Pharmacovigilance (PV) is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. It plays a critical role in ensuring drug safety and efficacy, particularly within the pharmaceutical industry. This study investigates the impact of IBM Watson on pharmacovigilance practices in the UK pharmaceutical sector, with a focus on quantifying reductions in manual workloads, improvements in data processing speeds, and enhancements in the accuracy of detecting adverse drug reactions (ADRs). The justification for this research lies in the increasing reliance on artificial intelligence (AI) technologies like IBM Watson to optimize PV processes. As the pharmaceutical industry faces growing demands for more efficient and accurate drug safety monitoring, there is a pressing need to evaluate the effectiveness of these AI tools. By assessing both the benefits and challenges associated with IBM Watson's implementation, this study provides a comprehensive understanding of its role in advancing PV practices. A thorough literature review is conducted to explore existing research on AI in pharmacovigilance, highlighting current trends, technological advancements, and the potential of AI to transform drug safety monitoring. This review also identifies gaps in the literature, particularly regarding the real-world application of IBM Watson in the UK pharmaceutical industry, thereby establishing the necessity of this study. The research employs a quantitative methodology, utilizing surveys distributed to professionals within the UK pharmaceutical sector to gather relevant data. Statistical analyses are performed to evaluate the impact of IBM Watson on operational efficiency and ADR detection accuracy. The findings reveal that a significant majority of respondents reported improvements in literature monitoring efficiency, reductions in manual workloads, and faster data processing with IBM Watson, confirming the hypotheses related to these outcomes. Additionally, the study addresses challenges such as data integration issues, the need for user training, and regulatory compliance, all of which are critical factors for the successful implementation of AI in PV. Upon analyzing the findings, the study will provide practical recommendations and identify critical success factors for optimizing pharmacovigilance practices through AI integration. These recommendations will be aimed at enhancing data integration, improving user training programs, and ensuring compliance with regulatory standards. Ultimately, this research contributes valuable insights into the role of AI in healthcare, offering guidance for future implementations of AI technologies in pharmacovigilance and potentially influencing industry-wide best practices.