We asked our 2026 Tech Trends contributors a simple but loaded question: What do you think is the most compelling use case for AI in our industry? The answers ranged from the philosophical to the pragmatic, but two big themes emerged: AI as a turbocharger for research, and AI as a liberator of human time. (And we’d argue those two themes are really one…) 

AI as a Research Accelerator

If there's one idea that generated the most excitement, it's the possibility that AI fundamentally speeds up the pace of discovery.

Andrew Smeall, VP of Product Innovation at Sage Publications, pointed to AI's potential for "accelerating the pace of discovery through removing barriers to knowledge, automating literature reviews, improving reproducibility, generating hypotheses, and powering in silico research in some areas of STEM."

Jessica Miles, Founder of The Informed Frontier, put it simply: "Definitely tools that accelerate research discovery and impact. I think we're only going to see a growth in these types of applications."

Jeremy Little, AI Technology Lead at Silverchair, offered a useful framing for publishers trying to make strategic decisions in a fast-moving environment: "The most compelling use case is AI as a research accelerator. While specific tools will evolve, this acceleration of research is permanent. Publishers should build for adaptability rather than betting on specific technologies."

Nicholas Liu, Lead Strategic Analyst, AI at Oxford University Press, went further — raising a provocative possibility that AI could help surface the science we almost never see. "What if AI tools could make it less onerous to publish a machine-readable version of record on a 'no effect' finding?" The null hypothesis, finally getting its moment.

AI as a Liberator of Human Time

The second major theme was less about research and more about the people doing the work, including what becomes possible when AI takes on the drudgery.

Michael Di Natale, Director of Journal Production and Platform at AACR, argued that the internal case is actually more compelling than the external one: "I think the performance gains and automation of workflows within the peer-review, production, and publishing processes offer the most compelling cases for the adoption of AI tools, much more than external-facing products."

Dawn Melley, Senior Director of Publishing Operations at IEEE, echoed that sentiment: "AI can help us make better tools in order to save valuable human resources for the important and intellectually challenging things we do to ensure that we are publishing high-quality science."

Adam Day, CEO of Clear Skies Ltd, said it most memorably: "[The best use case for AI is] anything that takes the pain out of work without taking its soul."

AI for Peer Review

Perhaps nowhere is the potential for meaningful, near-term AI impact more concrete than in peer review — a process that has long been strained by volume, inconsistency, and a shrinking pool of willing reviewers. Contributors were optimistic that AI could help, without replacing the human judgment that makes peer review worth having in the first place.

John Challice, Hum’s own SVP of Business Development, framed it as a matter of priorities: "I think publishers will direct AI first at the biggest problems facing scholarly publishing, which I'd say are improving research integrity and improving the peer review process and experience.”

Dawn Melley pointed to triage as a particularly high-value entry point: "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."

IEEE relies on Alchemist Review to do exactly that - helping editors quickly understand incoming manuscripts and assess journal fit before valuable editorial time is ever on the line.

Teo Pulvirenti, Vice President of Global Editorial Strategy at ACS Publications, sees the potential for AI in peer review extending all the way to equity and access: "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."

The Bottom Line

Whether you look at AI through the lens of research impact, operational efficiency, or the integrity of the peer review process, the common thread is the same: the goal isn't replacement, it's amplification. AI doing the heavy lifting so that researchers can think, editors can judge, and publishers can focus on what they do best.

Want to hear directly from the leaders shaping this conversation? Download the full 2026 Publishing Tech Trends Report from Hum and Silverchair.