How artificial intelligence is reshaping pharmaceutical operations and drug discovery was the focus of recent statements from industry leaders. Pfizer CEO Albert Bourla credited AI for enabling the company to cut billions in spending across research, development, and administrative operations over the past year. “We didn’t just cut cost, what we did is we improved productivity,” Bourla said during a February 3 call on fourth quarter earnings. “And the main lever—of course, there [were] simplification efforts that also took place—but the main lever was the successful deployment of AI.”
Executives at Pfizer outlined how AI has streamlined processes throughout the organization. The use of artificial intelligence is now seen as an expectation rather than a future prospect among investors and analysts in the sector.
Bourla stated, “The technology is ready now.” However, industry experts note that while AI’s ability to uncover new biology or design entirely novel drugs remains unproven, it has brought significant improvements to other parts of drug development. John Wu of BCG told BioSpace, “The real promise of AI that people were so excited about is that it can uncover new biology. It can design new drugs that humans otherwise couldn’t do or will take forever to do. I think the proof of that has yet to emerge, but we’re getting closer.”
Eli Lilly recently announced several partnerships focused on integrating AI into drug discovery. In January, Lilly revealed a collaboration with NVIDIA to establish a co-innovation lab using NVIDIA’s BioNeMo platform and Vera Rubin architecture, with up to $1 billion invested over five years. Another deal with Chai Discovery aims to design novel biologic therapeutics using staff experienced in large language models and advanced research.
Lilly’s TuneLab initiative gives select biotechs access to its suite of predictive models in exchange for data sharing—a move designed to grow their technological capabilities and nurture early-stage innovation through its Catalyze360 program. Aliza Apple, VP of Catalyze360 and global head of Lilly TuneLab, explained: “We’re not really at the frontier model, leading edge trying to figure out where the next breakthrough in architecture is going to be. We’re much more focused on what is actually useful today, and how do we make sure that beyond Lilly’s walls, our biotech ecosystem is able to actually take advantage of that as well.” She added: “We live in a predict-first era already… That’s no longer aspirational; that’s operational.”
At Pfizer, further investments are being made into computing infrastructure by adding more than 1,200 GPUs for AI-driven projects spanning R&D to marketing. CFO Dave Denton said these efforts have allowed Pfizer to absorb additional R&D demands following multiple acquisitions while planning $11 billion in R&D spending for 2026.
Chief Scientific Officer Chris Boshoff noted: “Productivity is speed and cost. We bring costs down by embedding AI and, obviously, accelerating speed.” Aamir Malik, chief U.S. commercial officer for Pfizer said: “We invest more time with physicians rather than behind screens,” highlighting how AI enables sales teams to optimize their interactions.
AI tools have also helped Pfizer quickly adapt marketing materials for different international markets with varying regulatory requirements. Chief International Commercial Officer Alexandre de Germay described how tasks previously requiring manual adjustments are now automated through AI systems.
Other major pharmaceutical companies are taking similar approaches but remain cautious about expectations for immediate breakthroughs from AI-driven drug discovery alone. Fiona Marshall at Novartis commented: “It’s not a magic panacea… It can replace some bench-level science but not all.” AstraZeneca reports faster target drug design using AI—more than 50% improvement according to Mohit Manrao—and uses synthetic control arms based on real-world data in clinical trials.
Novartis utilizes its proprietary database called data42 alongside custom-built algorithms for target identification and molecule design work. Bristol Myers Squibb (BMS) is leveraging its partnership with Insitro applying reversion screening techniques powered by machine learning in ALS research—a disease area historically resistant to new therapies.
Robert Plenge from BMS shared: “We’re beginning to find previously unknown biology that maps to some of the genetic nodes that we’ve actually talked about.” He added his perspective on progress: “I don’t think there’s going to be this magic unlock in the next 12 to 24 months… But if you take the long-term view, AI is going to be an important part of that.”
Tech firms like NVIDIA are expanding their involvement through collaborations with pharmaceutical companies such as Novo Nordisk and Amgen while investing early-stage capital into biotechs like ArsenalBio—whose CAR T programs were developed using AI-enabled T cell modeling.
Clinical milestones have begun emerging from these partnerships; Takeda reported positive late-stage results from an AI-designed molecule zasocitinib developed with Nimbus Therapeutics.
Apple observed rapid evolution within predictive modeling tools stating: “There’s interesting data points… but we’ve also seen that the models have improved dramatically over the time those were in clinical development.” She emphasized ongoing hopes for success among current clinical assets derived from earlier versions of these technologies.
While operational efficiencies remain clear gains today—with growing adoption across manufacturing optimization and regulatory workflow modernization—the search continues for more transformative applications capable of producing entirely new medicines.
