The scholarly publishing landscape is undergoing rapid transformation, driven by advances in AI, automation, and the growing complexity of manuscript submissions. Surging submission volumes, increasingly sophisticated AI-generated content, and the rise of advanced paper mills are pushing traditional editorial workflows to their limits. For journal editors and publishing professionals, the question is no longer whether to adopt AI tools, but how to implement them strategically, while safeguarding the integrity and trust that form the foundation of scholarly communication.
The Challenge: Scale Meets Sophistication Editorial teams today face a perfect storm of pressures. Manuscript submissions are growing at an unprecedented pace, stretching editorial capacity to its limits. At the same time, the sophistication of potentially problematic submissions has increased dramatically. From AI-generated manuscripts that convincingly mimic academic writing to complex data manipulations and image integrity issues, the challenges now go well beyond simple plagiarism detection.
To maintain their critical gatekeeping role, editors need more than traditional tools—they need intelligent systems that can keep pace with both volume and complexity.
The solution is not to replace human judgment with artificial intelligence, but to develop symbiotic workflows that combine human expertise with machine precision—allowing each to do what it does best.
A Strategic AI–Human Editorial Framework
Successfully integrating AI into editorial workflows requires a design mindset where automation augments, not replaces, human decision-making.
The following eight-stage framework outlines how editorial teams can strategically combine the strengths of AI with the critical thinking, ethical oversight, and contextual understanding that only experienced editors can bring. This approach helps scale editorial operations while preserving the credibility and trust at the heart of scholarly publishing.
Stage 1: Intelligent Submission Processing When manuscripts arrive, AI can immediately handle the mechanical aspects of intake—processing metadata, validating file formats, and organizing submissions for efficient review. This automated processing ensures that human attention begins where it matters most: with properly formatted, complete submissions ready for substantive evaluation.
Stage 2: Comprehensive Pre-flight Screening Think of AI pre-flight checks as the editorial equivalent of airport security: fast, consistent, and scalable. These automated systems can rapidly assess submissions across multiple dimensions:
Technical validation ensures manuscripts meet basic formatting requirements and include necessary components.
Language and style analysis flags grammatical issues, clarity problems, and reference formatting errors before they consume editor time.
Ethics and integrity screening represents perhaps the most critical application—detecting plagiarism, identifying manipulated images, and flagging data irregularities that might indicate fabrication.
Scope matching using natural language processing can quickly identify manuscripts that fall outside a journal's focus area, preventing misaligned submissions from entering the full review pipeline.
Stage 3: Strategic Filtering and Prioritization Editorial capacity remains one of the most precious resources in scholarly publishing. AI-powered filtering helps preserve this capacity by identifying submissions that fail to meet baseline criteria—those clearly out of scope, missing essential ethical components, written below acceptable standards, or showing signs of being AI-generated without appropriate disclosure.
This filtering doesn't replace editorial judgment—it focuses human attention where expertise can have the greatest impact.
Stage 4: Enhanced Human Decision-Making When editors conduct their initial review, AI provides valuable signals and data points, but the critical evaluation remains entirely human-driven. Editors assess originality and novelty, evaluate the clarity of research questions, determine field relevance, and ensure alignment with journal standards.
The relationship here mirrors that between a physician and diagnostic tests: AI provides information, but trained professionals interpret that information within broader contexts that machines cannot fully grasp.
Stage 5: Intelligent Reviewer Selection AI can significantly streamline reviewer identification by analyzing expertise areas, publication histories, availability patterns, and diversity metrics. However, human editors make the final selections, ensuring contextual relevance, checking for conflicts of interest, and addressing potential biases that algorithmic matching might perpetuate.
Stage 6: Augmented Peer Review During the review process itself, AI tools can support—but never replace—reviewer judgment. Automated summary generation can help reviewers quickly grasp key points, reference and citation checking can identify potential issues, statistical analysis support can flag methodological concerns, and voice-to-text tools may assist reviewers who prefer dictation, though their use remains niche. Voice notes can be used to capture and organize key ideas or comments, while generative AI can help structure these thoughts into coherent review reports. Once the reports are generated, reviewers can use AI-assisted tools to review and proofread the content before finalizing and submitting it to the editors. All of this can take place within a secure environment provided by the publisher, helping to reduce both cognitive and manual effort. This enables reviewers to maintain better focus, attention, and flow—fostering the creation of high-quality peer reviews.
