For more than two centuries, Wiley built its business on publishing scientific journals and textbooks. Now, the company is repositioning itself for an AI-driven world — not only as a destination for research, but as the data layer powering it.
Last month, Wiley announced a partnership with AI startup OpenEvidence that will bring hundreds of peer-reviewed journals and medical references directly into physicians’ workflows. It typically takes years for medical research to find its way to patients. The agreement lets doctors access medical research in real time on OpenEvidence’s platform, as they are treating patients.
“It takes 17 years from testing a hypothesis all the way through to publication and getting it embedded into practice,” said Josh Jarrett, Wiley’s senior vice president of AI growth, in an interview with The AI Innovator. “We’re trying to shorten that time horizon so that content can be used by clinicians.”
The speed improvement is astonishing: The night an article gets published to the Wiley Online Library, it will be published within 24 hours to Open Evidence’s system. If a doctor does a search on the platform, the updated information will be provided.
Wiley publishes roughly 300,000 journal articles each year across about 2,000 journals, much of it highly specialized and accessible primarily through subscriptions. But historically, most of that research reaches clinicians only after passing through multiple layers of synthesis and validation.
The agreement includes access to the Cochrane Database of Systematic Reviews, widely used to inform clinical guidelines. Systematic reviews combine results from many studies to find the best answer to a medical question.
AI enables systems to ingest, search and surface large volumes of research in real time. Wiley’s strategy is to ensure that the data feeding those systems is authoritative, licensed and traceable.
“It’s about context and understanding what the situation is that the clinician is seeing based on what they put into their query, and then being able to cite the right evidence,” Jarrett said.
Doctors access links in the search results to read the actual research, as a way to double-check answers in case the underlying AI model hallucinates.
Wiley’s pivot reflects a broader transformation underway in academic publishing: Value is shifting from content distribution toward embedding of high-quality, licensed data directly into workflows where decisions are made. In health care, that means bringing research closer to the bedside.
From publisher to data provider
For Wiley, that insight is reshaping both its role in the health care ecosystem and its business model.
“We see ourselves as a really critical player in health care AI because people are using AI tools more and more in health care,” Jarrett said. “These tools are only as good as the evidence that they’re able to cite, and so what we’re trying to do is actually connect those dots.”
In March, Wiley announced a partnership with Microsoft Dragon Copilot that gives the ambient listening tool access to its medical content. This tool listens to doctor-patient conversations and turns them into notes, summaries and clinical information.
It also has a partnership with AWS in which Wiley’s life sciences and Cochrane library are available as plug-ins for agents and other AI systems to check medical evidence, Jarrett said.
That approach marks a departure from Wiley’s traditional role as a publisher. In the past, the company’s primary product was access to journals and books — essentially static content for a relatively narrow audience of researchers and clinicians. AI is expanding both the audience and the ways that content is consumed.
“We think about ourselves increasingly as a data analytics and a data services company,” Jarrett said.
Instead of selling individual articles or subscriptions to libraries, Wiley is building APIs and data services that allow enterprises to integrate its content directly into their own systems. In health care and life sciences, that includes clinical decision support tools, drug discovery platforms and research workflows.
The model is shifting toward continuous access to updated data streams rather than one-time purchases. “You want that feed of constantly updated knowledge and be able to hit that API in real time,” Jarrett said.
That change has significant implications for the company’s economics. By moving closer to the point of decision-making, Wiley can capture more value from its content and expand its addressable market beyond traditional academic users.
Jarrett described the potential as a step change in scale, driven by AI’s ability to make complex research accessible to a much broader audience.
A focus on deep data
Wiley’s strategy also reflects a growing divide in AI between systems trained on broad, publicly available data and those built on specialized, proprietary datasets.
Much of the recent attention in generative AI has focused on large language models trained on internet-scale data. But in regulated industries such as health care, those models face limitations due to concerns about accuracy, bias and lack of traceability.
“What we’re trying to do is actually connect to deep stores of content that’s not publicly available on the web,” Jarrett said.
That deep data includes peer-reviewed journal articles, systematic reviews and reference works that have undergone rigorous editorial and validation processes. It is also typically behind paywalls, making it inaccessible to many general-purpose AI systems. By licensing and structuring that content for AI use, Wiley is positioning itself as a critical supplier in the emerging AI ecosystem.
The company has also taken a firm stance on how its content can be used, requiring authorization for AI training and emphasizing transparency and attribution. Its AI principles highlight human oversight, data provenance and governance as core requirements for responsible deployment.
Those priorities align with increasing scrutiny from regulators and health care providers, who are demanding greater accountability from AI systems used in clinical settings.
While health care is a primary focus, Wiley is applying the same model to other research-intensive industries. These are industries where the scientific record is constantly changing. Jarrett pointed to pharmaceuticals, materials science and agriculture as key areas of growth.
In each case, the challenge is similar: connecting rapidly evolving research with practical applications in real time.
For Wiley, that represents both a mission-driven goal — accelerating the pace of science — and a commercial opportunity to redefine its role in the AI economy.
Be First to Comment