Pharmaceutical companies have long been at the forefront of artificial intelligence, with investments exceeding $4bn in 2025 alone. According to Mordor Intelligence, this figure is projected to rise to $25.7bn over the next four years.
Historically, AI has been concentrated in drug discovery and development, where it has helped reduce research timelines and expand the search for viable compounds. While fully AI-developed drugs have yet to receive regulatory approval, progress has been rapid: companies are accelerating early-stage clinical candidates and using deep learning to design and optimize molecules. For instance, Insilico Medicine used GENTRL, a generative adversarial network approach, to complete an AI drug discovery challenge within 21 days. A growing number of AI-enabled drug pipelines are now advancing into clinical phases globally, signaling a shift from experimentation to scaled application.
Itās not only pharmaceutical companies driving these advances. Major technology firms play an increasingly central role by developing foundational AI models for life sciences. In June 2025, Nvidia announced its collaboration with Novo Nordisk to accelerate drug discovery by leveraging its open-source machine learning framework (BioNeMO). The growing impact of AI is underscored by the 2024 Nobel Prize in Chemistry, awarded to scientists behind AlphaFold for using neural networkābased AI to predict complex protein structures and enable the design of entirely new proteins.
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Yet, despite this momentum, the most transformative promise of AI in drug discovery remains a longer-term prospect. While early-stage gains are evident, Vas Narasimhan, CEO of Novartis, recently characterized AI investment as critical but ultimately a long game, estimating a āseven- to 10-year timeline [until]… weāll see the benefits of AI in drug discovery.ā At the same time, he emphasized that Novartis is embedding AI across the entire value chain, highlighting the potential to leverage AI well beyond R&D alone.
The pharmaceutical industry operates within a fragmented ecosystem of AI-driven specialists. As illustrated in the IMD AI Healthcare Ecosystem Map, the landscape is segmented into eight critical operational areas where traditional pharma firms and AI-native startups intersect, including within drug discovery and development, precision and personalized medicine, and pharma operations.
Seven global pharmaceutical companies ā AstraZeneca, Merck & Co., Eli Lilly, Novartis, Sanofi, Novo Nordisk, and GSK ā rank among the top 100 most AI-mature firms in the 2025 IMD AI Maturity Index.
This index draws on data from the worldās 300 largest companies and evaluates how effectively organizations embed and scale AI across five dimensions: executive support, technology and infrastructure, operational excellence, workforce and culture, and ethics and risk management. Together, these dimensions highlight a fundamental challenge: success with AI is not driven by technology alone, but depends on the alignment of leadership, people, and systems around a shared purpose. What distinguishes top companies is not just model sophistication, but their ability to integrate AI into workflows and functional support, as we highlight in examples below.
The AI linked mid-stage trial results to data from millions of patients to predict the long-term risks in a population similar to the trial.
Major drugmakers are using AI to find patients for clinical trials quickly, or to reduce the number of people needed to test medicines, both accelerating drug development and potentially saving millions of dollars. For years, companies such as Amgen, Bayer, and Novartis have used AI to scan billions of public health records, prescription data, medical insurance claims, and their own internal data to find trial patients. As early as 2023, this was halving the time it takes to sign patients up for clinical trials. For example, for a late-stage trial for Asundexian, an experimental drug designed to reduce the long-term risk of strokes in adults, Bayer used AI to cut the number of participants needed by several thousand. The AI linked mid-stage trial results to data from millions of patients to predict the long-term risks in a population similar to the trial. Using that data, the German drugmaker was able to start the late-stage trial with fewer participants. Without AI, Bayer said it would have spent millions more, and that it had saved up to nine months in volunteer recruitment.
Leading pharmaceutical companies have embedded AI into their operational processes and workflows, focusing on applications that improve efficiency, reduce costs, streamline internal operations, and enhance business performance. One clear example of efficiency gain is streamlining product maintenance. The end-to-end process for managing both deviation and corrective and preventive actions (CAPAs) is fraught with challenges. Common pain points include delayed detection, manual tasks, a low rate of right-first-time solutions, low effectiveness, inconsistent documentation, and a reactive process. But by using an AI-powered process, Pfizer scientists can detect anomalies as they occur, using a continuous manufacturing process for producing solid oral-dose medicines. The AI-powered process can also predict maintenance needs and reduce equipment downtime.
In Europe, nearly half of physicians pointed to the need for more robust and higher-quality information.
AI is increasingly reshaping pharmaceutical commercial operations, particularly in how companies engage healthcare professionals (HCPs) through personalized, data-driven engagement. A 2025 Bain survey found that 52% of physicians in Asia-Pacific identified the need for a single, consolidated source of content, while 45% highlighted on-demand access to information as key areas for improvement. In Europe, nearly half of physicians pointed to the need for more robust and higher-quality information. These findings highlight a clear opportunity: AI can enable pharmaceutical firms to deliver the right content to the right HCP at the right moment. Technology vendors, such as Salesforce, are actively partnering with pharma companies to support this shift.
