protocols in its 2024 CEO annual letter to shareholders. The letter does not quantify recruitment gains or establish how much AI, as distinct from other data tools, contributes to those activities.
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9. Takeda: real-world evidence for GammaGard Liquid
Takeda says the January 2024 U.S. FDA approval of GammaGard Liquid for chronic inflammatory demyelinating polyneuropathy (CIDP) relied in part on a real-world evidence study using licensed databases. The company says this approach reduced timelines by several years. Those are Takeda’s account and timeline claim; the cited description does not say AI generated the evidence or drove the approval.
Where else are companies applying AI?
10. Takeda: manufacturing quality proof of concept
Takeda describes AI and machine-learning proof-of-concept work for risk classification and summarizing manufacturing deviations. The company says it planned to scale the work after testing. This is explicitly proof-of-concept activity with planned scale-up—not evidence of established deployment across Takeda’s manufacturing network.
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Additional company-reported workflows
Beyond the 11 examples, Pfizer’s 2024 annual review describes AI and machine learning for demand prediction across about half of its products, a digital assistant to forecast potential shortages, AI-assisted manuscript preparation and AI-directed advertising targeting. These illustrate supply-chain and commercial uses alongside research and development. Pfizer also attributes a 40% reduction in first-draft manuscript production time to its Medical AI Assistant and a 15% reduction in overall manuscript submission time to the tool. The company reports an estimated $735 million annual impact from AI use cases; that is a company-wide estimate, not an independently audited causal measure.
Takeda also describes a digital twin for distribution and logistics and predictive analytics for personalized engagement with health-care providers. These are company-reported operational examples, not quantified clinical outcomes.
11. FDA: generative AI-assisted scientific review
In a May 8, 2025, announcement, the FDA reported completing its first generative-AI scientific-review pilot and set a goal of deploying a common secure system across its centers by June 30, 2025. The announcement records the target; it does not establish that every center completed deployment on schedule. This is agency use of AI to support scientific review, not a pharmaceutical company using AI to discover a drug.
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FDA reviewer Jinzhong (Jin) Liu, deputy director in the Office of Drug Evaluation Sciences, Office of New Drugs, CDER, said of the pilot: “This is a game-changer technology that has enabled me to perform scientific review tasks in minutes that used to take three days.” That is a first-person account of the pilot, not a validated average performance result. FDA’s announcement provides the pilot context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does FDA’s regulatory activity say about AI in drug development?
FDA CDER’s Artificial Intelligence for Drug Development page describes AI applications across the drug-product lifecycle and discusses 2025 draft guidance on AI information intended to support regulatory decisions about safety, effectiveness or quality. CDER says its guidance work drew on experience with more than 500 submissions containing AI components from 2016 through 2023. That is a count of submissions with AI components—not a count of approved AI-developed medicines.
FDA’s Center for Biologics Evaluation and Research describes a risk-based review approach that considers the AI method, its specific application, and product and clinical considerations in its AI/ML information for biological and other products it regulates. Together, these materials indicate that regulators are considering AI within specific contexts and applications. They do not amount to blanket validation or approval of medicines developed with AI.
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For any reported use, ask what work AI supports, how far along that work is, and what outcome has actually been reported. A platform description, a partnership milestone, a proof of concept and a clinical result are different kinds of evidence. Company disclosures can establish what a company reports doing, but a company-reported time saving or financial impact should remain attributed to that company.
Quick Recap
- Identify the lifecycle stage: discovery, clinical operations, manufacturing, supply chain, regulatory review or commercial activity.
- Check maturity: distinguish an ongoing workflow or clinical-stage program from a collaboration, internal pilot or proof of concept.
- Separate tool from outcome: a data package, target or workflow delivery does not by itself establish clinical benefit.
- Look for the evidence source: FDA materials describe regulatory or agency activity; company filings and annual letters describe company-reported work.
- Keep human and conventional validation in view: the cited examples do not establish AI as a substitute for experimental, clinical or regulatory judgment.
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