From manuscript preparation through editorial workflows to peer review and content discovery, AI's influence spans numerous touchpoints in the publishing ecosystem. The report's contributors anticipate that AI will reshape how researchers interact with content, moving from traditional search behaviors toward AI-mediated discovery and synthesis. Editorial and peer review processes stand to gain substantial efficiencies through AI-enabled triage and integrity checks, while publishers face a fundamental shift in how audiences consume content, with implications for subscription models and engagement metrics that have long defined the industry.
Here's what contributors predict will have the most transformative impact on in scholarly publishing:
"There will be growing acceptance of AI tools around manuscript preparation and editorial workflows, for example with the generation of abstracts. There is evidence that many authors are already relying on genAI to summarize something complex like a full paper into something simpler." —Nicholas Liu, Lead Strategic Analyst, AI, Oxford University Press
"How end users engage with publisher content will be heavily impacted by GenAI in the coming year as users from publisher's traditional core audiences migrate to GenAI tools for search and discovery needs. This will directly impact direct usage that subscribers have used for measuring the cost and effectiveness of subscriptions and threaten revenue." —Michael Di Natale, Director, Journal Production and Platform, American Association for Cancer Research (AACR)
"Editorial Workflow Automation - Whilst I don't believe AI will ever replace humans in our editorial processes, I think AI can offer some valuable efficiencies and insight to support the process, improving quality, speed and user experience. Whether the sweet spot is with manuscript triage and screening, supporting integrity checks, or recommending metadata to improve discoverability, there are a wealth of use cases to explore and potentially pilot." —Natalie Jacobs, Chief Product Officer, Emerald Publishing
"Reader interaction. Consumption will shift from search-and-scroll to answer-and-explore, where AI intermediates discovery and synthesis. Content becomes an interactive experience rather than a static asset." —Jonathan Woahn, Chief Experience Officer, Cashmere
"I see great opportunity in the development and use of AI-enabled triage tools in peer review. Spotting unsuitable submissions as early in the process as possible will help to reduce the strain on editors (at all levels) and reviewers." —Dawn Melley, Senior Director, Publishing Operations, IEEE
"Content discovery and usage" —Andrew Smeall, VP, Product Innovation, Sage Publications
"Content consumption is transforming dramatically. Researchers are shifting from traditional search-and-read patterns to having AI agents digest hundreds of papers on their behalf. We're seeing meta-analyses that once took months now completed in days, and literature reviews are fundamentally different with tools like Model Context Protocols. Publishers who recognize this as current reality rather than future possibility will be the ones adapting their platforms successfully." —Jeremy Little, AI Technology Lead, Silverchair
"Discovery. Researchers will care less and less about the journal or even the article as a unit of research consumption. We're living in an "answer economy" now, and, for most, the answer in the summary will be good enough. But "curated" discovery pathways will still be valued by the discerning researchers who may be willing to invest more." —Heather Staines, Senior Consultant, Delta Think
"I believe AI's biggest impact will be reinventing our approach to peer review to improve the experience for authors, reviewers, and editors. Generative AI adds another layer, powering content summarization, translation, and accessibility for authors, thus making scholarly communication more efficient, fairer, and, above all, more inclusive." —Teo Pulvirenti, Vice President, Global Editorial Strategy, ACS Publications
"I think publishers will direct AI first at the biggest problems facing scholarly publishing, which I'd say are (1) improving research integrity and (2) improving the peer review process and experience. AI can also help solve some 'low-hanging fruit' problems, like technical manuscript assessments and journal matching." —John Challice, SVP, Business Development, Hum
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