AI-Augmented Document Review for Regulatory Affairs Efficiency

Abstract

The escalating complexity and volume of regulatory submissions in the life sciences and pharmaceutical industries have created significant burdens for regulatory affairs (RA) teams tasked with ensuring dossier accuracy, completeness, and timeliness. Traditional manual review workflows—characterized by repetitive checks for section compliance, crossreference validation, and terminology consistency—are increasingly insufficient in coping with the growing demands imposed by global harmonization initiatives such as ICH, evolving regional regulations, and the sheer scale of data generated in modern drug development. This study investigates the integration of an AI-augmented document review platform designed to automate routine validation tasks, surface potential compliance risks, and provide contextual decision support to RA professionals. Over a six-month pilot involving three mid-to large-sized pharmaceutical companies, the system was deployed alongside existing document management systems, with reviewers trained to leverage AI-generated annotations. Quantitative analysis revealed an average 45% reduction in end-to-end review cycle time—dropping from 120 to 66 hours— and a 60% decrease in minor compliance errors per 100 pages, significantly improving dossier quality. Qualitative feedback indicated high user satisfaction: 85% of reviewers found the platform intuitive, and 78% trusted AI-flagged issues. Survey respondents reported that automation of mundane checks freed their time for strategic activities such as regulatory strategy development and proactive risk management. The findings demonstrate that AI-augmented review can streamline workflows, reduce human error, and enhance the capacity of RA teams to manage complex, multiregional submissions, thereby supporting faster patient access to critical therapies. Future research should assess long‐term cost impacts, explore AI’s role in strategic intelligence gathering, and evaluate regulatory authorities’ acceptance of AI-generated review outputs.