The Hidden Cost of Peer Review Delays: How AI Can Help Publishers Save Millions

In a previous blog, we argued that peer review doesn’t just need an upgrade—it needs deeper rethinking. Peer review delays aren't just procedural hiccups–they reflect deeper inefficiencies ingrained in traditional academic publishing workflows.
But the impact of these delays extends far beyond the frustration of waiting authors and overburdened editors. For each week a manuscript sits in review, publishers incur hidden yet escalating financial and reputational costs. These include prolonged publication timelines, increased staff workload, missed opportunities for citation and impact, and a diminished author experience that could drive researchers toward more responsive platforms.

Recent studies have quantified the burden: researchers donate billions of dollars’ worth of time annually to unpaid peer review, while inefficiencies in editorial workflows continue to drain resources. Despite this investment, many journals still struggle with reviewer recruitment and timely decision-making, raising questions about the sustainability of the current model.
This article explores how peer review delays translate into real costs for publishers and what the industry can do to shift from reactive to strategic solutions.

Mapping the True Cost of Peer Review Delays

Peer review delays may appear to be a matter of days or weeks on a publishing calendar—but their cumulative financial and strategic consequences are far more significant.

1. Direct Financial Costs

Each stalled manuscript requires ongoing oversight, repeated follow-ups, and additional rounds of communication. Editors and editorial staff spend valuable time managing delayed reviews, which inflates per-article costs. According to ReviewerCredits, the cumulative value of researcher time spent on peer review globally exceeds $1 billion annually–yet this doesn’t account for the in-house costs incurred during extended review cycles.
Additionally, identifying and inviting new reviewers, sending reminders, and processing late or declined invitations require substantial administrative effort. These logistical demands eat into staff productivity and increase operational expenditures.
Furthermore, when authors withdraw delayed manuscripts and submit elsewhere, the entire editorial workflow—screening, assigning editors, formatting—must be repeated by another journal. This duplication of labor is a loss shared across the ecosystem, with no added value to science communication.

2. Opportunity Costs

Delayed publication timelines don’t just inconvenience authors—they can actively cost publishers competitive advantage. Authors, especially in fast-moving fields, may redirect their submissions to journals with faster turnaround times. This drain on submission volume can reduce a journal’s revenue–especially for journals relying on article processing charges (APC) or hybrid models, affect citation rates, and dilute its academic influence.

3. Hidden and Reputational Costs

Reviewer burnout is a growing concern. Repeated requests, short deadlines, and little recognition have made it harder to engage qualified reviewers. As burnout sets in, reviewer pools shrink, errors increase, and trust in the system erodes.
Author frustration is another silent cost. Authors waiting months for reviews often share their dissatisfaction publicly or choose not to return to the same journal. A few negative experiences can snowball into lasting reputational damage, particularly in competitive fields where journal prestige matters.

Why Traditional Fixes Fall Short?

Publishers have long attempted to curb peer review delays with incremental interventions—automated reminders, reviewer incentives, or editorial nudges. While these efforts may help marginally, they often add administrative burden without addressing systemic inefficiencies.
Even innovative solutions like open peer review or paying reviewers raise new challenges. Open reviews require significant community moderation, increased editorial oversight, and can deter reviewer participation due to transparency-related pressures. Paying reviewers—while seemingly fair—raises cost concerns and creates inequity across journals with different funding capacities.

At its core, the current system is struggling because it was built for a slower, print-dominated world. Digital-era demands—faster publishing cycles, higher submission volumes, global competition—have exposed the system’s foundational flaws. Without a structural redesign, traditional patches will continue to fall short.

AI as a Cost-Saving Solution: Where Automation Makes a Difference?

While traditional interventions struggle to keep pace with rising peer review demands, AI offers a transformative path forward. By automating labor-intensive tasks, enabling data-driven decisions, and minimizing administrative delays, AI technologies can deliver real cost savings while preserving quality and rigor in peer review.

1. Intelligent Reviewer Matching and Workload Optimization

AI-powered algorithms can instantly scan manuscript metadata, author suggestions, and reviewer histories to recommend suitable reviewers. Unlike manual searches, this intelligent matching is not only faster but also better at identifying subject-matter expertise and balancing reviewer loads. By avoiding overburdened individuals and strategically distributing assignments, AI helps reduce burnout and prevents costly reviewer dropouts mid-process—streamlining workflows and minimizing staff intervention.

2. Expanded Automation of Routine and Compliance Checks

AI already plays a role in plagiarism detection and formatting reviews, but its potential goes further. Advanced tools now assist with verifying ethical approvals, conflict of interest statements, data transparency, and methodological soundness. Automating these early compliance checks dramatically reduces editorial screening time, eliminates manual bottlenecks, and ensures that only submission-ready manuscripts proceed to peer review—cutting down both costs and rework.

3. Peer Reviewer Assistance

AI tools now support reviewers by offering pre-analyzed summaries of manuscripts, flagging inconsistencies, and suggesting potential gaps or strengths. Agentic AI systems can provide contextual manuscript evaluations by following a structured, domain-aware approach, easing the cognitive load on reviewers. This assistance not only speeds up the review process but also enhances its thoroughness—allowing publishers to maintain high quality without incurring additional human resource costs.

4. Peer Review Analytics

AI can mine historical submission and review data to uncover delay patterns and predict future bottlenecks. Predictive models flag at-risk manuscripts early, allowing editors to intervene before deadlines are missed. These systems can also prioritize manuscripts based on urgency, novelty, or funding deadlines, ensuring time-sensitive research isn’t held back by procedural delays.
Moreover, AI can assess reviewer performance by tracking metrics such as timeliness and review quality. With this data, editors can select reviewers with strong track records, minimizing the likelihood of re-reviews and further delays—delivering tangible efficiency and cost benefits.

5. Automated Communication Management

AI-enabled communication tools take over the repetitive, time-consuming task of sending reviewer invitations, reminders, and follow-ups. These personalized, sequenced messages improve reviewer response rates while freeing editorial staff for more strategic tasks. Additionally, reduced email overhead and quicker turnarounds directly lower the costs of managing peer review cycles.

Turning Cost Centers into Value Drivers

Peer review delays are more than just operational hiccups—they represent a persistent, under-acknowledged drain on publisher finances and reputational capital. Each missed deadline, delayed response, or lost submission translates into measurable editorial costs, opportunity losses, and diminished author trust.

By strategically implementing AI across the peer review pipeline, publishers can turn this cost center into a value driver. Automation doesn’t just reduce overhead—it enhances reviewer engagement, accelerates turnaround times, and improves decision-making through data insights. As submission volumes continue to grow and expectations for speed and transparency rise, AI offers not just a fix but a blueprint for sustainable, data-driven, and researcher-friendly peer review.

The next phase of innovation isn’t about patching a broken system—it’s about reimagining peer review as a smarter, faster, and more cost-efficient process.