Integrating Artificial Intelligence into Root Cause Analysis for CAPA systems: A Survey Based Study on Perception, Readiness and Implementation Challenges in the Indian Pharmaceutical Industry

Abstract

This study investigated the perception, organizational readiness and implementation challenges of integrating Artificial Intelligence (AI) in to Root Cause Analysis (RCA) within Corrective and Preventive Action (CAPA) systems in the Indian pharmaceutical industry. A quantitative survey was conducted among 101 professionals working in quality assurance, quality control, manufacturing and regulatory roles. Collected Data were analyzed using descriptive statistics, frequency distributions and measures of central tendency to identify existing attitudes, readiness levels and perceived barriers. The results showed that the majority of respondents recognized AI’s potential to enhance the RCA accuracy, reduce human errors and improve compliance with Good Manufacturing Practices (GMP) standards, with two-third of total sample size agreeing on its benefits for efficiency and decision making. However, readiness levels were not found to be same, with approximately 60% reported having some relevant technical infrastructure and other 40% believed their organizations had both the technology and skilled personnel required for effective implementation. Key barriers included high implementation costs, limited AI expertise, resistance to change and uncertainty about regulatory acceptance. Participants have identified a strong need for clearer regulatory guidelines, targeted training programs, pilot implementation projects and improved data quality and security to support AI adoption. The findings indicate that although optimistic belief towards AI adoption in CAPA systems is strong, there are some practical limitations which constrain the current progress. This study concludes that successful integration will require coordinated efforts between industry stakeholders and regulators, strategic investments in infrastructure and training, and structured change management initiatives. This research contributes real world data on an underexplored geographical context, giving a combined view of perceptions, readiness and challenges to help shaping the policies and organizational strategies which promote AI adoption. Recommendations includes initiatives for small scale pilot programmes, promoting industry-regulator collaborations and developing more regulatory guidelines for AI- validation. In future, more investigations has to be done in relation to changes in AI-adoption over time, assessment of cost-effectiveness and comparisons with other regulated industries.