Each year, more than 5.14 million academic articles are published, and studies show that submissions grew 48% between 2015 and 2024. Yet despite this surge, the publishing pipeline has not kept pace. Bottlenecks occur at nearly every stage - from submission triage (where manuscripts are screened for scope, formatting, ethics, and integrity) to peer review, copyediting, typesetting, and metadata preparation.
To address this, publishers are increasingly turning to AI across the workflow. Studies how that more than two-thirds of STM publishers are piloting AI for tasks such as plagiarism and image integrity screening, reviewer recommendations, language refinement, copyediting, metadata enrichment, and even accessibility.
In this blog, we will explore how AI can complement each stage of the publishing workflow -where it delivers real efficiencies, where a hybrid human + AI approach is best suited, and where human judgment must remain central.
A Closer Look at AI Across Editorial Stages
Manuscript intake and initial check: AI tools now handle the first layer of checks that used to take editors hours. Instead of sifting through raw files, editors receive structured flags - whether a manuscript is out of scope, missing required information, or triggering plagiarism alerts. This means fewer back-and-forth rounds and more time spent on evaluating the substance of the work.
Peer review support: Reviewer selection and evaluation have historically been among the most time-consuming aspects of editorial workflows. Today, systems can match submissions with suitable reviewers more effectively, while also providing early indicators of competing interests or prior associations. For publishers, this shift means reviews are initiated faster and with greater confidence in the fairness of the process, improving both timelines and trust in peer evaluation.
Copyediting and proofing: Instead of line-by-line checks for grammar and style, AI pre-flags sections that need attention, from syntax issues to citation mismatches. Automated proofing tools also highlight formatting inconsistencies and citation errors, giving editors a clearer starting point. The result is not a replacement of the editorial eye, but rather a cleaner version of the manuscript that minimizes time spent on mechanical corrections and maximizes attention to contextual accuracy.
Metadata enrichment: Metadata plays a critical role in discoverability and long-term value of scholarly work. Automated enrichment processes now ensure that abstracts, keywords, and author affiliations are standardized, while citations are validated against reliable sources. For publishers, this means faster indexing, fewer missed connections, and an overall improvement in how research is surfaced to global audiences.
Production and layout: The production stage, once characterized by labor-intensive formatting and back-and-forth checks, is increasingly supported by AI that scans for layout inconsistencies, broken references, and even cover image suitability. These tools give production teams structured insights with actionable corrections, reducing delays and ensuring design standards are upheld without repeated interventions.
Accessibility: Accessibility is no longer a post-production add-on but an integral part of ensuring that published work reaches the widest possible audience. Automated checks now evaluate whether content meets accessibility guidelines, from alternative text for images to navigable layouts for screen readers. For publishers, this integration ensures compliance with global standards while also reinforcing their commitment to inclusivity in scholarly communication.
Mapping where time is spent highlights not just inefficiencies, but also the pressure points that shape editorial judgment. With that context, the broader implications of the workflow become clearer.
Striking the Right Balance
While AI introduces efficiency at multiple points in the editorial workflow, each stage also carries risks if not carefully managed. This makes it essential to keep human oversight embedded within the process. The following diagram provides a clear overview of how AI can improve each stage of the publishing pipeline while also highlighting the corresponding risks that arise without careful human oversight.
Submission Intake: AI can assist in the initial screening of manuscripts by flagging submissions that are out of scope, have formatting issues, or raise potential ethical or integrity concerns. This reduces the time editors spend on routine checks and allows them to focus on evaluating the substance and relevance of the work. However, human judgment remains essential to interpret complex cases, such as unconventional research formats or borderline ethical issues, ensuring that valid submissions are not rejected prematurely.
Peer Review: Algorithms can help match manuscripts to potential reviewers by analyzing subject matter, keywords, and past publications. This speeds up what is often a lengthy stage. Still, reviewer selection cannot rest solely on data points. Expertise, conflicts of interest, and professional nuances are best evaluated by human judgment, ensuring the review process maintains its integrity.
Copyediting: AI tools can flag grammar, style, and citation inconsistencies, producing cleaner drafts for editors to review. This reduces time spent on mechanical corrections. However, editorial oversight is crucial to preserve tone, disciplinary conventions, and nuanced meaning, which automated systems may distort through over-correction.
Metadata: Metadata automation ensures keywords, author affiliations, and citations are standardized and validated, improving discoverability and indexing accuracy. Yet, editors must review these outputs carefully, since AI-generated metadata can sometimes be incomplete, misleading, or “hallucinated,” which risks undermining long-term research visibility.
Production: Automation can accelerate copyediting, reference checks, and typesetting. Yet, research articles are not only about presenting information; they must also uphold disciplinary conventions and readability standards. Editorial teams must review AI-processed drafts carefully to confirm that clarity, tone, and disciplinary expectations are preserved.
Accessibility: AI tools can generate plain-language summaries, alternative text for images, and even audio versions of content, making research more inclusive. Still, accessibility is not just about compliance but about understanding how different audiences engage with scholarly work. Editors need to guide and refine these outputs so they truly serve readers rather than being treated as automated add-ons.
A well-balanced workflow ensures that while AI accelerates routine processes, human oversight safeguards the integrity, fairness, and trustworthiness of academic publishing.
Hybrid Approach – AI + Human Intelligence
The introduction of hybrid workflows signals more than just operational efficiency. It changes how editorial teams think about their roles, moving from line-by-line gatekeeping toward shaping research visibility, guiding ethical standards, and ensuring that the right expertise weighs in where it matters most. This redistribution of focus is what allows journals to remain credible and resilient, even as the publishing volume accelerates.
Looking forward, this kind of evolving workflow is what will make it possible to manage the rising wave of global submissions. It ensures that quality and timeliness move together, rather than one coming at the cost of the other. In that sense, the adoption of hybrid workflows isn’t just about speed, it’s about reshaping how editorial work is defined, and where human judgment adds the most value.
Conclusion
Hybrid workflows are no longer experimental fixes but the foundation for a sustainable publishing ecosystem. Also, AI is not meant to replace human oversight but to support it, speeding up routine checks while editors remain responsible for novelty, ethics, and quality.
At Apex Covantage, we are helping publishers adopt this model by integrating AI into structured hybrid workflows that preserve accuracy while keeping pace with growing submissions. Now is the moment to rethink editorial processes for the long term. So, connect with our expert team to explore how hybrid workflows can redefine the way you publish.
