The global scholarly publishing system now produces more than 5 million research articles annually. But published articles are just the tip of the iceberg. Behind every paper that reaches publication are several manuscripts that undergo editorial checks and the peer review process. As the submission volume keeps expanding, journals are struggling to manage the workload. One of the biggest challenges editors face is recruiting reviewers quickly and making the turnaround time for decisions shorter. Unsurprisingly, the conversation around peer review typically revolves around the capacity problem. While the problem is genuine, solely focusing on the capacity issue can obscure a deeper concern.  

Peer review is buckling under pressure because it was never meant to operate at today’s scale. Reviewers are expected to take on more workload than before and complete reviews quickly. They are also expected to sniff out manipulation and fraud, which is becoming increasingly sophisticated and organized. The solutions to solve this problem include more automation and smarter reviewer matching. However, the question is no longer “Can peer review be scaled?” The real question is whether trust can scale too.  

Peer review is typically assessed through metrics such as reviewer turnaround times and time to first decision. Such measures help journals identify bottlenecks and improve author experience. However, they tell us very little about trust.  

As a publisher, how many of these questions would you answer in the affirmative?  

• Are we able to identify potential integrity concerns identified before publication? 

• As editors, do we feel confident of the publication decisions we are making? 

• Do peer reviewers have sufficient time and information to evaluate a manuscript thoroughly? 

• Do post-publication corrections reveal weaknesses in earlier review stages? 

• Are integrity concerns increasing as submission volumes grow? 

The behaviors that strengthen trust are time consuming as they require deep scrutiny. However, the ecosystem tilts in favor of productivity and largely assumes that trust will emerge as a byproduct. The fact is that, compared to efficiency, trust is inherently hard to define and measure. However, the ultimate outcome of peer review is trust. So, shouldn’t the lack of ways of measuring trust be as concerning as peer review’s capacity issue?  

Trust is often treated as an abstract concept. But could publishers define it more concretely? Trust is the ability to justify and defend the decisions that shape the scholarly record. As AI makes its presence felt across editorial workflows, defining trust is extremely important. Authors, institutions, readers, and regulators may begin asking questions such as: Why was this reviewer selected? What role did AI play in this decision? What was the role of human experts? Can the decision be explained and defended? 

If editors struggle to answer these, they may find it difficult to retain trust even if they are able to make quick decisions. Seen through this lens, trust is not simply about preventing fraud or detecting misconduct. It is about bringing transparency in the underlying processes that decide what enters the scholarly record. 

The current narrative suggests that AI can reduce friction in peer review and editorial workload. Editors can screen manuscripts and identify anomalies using AI tools. They can also find and match reviewers smartly. AI can also assist reviewers in using their time and expertise efficiently. However, the same technologies are also being used to undermine research integrity. Creating fraudulent records is quick and easy but detecting them can be slow and challenging. AI-assisted peer review should, therefore, be viewed as a way to support editorial judgment and improve confidence in publication decisions. 

If trust is becoming the defining challenge for peer review, then the solution should involve ways to measure and reward the activities that build trust. Publishers could track and report integrity-related indicators meticulously. These should be given the same level of importance as turnaround times and publication speed. Moreover, they should consider creating a standardized framework for measuring trust-related outcomes. These can include integrity investigations, reviewer engagement, and transparency mechanisms. Editors should be able to communicate their integrity concerns with the parties involved easily. Importantly, efforts could be made to recognize reviewers who highlight integrity issues. These rewards should help reviewers in career progression and gain recognition.  

The publishing industry has entered an era where submission volumes are only likely to increase, and more ways of gaming the system are likely to emerge. Publishers who can demonstrate trust along with efficiency will have a competitive edge. So, the next challenge for scholarly publishing is not merely scaling peer review. It is defining, measuring, and demonstrating trust in an increasingly AI-assisted publishing ecosystem. Because trust is no longer a byproduct of good process; it is the product itself.