Recent McKinsey & Company research suggests that while nearly eight in 10 companies report using generative AI, around 80 percent see no tangible bottom-line impact. The issue is not a lack of promising use cases, but rather the difficulty of scaling these beyond isolated pilots. According to industry executives, this issue is prevalent in the pharmaceutical industry.
Historically, AI has been deployed in isolated pockets within large multinational pharma firms, primarily in R&D, supply chain, and manufacturing, where machine learning is well established. With the advent of generative AI (GenAI), however, the barrier to entry has fallen significantly, enabling applications across a much broader range of activities. While this creates opportunities to bridge previously siloed use cases, it also introduces new challenges related to task interdependencies.
Ben Torben-Nielsen, Strategy and Portfolio Leader for Data Analytics and Insights at Roche, explains: āMost organizations focus on a handful of big AI use cases, but a typical workflow involves 20 to 50 tasks. I focus on embedding AI across as many of those as possible. Given the interdependence between tasks, optimizing just one rarely delivers a meaningful impact. Instead, I work on restructuring entire workflows, especially as some tasks may no longer be necessary with AI.ā
In addition to scaling challenges, industry executives also highlighted a second issue: the current emphasis on efficiency and speed as the primary metrics for AI implementation. David Drodge, AI strategy and digital transformation at Roche/Novartis, argues that this focus is too narrow and risks overlooking AIās broader potential for value creation. As he explains, āCurrent approaches to AI implementation often resemble a ārearview mirrorā view, focused on how things have been done in the past. Instead, pharma firms could leverage AI to build new sources of value. For example, how to leverage the capabilities of GenAI to support more personalized and direct communications with patients in markets where this is possible.ā
Building on this broader view of value creation, a third shift emerges at the level of the healthcare ecosystem. As AI adoption expands, it is not only transforming pharmaceutical operations but also reshaping how care is delivered more broadly. As Nicolas Weber, Head of Innovation & Activation at Novartis, explains: āAn ecosystem approach will be necessary to leverage the full extent of AI. AI is already changing consumer behavior; patients are increasingly turning to AI for initial medical inquiries rather than immediately consulting a physician. At the same time, physicians are using AI to support diagnosis. This means the system as a whole will need to rethink how it operates, while ensuring that LLMs remain up-to-date, scientifically validated, and accessible.ā
Although pharmaceutical companies are only one component of the healthcare system, AI is reshaping how all actors interact. As a result, AI strategies must be developed with system-level dynamics in mind.
Leaders therefore need to define the role their firm aims to play within an AI-enabled healthcare ecosystem.
As AI adoption continues to transform the pharmaceutical industry, leaders should take a holistic view of how their organization is performing and how it may need to be reimagined to drive competitive differentiation. Re-imagination can be approached along three dimensions.
AIās greatest impact may lie not within individual firms, but across the broader healthcare system. In this reimagined landscape, Weber suggestsĀ that trusted partnerships around the latest scientific knowledge and data, interoperability, and governance models connecting actors across the system will become increasingly important.
Leaders therefore need to define the role their firm aims to play within an AI-enabled healthcare ecosystem. Their AI strategies must reflect shifts in care delivery, patient behavior, and clinical decision-making.
AI may challenge core assumptions about how the pharmaceutical industry operates. With increasingly accessible tools, even small firms, startups, or individual researchers can leverage AI to focus on a specific target or indication and develop deep specialization. In this reimagined landscape, Torben-Nielsen suggests that the origins of R&D may shift, potentially altering industry dynamics.
This raises important questions about the traditional vertically integrated pharma model, in which large firms span research, development, and commercialization. Going forward, some organizations may choose to specialize more narrowly within the value chain, concentrating on areas where they can create the most value.
Pharmaās current focus on efficiency risks overlooking AIās broader potential for value creation. AI opens up opportunities in areas such as marketing, sales, patient engagement, and personalization. The challenge is to explore how AI can expand the scope of value creation by reimagining how firms go to market and engage with patients.
Drodge suggests that a shift towards more direct and continuous engagement could strengthen advocacy, influence prescriptions and patient adherence behavior. At the same time, advances in large language models are transforming the context in which HCP engagement occurs. AI is becoming a new point of access to care, reshaping pharmaās traditional role as a primary disseminator of medical knowledge.
The challenge for pharmaceutical leaders is not simply to adopt AI, but to decide where it matters most and how to reorganize around it.
The challenge for pharmaceutical leaders is not simply to adopt AI, but to decide where it matters most and how to reorganize around it. AIās impact will not be confined to isolated use cases or incremental efficiency gains. Instead, it is reshaping how work is organized, how value is created, and how firms position themselves within a broader healthcare ecosystem.
Firms that treat AI as a series of tools risk falling behind. Those that approach it as a strategic reconfiguration of their organization, and industry, will be better positioned to capture its full potential.
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