By Ron Ragsdale, Senior Director of Operations, BAO Systems
As artificial intelligence (AI) becomes more deeply embedded in knowledge production, editorial teams face a strategic challenge: how to responsibly integrate AI tools into publishing workflows without compromising intellectual property, editorial standards, or data security. The question is no longer whether to adopt AI, but how to do so with confidence.
What follows is a practical framework for integrating AI tools in scholarly publishing. The approach balances innovation with governance, enabling publishers to enhance efficiency while preserving integrity.
The Case for Modular AI Integration
There is ever-increasing evidence that AI can add value through efficiency gains in a range of publishing tasks: translating abstracts, summarizing peer reviews, auto-tagging metadata, validating image sources, or visualising structured data, for example. Public tools such as ChatGPT, DeepL, and Microsoft Copilot offer some of these capabilities, but integrating them into editorial workflows without jeopardizing sensitive content can be problematic.
Unlike enterprise publishing platforms with proprietary and in-built AI tools, a modular integration model, such as that enabled by the PublishOne architecture, using open APIs and a real-time event grid, allows publishers to embed best-in-class third-party AI tools as microservices, activating only the functions required. This model promotes agility, reduces vendor lock-in, and supports rapid experimentation without destabilizing core systems.
Example: A biomedical publisher can plug in a language model to assist with simultaneous multilingual translation and summarization of abstracts for online indexes, while disabling all model training and retention of data.
Editorial data, whether peer reviews, author manuscripts, or internal correspondence, is highly sensitive. As such, the first principle of any AI integration must be data security. BAO Systems and PublishOne enforce a secure-by-default policy in all deployments, ensuring that no customer data is retained or used to train third-party models unless explicitly authorized by the client. These security safeguards include:
- Data encryption (in transit and at rest)
- Regional compliance (e.g., GDPR, HIPAA)
- Configurable access controls by user role or workflow stage
This design enables publishers to retain control over proprietary content while leveraging AI for routine, low-risk tasks, such as metadata tagging or formatting assistance.
However, not all use cases can be solved with public models. In legal, medical, or STEM publishing, accuracy and explainability are paramount. The PublishOne platform supports custom LLM deployments, including Retrieval-Augmented Generation (RAG), where the model is grounded in trusted in-house data sources.
Example: A legal publisher might develop a domain-specific summarizer trained on a proprietary database of legal opinions.
Custom models can offer tighter alignment with editorial values, reduce hallucinations, and ensure outputs that can be traced back to validated sources, critical for academic credibility. Furthermore, even the most accurate AI tools require human oversight, so ensuring a human-in-the-loop (HITL) model, where editors can review, approve, or reject AI-generated suggestions, is critical. Features of this model include:
- Pre- / post-processing checks for anomalies
- Feedback mechanisms to improve model accuracy
- Optional bias and performance audits across versions
This hybrid model enables scalability without sacrificing editorial responsibility, aligning with current recommendations from academic and industry ethics boards.
To ensure real-world usability, it is also imperative that all AI integrations undergo rigorous User Acceptance Testing (UAT) in staging environments before deployment. Features can then be validated by actual users, with detailed feedback collected and implemented, with rollout only proceeding after full stakeholder approval. This staged approach minimizes operational risk and provides time for training and documentation, essential for editorial adoption–and as mentioned, can be implemented incrementally and API-ready tool sets tailored for specific tasks in the workflow.
Whether publishing hundreds of journals or launching a new monograph series, this framework is flexible and scalable. In addition to incrementally introducing AI tools, users can experiment in isolated workflows, and then activate or deactivate features without disrupting production.
When integrations are API-driven and activated on an opt-in basis, editorial teams are able to retain full control over when, how, and if AI is used. In a future marked by rapid AI evolution, this flexibility can be a real strategic advantage.
Conclusion: A Model for Responsible AI in Publishing
AI alone cannot ensure quality publishing, but, when integrated responsibly, it can significantly augment human expertise. The framework outlined here offers a secure, flexible, and transparent way to bring AI into editorial workflows. By foregrounding governance, precision, and human oversight, publishers can innovate without compromising trust. As the academic publishing landscape evolves, success will depend not just on using AI, but on using it responsibly and confidently.
About the Author
Ron Ragsdale, 
About BAO Systems
BAO Systems specializes in data and content management, analytics and reporting for the professional, academic, health and development sectors. It helps organizations implement smart, standards-compliant software and workflows that improve efficiency, reduce costs, and deliver measurable results.
About PublishOne
PublishOne is a leading publishing platform that simplifies complex, multichannel content production. Designed for large-scale, multichannel operations, PublishOne enables teams to create, manage, and distribute high-quality content across formats, from JATS-compliant XML to PDF to HTML, all from a single, intuitive interface.
REFERENCES
Anthropic, 2024. Introducing Contextual Retrieval. https://www.anthropic.com/news/contextual-retrieval
COPE (Committee on Publication Ethics), 2023. Artificial intelligence: Understanding current guidance and tools. https://publicationethics.org/topic-discussions/artificial-intelligence-understanding-current-guidance-and-tools
Cohere, 2023. Fine-tuning language models for specific domains. https://docs.cohere.com/docs/fine-tuning
exabeam, 2024. The Intersection of GDPR and AI and 6 Compliance Best Practices. https://www.exabeam.com/explainers/gdpr-compliance/the-intersection-of-gdpr-and-ai-and-6-compliance-best-practices
Harvard Business Review, 2023. 8 Questions About Using AI Responsibly, Answered https://hbr.org/2023/05/8-questions-about-using-ai-responsibly-answered
In Publishing, 2024. AI Governance in publishing: ensuring ethical, compliant content creation https://www.inpublishing.co.uk/articles/ai-governance-in-publishing-ensuring-ethical-compliant-content-creation-24773
Meta, 2024. Making protection tools accessible to everyone https://ai.meta.com/llama/
STM (International Association of Scientific, Technical and Medical Publishers), 2023. New white paper launch: Generative AI in Scholarly Communications https://stm-assoc.org/new-white-paper-launch-generative-ai-in-scholarly-communications/
World Economic Forum, 2023. Why we need to care about responsible AI in the age of the algorithm https://www.weforum.org/stories/2023/03/why-businesses-should-commit-to-responsible-ai/
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