The Role of Human+AI Editorial Workflows in Scholarly Publishing

Why editorial teams are rethinking their workflows

Over the past few years, many scholarly journals have seen a sharp rise in submissions, broader interdisciplinary work, and a growing demand for speed, all while maintaining rigorous quality and integrity. For editorial teams, this has meant increased workloads, tighter deadlines, and mounting risk: rushed checks, overwhelmed reviewers, delayed decisions, and the constant fear that something may slip through.

In response, more publishers and journal managers are exploring a hybrid model: combining human judgment with AI-powered assistance. This is not about replacing editors; it is about helping them manage scale, complexity, and quality sustainably.

What hybrid editorial workflow actually means

When we say “human+AI workflow,” we mean a process where artificial intelligence handles well-defined, repetitive or rule-based tasks, while human editors retain responsibility for judgment calls, interpretation, and ethical decisions. In such a system, AI might run checks or flag issues, but humans make the final call. This differs from a fully automated workflow in which AI would attempt to replace human decisions altogether. In scholarly publishing, the hybrid model tends to offer the best of both worlds, efficiency without compromising editorial standards.

Where AI adds the most value in editorial workflows

In practice, there are several kinds of editorial tasks where AI can meaningfully help, without overstepping its limits. For instance:

• During manuscript triage and initial checks, AI can screen submissions for completeness of metadata, check adherence to formatting requirements, and flag missing information.
• For compliance and integrity checks, tools can flag potential plagiarism or issues with references, identify inconsistencies, or highlight missing statements (e.g., conflicts of interest, data availability, ethical approvals).
• For language clarity and basic style checks, especially for submissions from non-native English speakers, AI tools can help identify unclear phrasing or grammatical errors, improving readability before human review.
• For quality control of non-textual content, checking figure captions, table formatting consistency, or image metadata (when allowed), to ensure basic compliance before editorial scrutiny.

Where human expertise remains non-negotiable

Despite what AI can do, there are critical aspects where human editors must stay involved. AI cannot, and should not, replace:

• Judging scientific merit, originality, or ethical nuance in a manuscript’s Methods and conclusions
• Interpreting complex or novel methods and understanding subtle implications of results
• Managing sensitive communication with authors and reviewers, handling disputes, clarifying intent, and guiding revisions
• Ensuring fairness, integrity, and transparency in peer review and editorial decisions
• Dealing with ambiguous or borderline cases flagged by AI, deciding when a flagged issue is a minor formatting glitch vs. a serious problem affecting the integrity of the research

How hybrid workflows improve quality and efficiency

For editorial leaders looking to balance integrity with throughput, hybrid workflows offer real benefits. AI-assisted screening reduces the initial workload and helps maintain consistent standards across submissions. Editors save time on routine checks and can dedicate more hours to reviewing substance, improving quality of decisions.

For journals overwhelmed with volume, this can reduce bottlenecks and shorten turnaround times. According to a recent industry-wide survey, about 60 percent of publishing companies have already integrated some form or the other of AI tools into their workflow (Source: Zipdo). This suggests that hybrid models are not just a theoretical idea, they are becoming mainstream in publishing operations.

Common challenges and how teams navigate them

Adopting a hybrid workflow is not without challenges. One major risk is over-reliance on AI outputs without sufficient human oversight. AI tools may flag too many false positives (overly cautious), or worse, miss subtle issues. Another challenge is integrating AI tools with existing editorial systems and workflows, especially in legacy setups.

Finally, there are ethical and transparency concerns; many journals are still figuring out how to acknowledge AI’s role in manuscript handling. Indeed, a recent analysis found that among the top 100 academic publishers, fewer than 20 percent had clear guidance on AI usage (Source: Arxiv)

When addressing these challenges, successful teams often start small, pilot AI for one or two tasks, document clearly what is automated vs what is human-reviewed and build internal guidelines. That combination of care, clarity, and gradual adoption tends to build trust in the hybrid model.

Practical steps to build a human+AI workflow without disruption

• Start with a narrow, low-risk pilot by selecting a few high-volume and low-complexity tasks such as metadata checks, formatting checks, or reference validation.
• Introduce one AI tool at a time and limit its use to the chosen tasks so teams can observe its impact without overwhelming existing processes.
• Train editors on how to read AI outputs, how to interpret flagged issues, and how to override or question AI suggestions when something does not look right.
• Document clear internal guidelines that explain which tasks AI handles and which decisions must always remain human responsibilities.
• Track operational outcomes from the pilot including false positives, time saved, rework required, and any changes in turnaround times.
• Expand AI use gradually once the team gains confidence, using pilot data to decide which tasks are safe to automate next.
• Keep the goal centered on improving sustainability and accuracy and focus on content quality rather than accelerating throughput alone.

What this means for the future of scholarly publishing

If adopted thoughtfully, hybrid human+AI workflows can help scholarly publishing adapt to increasing scale without sacrificing integrity. As submission volumes grow and demand for rapid publication rises, AI can help relieve editors from repetitive burdens, while human expertise continues to safeguard quality, fairness, and scientific integrity. Over time, we may see broader adoption, more efficient editorial cycles, and potentially better support for emerging or under-resourced journals. The future could belong to editorial teams that blend human insight with machine efficiency.

A balanced approach creates better outcomes

The idea of using AI in editorial workflows may initially feel risky or impersonal; after all, academic publishing depends on human judgment, nuance, and trust. But when viewed as a collaborative aid rather than a replacement, AI has the potential to make editorial processes more robust, scalable, and efficient. For journal managers, editors-in-chief, and publishing operations leaders, exploring hybrid models does not mean compromising standards. Rather, it can mean preserving them while adapting to the demands of modern scholarly publishing. Human judgement, supported by AI where it adds value, could very well be the future many academic publishers need.

If you are looking to strengthen your editorial workflows, our team can help you build a model that blends the best of human expertise with thoughtful AI support. Explore our editorial services to learn more.