Academic publishing is enabled by expert human judgment, yet much of the system surrounding that judgment is administrative. From manuscript triage and reviewer selection to compliance checks and production workflows, a large share of effort is spent on coordination rather than evaluation.
As submission volumes grow and expectations for speed increase, this imbalance is becoming harder to sustain.
Artificial intelligence (AI) is increasingly being applied to address this challenge. The most meaningful impact is not replacing editorial or peer review decisions but reducing the friction that surrounds them. Instead of asking whether AI can be used in publishing, we should consider where it truly adds value and in which situations it can be trusted.
Where AI makes a difference, and where it doesn’t
Much of the discussion around AI in publishing focuses on its potential to transform research assessment. In practice, the most immediate gains come elsewhere: administrative workflows.
Editors spend time checking whether a manuscript fits a journal’s scope and submission guidelines, and time identifying suitable reviewers. These steps are essential, but repetitive and time-consuming. AI systems can assist by analysing manuscript structure, subject relevance, and policy alignment before human review begins. This helps to prioritise and route submissions more efficiently.
When these tasks are streamlined, editors can focus on what matters most: evaluating scientific contribution, interpreting reviewer feedback, and making informed decisions. In this context, the value of AI lies less in automation itself and more in how effectively it frees human attention.
However, not all AI is equally suited to this environment. In scientific publishing, where decisions shape the permanent scholarly record, systems must be grounded in high-quality, curated data and designed to support verifiable outcomes. This is where domain-specific approaches – AI built for science and healthcare – become critical.
General-purpose models may assist with surface-level tasks, but higher-stakes workflows demand what is increasingly described as precision AI: systems that are transparent, evidence-based, and built to meet the standards required in research contexts.
Addressing scale without lowering standards
Scale is one of the biggest pressures in academic publishing. Submission volumes continue to rise, while the availability of reviewers does not keep pace. The result is growing strain on editorial teams and slower publishing times.
AI can help address this imbalance, particularly in reviewer identification. AI systems can generate candidate lists more quickly than manual methods by analysing publication history, expertise, and prior activity. In doing so, this reduces the search time and improves the likelihood of finding appropriate reviewers.
The result is not just faster processing, but more consistent workflows. Shorter administrative cycles can improve turnaround times for authors and readers alike. But speed is not the goal on its own; quality and fairness in peer review must be maintained.
This is where design matters. AI tools need to be transparent about how recommendations are generated and should allow editors to retain full control over final selections. Without this, efficiency gains risk being undermined by reduced confidence in the process.
Learning from parallel sectors
These challenges are not unique to publishing. There are parallels with other knowledge-intensive fields, such as healthcare, where highly trained professionals spend significant time on administrative tasks.
In clinical settings, documentation, data entry, and system navigation often reduce the time available for patient care. Studies, such as Elsevier’s Clinician of the Future, show that many clinicians increasingly view AI as a way to streamline these workflows and reclaim time for direct patient interaction.
In both cases, AI creates value by allowing experts to focus on core responsibilities rather than peripheral work. The lesson is not that publishing and healthcare are the same, but that the same principle applies: AI should support professional judgement, not replace it, and it must be built to meet the standards of the domain it serves.
Trust as a prerequisite, not an outcome
The effective adoption of AI in publishing depends less on technical capability and more on trust. Editors, reviewers, and researchers need confidence that AI systems are reliable, that their limitations are understood, and that they are aligned with the norms of scholarly communication. No AI system is entirely free from bias, but there are ways to recognise where bias may arise and take steps to minimise its impact.
There are several factors which shape this trust:
- Transparency: This is non-negotiable. Users are more likely to accept AI recommendations when they understand how they are generated, including the data used, ranking logic, and limitations. Being explicit about potential sources of bias is part of this transparency.
- Accountability: Publishing contributes to the permanent scholarly record, so responsibility for decisions must remain with the human. Clear governance is critical, and industry policies on generative AI reflect this growing focus.
- Privacy and data integrity: Manuscripts containing unpublished findings, and reviewer identities must be protected. AI systems need to operate within strict data governance frameworks to maintain confidentiality and compliance.
Underlying all of this is trusted content. Systems trained on high-quality, authoritative data, and designed to provide clear attribution are better aligned with the needs of research communities.
In this context, transparency and citation are not optional; they are core requirements. Without trust, even highly effective tools will see limited adoption.
From tools to workflows
Another shift underway is the move from standalone AI tools to integrated, workflow-focused environments. Rather than solving isolated tasks, AI is increasingly being embedded across the entire editorial process.
These environments combine capabilities such as manuscript screening, reviewer recommendations, and workflow management into a single system. The benefit is not just efficiency, but coherence, reducing system switching and improving the overall experience for editors and reviewers.
Integration, however, brings complexity. Systems must be interoperable, adaptable to different journal policies, and able to evolve as standards change. This makes strong governance and transparency embedded in the design even more important.
The path to progress
To deliver real impact, AI in publishing needs to be assessed based on its application against a few clear outcomes:
- Its tangible ability to reduce administrative workload for editors and reviewers.
- The ability to improve consistency in processes, such as manuscript triage and reviewer selection, to help reduce variability and reduce potential biases.
- Faster publishing times without compromising quality.
- Sustained trust in the publishing process.
Any gains in speed and scale must never come at the expense of transparency, accountability, or editorial independence.
Looking ahead
There’s no doubt that AI will play an evolving role in academic publishing. Reducing administrative burden is one of the clearest opportunities to create value for editors, reviewers, and researchers.
However, the impact on publishing will not be defined by AI usage alone, but by how well systems are designed around user needs and how the technology is applied. Specifically, the most effective uses will be those that focus on supporting, rather than replacing, human expertise.
AI is not a solution in itself; it is a tool that, when applied thoughtfully, can help rebalance the system not only for greater efficiency but to support a stronger and more reliable scholarly record, where more time is spent advancing knowledge, and less on managing the processes around it.
