AI's most compelling use cases in publishing include improving workflow efficiency, enhancing personalization, and democratizing access to tools and content. It offers opportunities to streamline processes such as peer review, integrity checks, and summarization, while enabling small publishers to adopt advanced features previously limited to larger organizations. AI could also transform scholarly communication by facilitating more dynamic, targeted formats like concise summaries or interactive visuals, helping researchers uncover novel insights and engage diverse audiences effectively.
What do you think is the most compelling use case for AI in our industry?
“Taxonomy.” – Mike Di Natale, AACR“Lean workflow analysis focused on adding virtual FTEs to each and every step where we do the bare minimum / deliver poor quality / low output due to people constraints. Meaning, AI-ing up = staffing up.” – Paul Gee, AMA/JAMA Network
“For detecting articles and images produced by AI!” – Andrew Pitts, PSI
“The ability to provide personalized content recommendations to our readers. In an era of information overload, readers are often overwhelmed by the sheer volume of available content. By using AI to analyze user preferences, browsing patterns, and research interests, we can curate personalized recommendations that enhance the user experience and increase engagement with our publications.” – Lou Peak, The International Bunch
“Given our primary end user group is heavily engaged in research, I think the most compelling use case is the one where AI allows them to push the boundaries of their research by presenting unexpected and maybe even novel relationships and connections within the published content. While not being a source of that research specifically, AI could become a tool that gets researchers challenging their own assumptions and heading off on unexpected journeys.” – Alistar Reece, GeoScienceWorld
“AI as equalizer - allowing even small publishers and society-based operations to adopt new tools and content features previously available only to well-resourced publishers with large development teams.” – Lauren Kane, BioOne
“There is huge potential in education, the ability for students to create personalized quizzing that addresses their personal knowledge gaps will be a game changer. But also, to assist peer reviewers in focusing their attention on the most important aspects of papers. Completeness checks. Data checks. Code checks. Reference checks. Plagiarism checks.” – Heather Staines, Delta Think
“AI's most compelling use cases are democratizing research access and promoting equality among researchers, with language services being a key example. It can streamline workflows like integrity checks, reducing costs. Currently, we combat AI challenges with human intervention, yet many human tasks can be assisted or replaced by AI, making it a valuable tool to manage increasing demands efficiently. While AI has risks, it offers valuable benefits. However, LLMs are not true AI but tools based on experience, lacking reasoning. We're still early in adoption, and AI is one of many tools, not a complete solution.” – Christian Grubak, ChronosHub
“AI in Peer Review. It is the area most in need of help and, being data rich, it provides a fertile opportunity for innovation.” – Richard Bennett, Hum
“There are so may compelling uses, I don't know that I can home in on a single use case, unless we look at it very broadly and say that AI is a tool to help us do things better, faster, and more efficiently. There is better search and discovery - that's almost a given, to increase efficiency/save time for researchers. The ability to monitor research integrity to a greater and greater degree to ferret out bad actors is incredibly important. Summarization is certainly an area where AI can really offer greater efficiencies in terms of developing content to broaden the reach of research for scientifically related audiences and the lay public as well. Those are just a few areas, but there are many more. And then of course there are workflow efficiencies throughout the publishing process.” – Lori Carlin, Delta Think
“One of the most compelling use cases for AI in our industry is transforming the future of the scholarly paper as the de facto format for scholarly communication. As AI advances, researchers will be able to generate accurate analyses of data and produce results and conclusions tailored for both machine and human consumption. This raises the question: will there still be a need for the traditional, longer-form narrative (the scholarly paper) to communicate findings? AI could enable a shift towards smaller, more targeted communication formats that are better suited to specific audiences. Researchers might produce concise summaries, interactive visuals, or multimedia that highlight key findings and insights. These could be more accessible and engaging for different stakeholders, including policymakers, professionals, and the general public. AI-driven tools could also provide real-time updates and dynamic content, allowing research to be disseminated more quickly and efficiently. This could lead to a more agile and responsive scholarly communication ecosystem, where the focus is on delivering relevant information in the most effective way possible.” – Josh Dahl, Silverchair
Read the full 2025 Publishing Tech Trends Report here, and subscribe to our newsletter for more insights.