The AI Solutions Ideathon roundtable, held in London on September 10, 2024, brought together tech leaders from top publishers and societies to discuss the evolving role of AI in scholarly publishing.
Organized by CACTUS, the roundtable aimed to address industry challenges, explore innovative solutions and collaboration between publishing stakeholders. Key themes included peer review reform, AI-driven editorial workflows, discoverability, research integrity and user engagement. The discussions highlighted both the opportunities and challenges of integrating AI into existing publishing systems, emphasizing the need for standardization, end-to-end solutions and stronger industry partnerships.
From peer review inefficiencies to discoverability challenges and AI integration roadblocks, the discussions provided deep insights into how publishers are navigating this changing landscape. Here are the five key takeaways from the roundtable.
- AI is Gaining Traction, but Standardization is Crucial
AI is increasingly being used in various areas of scholarly publishing, such as journal cascading and AI-driven rejection analysis. While these advancements are promising, the industry lacks a universal standard for AI implementation. Editors and publishers prefer AI as a tool that supports decision-making rather than fully automated systems. A key concern is that AI models must be transparent and explainable for editors and researchers to trust them. Without clear guidelines, AI adoption remains fragmented across different publishing workflows. Standardization would help ensure consistency and streamline the integration of AI across the industry, making it easier for publishers to adopt AI-driven solutions with confidence.
- Peer Review Needs Urgent Innovation
Peer review remains one of the biggest bottlenecks in scholarly publishing due to inefficiencies, slow turnaround times, and a lack of standardization. Finding qualified peer reviewers is increasingly difficult, exacerbated by the fact that 35% of researchers have not published in the last three years, shrinking the available reviewer pool. Additionally, there is no consistent assessment of peer review quality, making it harder to maintain rigorous evaluation standards.
One of the solutions could be AI-powered peer reviewer matchmaking, which can help connect manuscripts with the most suitable reviewers. Another approach is offering incentives to peer reviewers, such as exclusive access to valuable research tools, to encourage long-term engagement. Building a peer review community that provides training and recognition could also improve the system, ensuring that reviewers remain motivated and committed to the process.
- Discoverability is Evolving: Are Publisher Websites Becoming Less Relevant?
Researchers are shifting away from directly visiting publisher websites, instead discovering content through Google and AI-generated search results.
While SEO remains important, traditional engagement metrics such as “time on site” are losing relevance. A promising strategy for increasing visibility is using video abstracts on platforms like YouTube, which can help research reach a wider audience. At the same time, improvements in metadata and structured data are necessary to ensure that articles are easy to find in an AI-driven search landscape. As AI-powered discovery tools become more dominant, publishers need to prepare for a future where their websites are no longer the primary destination for researchers.
- Research Integrity & Retractions Need More Transparency
Managing research integrity and retractions is a major challenge for publishers, as there is no standardized approach for handling retractions across the industry. Legal teams often struggle with retraction cases due to a lack of clear procedures. One of the biggest issues is tracking author identity and affiliations, making it difficult to connect past and present retractions. Without a proper system, retracted papers may not always raise the necessary concerns about an author’s previous work. AI-driven solutions could help by automating retraction monitoring, flagging retracted authors, and linking them to previous cases. Better metadata tracking could also provide a clearer history of an author’s publication record, making it easier for publishers to manage research integrity. Additionally, ensuring consistency between public and private messaging about retractions would improve transparency and trust within the academic community.
- AI Integration is a Major Challenge for Publishers
Despite growing interest in AI, integrating these technologies into existing publishing systems remains a significant hurdle. Many manuscript tracking systems are not designed to accommodate AI-driven workflows, making implementation difficult. Legacy systems add another layer of complexity, as publishers struggle to modernize their infrastructure while maintaining continuity. Beyond technical challenges, change management is a key issue—internal decision-makers often lack the technical expertise needed to oversee AI adoption effectively. Without a clear strategy for integration, AI’s potential to improve publishing workflows remains limited. Publishers must find ways to bridge these gaps, ensuring that AI solutions can be seamlessly incorporated into their systems without disrupting existing processes.
Conclusion
The roundtable discussions highlighted AI’s growing role in scholarly publishing while emphasizing the need for standardization, better peer review incentives, evolving discoverability strategies, improved research integrity, and seamless AI integration. As publishers navigate these challenges, collaboration between industry leaders and technology innovators will be key to shaping the future of scholarly publishing.
If you’re looking to explore AI-driven solutions or discuss how these insights can shape your publishing workflows, we’d love to connect. Get in touch with us to learn more.