These tools enhance reviewer efficiency while ensuring that the substantive evaluation remains fully human-led.
Stage 7: Decision Support, Not Decision Making When synthesizing reviewer feedback, editors can benefit from AI-generated summaries and highlighted discrepancies between reviewer reports. However, the final editorial decision must remain grounded in human expertise, considering factors that extend far beyond what any algorithm can assess.
Stage 8: Quality Assurance and Production Support Even after acceptance, AI can continue to add value through reference normalization, disclosure validation, and identification of production inconsistencies. Human editors maintain oversight to ensure that quality, tone, and contextual nuance are preserved throughout the publication process.
Stakeholder Impact: Beyond Editorial Teams While AI integration primarily transforms editorial workflows, its ripple effects extend across the broader publishing ecosystem. Authors benefit from faster, more transparent submission checks and clearer, earlier feedback—helping them navigate the publication process with greater confidence. Reviewers gain access to supportive tools—such as AI-generated summaries, voice-note capture, and reference validation—that reduce manual effort and allow them to focus on scientific and ethical evaluation.
Publishers and institutions stand to benefit from increased workflow scalability, improved compliance oversight, and greater reputational assurance—driven by more consistent, transparent, and auditable editorial processes. When delivered within secure, publisher-managed environments, AI-enabled tools not only streamline operations but also reinforce trust among all stakeholders.
By thoughtfully considering the needs and experiences of each participant in the scholarly publishing value chain, AI-powered workflows can enhance both efficiency and integrity—ultimately strengthening the credibility and impact of scholarly communication.
Guiding Principles for Successful Implementation The most effective AI implementations in editorial workflows follow a set of key principles:
AI should save time, not replace discernment. The goal is to eliminate mechanical tasks that drain editorial energy—freeing professionals to focus on areas where human judgment is essential and irreplaceable.
Boost throughput while maintaining oversight. While AI can dramatically accelerate certain processes, strong editorial oversight ensures that efficiency gains do not come at the expense of quality, ethics, or integrity.
Keep human judgment at the core of editorial decision-making. Perhaps most importantly, editorial teams must resist the temptation to outsource critical decisions to machines. The credibility of scholarly publishing depends on human experts making informed, context-sensitive evaluations of what merits publication.
Ensure a secure, publisher-managed environment for AI-supported tasks. All AI-assisted editorial activities—whether manuscript screening, voice-note capture, structured report generation, or language refinement—should take place within secure environments provided and monitored by the publisher. This safeguards data privacy, protects intellectual property, and builds trust among reviewers, editors, and authors.
Aligning with Evolving Industry Standards As editorial teams integrate AI into their workflows, it's essential to ensure alignment with evolving industry guidelines and institutional policies. Organizations such as the Committee on Publication Ethics (COPE) and initiatives like the STM Integrity Hub are actively shaping frameworks for responsible AI use, research integrity, and misconduct detection. AI tools used in manuscript screening or peer review support must be configured to respect these standards, ensuring transparency, fairness, and accountability in decision-making. By incorporating these guidelines into their AI strategies, publishers can reinforce trust, maintain compliance, and stay ahead of regulatory and reputational risks.
Crucially, selecting reputable AI vendors and solutions that prioritize transparency, fairness, and ethical AI development is paramount. Publishers must perform due diligence to ensure that the tools they adopt align with industry best practices and uphold the fundamental principles of scholarly integrity.
Conclusion: Building the Future of Editorial Integrity
If human judgment is removed from the gatekeeping function, we risk eroding the very foundation of scholarly trust. The challenge today is not whether to adopt AI, but how to do so in a way that strengthens—rather than compromises—the editorial values that underpin scholarly publishing.
To future-proof editorial operations, successful organizations are reinforcing the role of human editors while using AI to eliminate mechanical burdens. They're investing in what matters most: discernment, ethics, and sustained attention—uniquely human capabilities that become even more valuable in an AI-enabled environment.
At the same time, enabling tools such as voice note capture, AI-assisted review drafting, and automated proofreading—delivered through secure, publisher-managed platforms—can enhance reviewer experience and efficiency. These technologies reduce friction, not intellectual engagement, and allow reviewers to focus their energy where it matters most: thoughtful, high-quality peer evaluation.
The integration of AI into editorial workflows is both a responsibility and an opportunity. When implemented with care, AI can help manage increasing submission volumes, support consistency and integrity, and reduce cognitive load across the editorial and peer review process. But this must always happen within frameworks that protect privacy, ensure transparency, and uphold trust.
