Speakers will discuss how GenAI is being integrated into peer review processes, examining both the benefits and challenges. They will consider AI's potential to enhance efficiency, improve accuracy, and reduce bias, while also discussing concerns related to over-reliance on technology, ethical considerations, and the need for transparency. Automated submission screening and desk review checks can swiftly filter out manuscripts that do not meet basic criteria; reviewer matching algorithms ensure that manuscripts are assigned to the most suitable reviewers. Workflow optimization tools further streamline the review process by managing deadlines, sending reminders, and tracking progress, however, generative AI (GenAI) tools are not yet widely (if at all?) integrated into publisher peer review workflows, and many publisher policies restrict reviewers from using publicly availably GenAI in the peer review process. These policies may overlook the benefits that a private and secure GenAI tool could bring to review efficiency and insight. To address these concerns, publishers should be considering the implementation of transparent, traceable, and secure GenAI workflows within the peer review system. Ensuring that GenAI’s role within peer review is fully documented and subject to human oversight will help maintain trust and accountability, whilst also bringing resource efficiencies. The session will close with a demo of the Enago Read AI Peer Review Assistant workspace which provides for AI supported contextually appropriate peer review analysis within a structured peer review form, with the benefits of literature summarization, co-pilot paper querying, reference analysis and related papers suggestions.