The Real AI Battleground Is 200 Years Old

For most of the AI boom, attention has centered on the companies building models, chips, and applications. OpenAI, Anthropic, Google, and NVIDIA have dominated the conversation. Academic publishers, for one, have not.

The shift is visible in the recent moves of Wiley, the 219-year-old scientific publishing company. Over the past year, Wiley partnered with OpenEvidence, Microsoft, Anthropic, Amazon Web Services (AWS), and IQVIA. It acquired Emerald Publishing for $452 million and nearly doubled its AI-related revenue in two years.

Other publishers are moving in the same direction. Companies that spent decades collecting and validating knowledge are finding that those archives have a new kind of buyer, as enterprises search for trusted data to train and power AI systems.

Publishers Spent Decades Building What AI Suddenly Needs

Generative AI has made data quality a strategic issue. Large language models can generate convincing answers on almost any subject, but enterprise and scientific applications require a higher standard. Pharmaceutical companies developing drugs and engineers designing products cannot rely on information scraped from the internet.

That gap is where academic publishers come in. Companies such as Elsevier have spent years expanding beyond publishing into research intelligence through platforms such as Scopus, SciVal, and ClinicalKey. Springer Nature recently disclosed that nearly 60 AI tools support various parts of its publishing workflow.

Wiley has spent decades collecting peer-reviewed research across medicine, chemistry, engineering, materials science, and agriculture through the Wiley Online Library. The company now argues that content can function as AI infrastructure.

That strategy is visible in its partnerships. Wiley signed an agreement with OpenEvidence to bring peer-reviewed medical research into clinical AI workflows. It partnered with Microsoft to integrate trusted medical content into Dragon Copilot, the company's AI assistant for healthcare professionals.

Wiley also worked with Anthropic on life sciences-focused AI initiatives and with AWS on scientific research agents designed to access scholarly content. The goal across all of these deals is the same: get Wiley's content embedded inside external AI products before competitors do.

A similar shift is underway in enterprise software. Some companies are reconsidering whether traditional dashboards remain the primary way people interact with information as AI becomes the default interface.

Wiley Wants to Become More Than a Publisher

During its latest earnings call, Wiley management described the company as operating two growth engines: Research Publishing and AI and Data Analytics. Publishing generates proprietary content; AI products, licensing agreements, and analytics services create new ways to monetize it.

AI-related revenue grew from $23 million in fiscal 2024 to $49 million in fiscal 2026, according to the company's fiscal 2026 earnings call. Recurring AI revenue grew from roughly $1 million to $8 million over the same period. Wiley now counts seven of the world's top 10 pharmaceutical companies among its AI knowledge-feed customers.

Wiley is also building Nexus, a content licensing network that aggregates material from dozens of publishing partners, to position itself as a content supplier across the AI ecosystem.

Elsevier has taken a different path, building proprietary research and analytics platforms over several years. Wiley is focused on partnerships and embedding its content inside external AI products. As enterprises move from AI pilots to production deployments, which approach holds up better is an open question.

The New Value of Old Knowledge

Clinical Outcome Assessments (COAs) are structured tools used in clinical trials to measure patient-reported outcomes and treatment effectiveness. For years, they were a minor niche inside Wiley's portfolio.

COA revenue grew from $700,000 in fiscal 2021 to $11 million in fiscal 2026. The underlying data did not change. What changed was the demand for structured, validated information in high-stakes healthcare applications.

As AI moves deeper into clinical settings, healthcare organizations are searching for datasets that reduce risk and improve reliability. That demand is showing up across clinical documentation, recruitment, and patient engagement.

The same dynamic shaped Wiley's decision to acquire Emerald Publishing, expanding its footprint in economics, business, finance, and social sciences. In earlier years, publishing acquisitions were measured by journal subscriptions and scale. Wiley is treating proprietary content as a strategic asset.

That logic is not unique to publishing. John Deere, for instance, is applying its decades of agricultural field data to power autonomous and AI-driven farming systems.

The AI race has largely been framed around model capability. What Wiley and its peers are building a case for is that the data those models depend on carries its own competitive weight. The institutions that spent decades organizing it were not late to the AI era. They were building for it before it had a name.