The future of scholarly publishing doesn't lie in choosing between artificial intelligence and human expertise—but in thoughtfully combining them to create workflows rooted in quality, insight, and unwavering commitment to integrity. Editorial teams that embrace this balance will be better prepared to navigate complexity, detect manipulation, and elevate the standards of scholarly communication for the long term.
Key Takeaways:
The rise in submission volumes and sophisticated AI-generated content is challenging traditional editorial workflows.
AI should not replace human judgment but serve as a strategic assistant to enhance editorial efficiency and integrity.
A balanced editorial–AI workflow includes eight stages: from intelligent submission processing to production support.
AI excels at repetitive, mechanical tasks—allowing editors to focus on decision-making, scope assessment, and quality evaluation.
Editorial decisions must remain human-led to preserve trust and credibility in scholarly publishing.
Successful AI implementation should align with evolving standards such as COPE and STM Integrity Hub guidelines.
A secure, publisher-provided environment for AI-assisted tools supports reviewer efficiency while protecting data integrity and trust.
The future lies in thoughtfully combining human expertise and AI to uphold quality, integrity, and impact.
Keywords
AI in scholarly publishing
editorial workflows
peer review automation
responsible AI
research integrity
manuscript screening
human-AI collaboration
STM Integrity Hub
COPE standards
editorial decision-making
publishing technology
reviewer selection tools
Ashutosh Ghildiyal
Ashutosh Ghildiyal is a strategic leader in scholarly publishing with over 18 years of experience driving sustainable growth and global market expansion. His diverse career spans customer service, business development, and strategy, where he has collaborated closely with authors, institutions, and publishers worldwide. Having successfully established and scaled operations across various international markets, including India and China, Ashutosh currently serves as Vice President of Growth and Strategy at Integra. In this role, he partners with scholarly publishers and societies to optimize both upstream and downstream publishing workflows. Ashutosh actively contributes to industry advancement through thought leadership and collaborative initiatives. He serves on multiple influential bodies, including the Board of Directors of ISMTE, the ISMTE Asia-Pacific Advisory Council, the Steering Committee for International Peer Review Week, and the Advisory Cabinet of the Asian Council of Science Editors (ACSE).
A very thoughtful and well-structured perspective, Ashutosh. I appreciate how you’ve emphasized the importance of keeping human judgment at the core while using AI to ease the growing editorial workload. It’s an innovative, balanced approach that respects both innovation and integrity.
Kaiser Jamil
07 July, 2025
Ashutosh has cautioned the users of AI- ChatGPT regarding the importance of a balanced approach to its use, and I fully agree with the narrative. It’s a well-written perspective, and most users are now aware of its importance in scholarly writings and also its use in reducing the editorial workload. I am learning more and more each day through different sources and there is more to see and learn than what we can imagine. I am happy to read that Ashutosh has listed out guiding principles of the use of AI. Congratulations . Thank you for sharing your thoughts.
Muhammad Sarwar
07 July, 2025
Thanks for sharing your thoughts.
Dr Bello RS
07 July, 2025
Dr, thanks for the well researched work and especially your thoughts on human interface in editorial reviews. AI can only enhance journal quality, but human editorial judgements are required to make decisions beyond ai reasoning. So human editorial will always remain relevant in turning out qualitative journals.
Kudos to the writer.
Maryam Sayab
07 July, 2025
I really appreciated how you framed AI not as a threat, but as a practical support system to ease the growing pressures on editors. Your emphasis on augmenting, rather than replacing, human judgment felt especially relevant and reassuring. The structured stages you outlined make the conversation around AI much more actionable. Truly worth the read!
Clara Slone
07 July, 2025
This piece really hits home. The pressures on editorial teams are real, and I think your call for a balanced, human-centered use of AI is exactly what the industry needs right now. I especially liked how you acknowledged the reviewer experience, often overlooked in these discussions. Thoughtful, clear, and forward-looking, thank you for writing this!
Leave a Comment
Your email address will not be published. Required fields are marked *
Muhammad Sarwar
07 July, 2025A very thoughtful and well-structured perspective, Ashutosh. I appreciate how you’ve emphasized the importance of keeping human judgment at the core while using AI to ease the growing editorial workload. It’s an innovative, balanced approach that respects both innovation and integrity.