Adoption of Artificial Intelligence in Drug Development Within Pharmaceutical Industry - Belsare, Mugdha (Digital Repository of Theses, 2025)
The pharmaceutical industry now experiences major changes in research and innovation because Artificial Intelligence (AI) was introduced into drug development processes. The main objective of this research is to present in-depth information about how artificial intelligence (AI) modifies drug development procedures. This research investigates how independent variables affect the relationship between variables leading to the adoption. The study examines independent external variables’ substantial influence on AI's adoption and integration in drug development. Regulatory frameworks, navigating the balance between innovation and compliance, play a pivotal role. An understanding emerges of regulatory authorities recalibrating guidelines to accommodate AI-driven methodologies. Market dynamics and competitive pressures amplify AI's importance, prompting organizations to embrace AI for sustainable growth and competitive edge. Patient-centric imperatives underscore AI's potential to drive personalized medicine, optimize clinical trials, and enhance patient outcomes. Internally, organizational culture shapes AI assimilation, influencing the readiness to adopt innovations. Technical infrastructure and financial resource allocation determine AI integration feasibility and scope. The review highlights AI's potential to foster novel collaboration models and cross-functional synergies within organizations. The literature review led to the development of an appropriate conceptual framework for this research. The research adopted a quantitative approach for its design. For this research the chosen tool was a questionnaire. The questionnaire contained 24 non-demographic questions rated on a five-point Likert scale and was distributed to the relevant participants. The survey obtained 306 responses from the 1500 participants achieving a final response rate of 21%. This participant group contained professionals from both the pharmaceutical industry and AI technology who represented international expertise. Software package ADANCO 2.4 enabled analysis of variance-based structural equation modelling to develop model hypotheses. The framework achieved complete success in reliability and validity assessment measures. The analysis revealed significance in nine hypotheses created to explain direct relationships and indirect relationships between variables. Surveys with demographic data were distributed to pharmaceutical organizations throughout the world. The research findings indicate that artificial intelligence markedly improves the efficiency and effectiveness of drug development processes, especially within data-driven contexts. However, effective deployment involves a high level of organizational agility and culture, and market landscape and dynamics. Ethical considerations and regulatory frameworks serve as challenges to implementation, underscoring the necessity for a strong technological infrastructure and systemic restructuring to utilise big data efficiently. Organisational agility, influenced by data standards and a culture focused on innovation, is a critical element in the adoption of AI solutions. Market dynamics significantly influence AI readiness, highlighting digital adoption as an essential competency for modern pharmaceutical organisations. The findings highlight the potential of AI to transform drug discovery, depending upon companies effectively addressing critical challenges associated with data privacy, feature selection, and regulatory compliance. This research provides practical insights for aligning organisational strategy with technological innovation to achieve successful adoption of AI. The study revealed essential understanding regarding AI adoption within pharmaceutical drug development operations. Research findings function as an initial reference point to connect research areas while recognizing essential factors within Pharma industry operations in AI-driven drug development. This research established an explanatory model which pharmaceutical industry users can utilize to resolve their issues or foresee potential barriers in implementing AI effectively.