As publishers integrate artificial intelligence, a central question emerges: How much additional scholarly output can journals absorb? The answer hinges on what happens after AI completes its task. Faster preliminary assessment may empower an editor to act with confidence; conversely, it might surface complex issues requiring hours of specialist intervention. While both outcomes elevate publishing standards, they carry starkly different implications for capacity.
This distinction becomes critical as incremental gains accumulate across the editorial workflow. Automated screening and peer-reviewer selection accelerate manuscript movement into external review. However, if reviewer bandwidth remains unchanged, the journal merely expedites paper arrival without expanding structural capacity to evaluate them. Effective growth planning must trace manuscripts through revision and decision, where the downstream effects of early gains truly manifest.
Consequently, significant weight rests on the precision of editorial assignments. A reviewer invited for expertise in a specific methodology must understand precisely what requires evaluation and which aspects are addressed elsewhere. For example, an AI flag indicating potential image manipulation requires a reviewer equipped to inspect technical evidence. Forwarding flags without clarifying responsibility introduces ambiguity. AI’s true value lies in helping editorial offices identify concerns early, route them appropriately, and define remaining review scope.
This efficiency need not compromise rigor. Significant inconsistencies warrant thorough investigation, even if timelines extend. Equally, a reviewer’s time should not be consumed reexamining settled questions or addressing issues tangential to publication criteria. AI’s contribution should be evaluated at this granular level: the specific tasks it completes, the evidence human experts verify, and the subsequent workload generated by their decisions.
Tracking review effort per decision offers publishers a metric to quantify these dynamics. By analyzing comparable externally reviewed manuscripts, this metric follows cumulative editorial and reviewer input through final decision. To remain robust, it must differentiate initial evaluations from revisions and specialized investigations, while keeping ongoing cases visible. Author effort also demands distinct consideration; reducing internal editorial effort by placing greater burden on researchers merely shifts the workload rather than generating genuine efficiency.
Measuring this effort requires combining quantitative workflow data with qualitative sampling. Turnaround times often reflect idle waiting periods, and a submitted review reveals little about the effort required to produce it. Therefore, metrics must be interpreted in light of task complexity and review quality. They should also align with the specific AI tools permitted by each journal, rather than assuming a uniform application of technology across all workflows.
A credible AI strategy must account for whether streamlined workflows generate, preserve, or eliminate review effort. For publishing leaders, empirical evidence will highlight where additional submission volume can be accommodated and where specialist capacity requires reinforcement. This provides a far more reliable foundation for scaling operations than the assumption that faster individual tasks will yield a proportional increase in published decisions. Read more
Knowledgespeak Editorial Team
