Generative artificial intelligence (AI) is emerging as a transformative technology in pharmaceutical research and development, with the potential to address longstanding challenges in trial design, execution, and documentation. As clinical development becomes increasingly complex and costly, the strategic integration of AI technologies will be crucial accelerating the delivery of new treatments to patients and maintaining a competitive advantage in the industry. We present a brief overview of current generative AI capabilities, including foundation models and emerging techniques such as retrieval-augmented generation and AI agents, and their potential applications in clinical research. The chapter examines how large language models (LLMs) can enhance various aspects of clinical trials, from patient recruitment to data analysis and reporting. Through detailed case studies, we demonstrate how LLMs can improve trial efficiency through improved site selection, enhanced patient recruitment, and accelerated documentation generation, including clinical study reports (CSRs) and patient-friendly version of investigator brochures (IBs). While precise quantification of efficiency gains and return on investment remains challenging due to the nascent state of AI adoption in clinical trials, early experiences suggest the potential for significant operational improvements. Our analysis shows that AI-driven approaches can significantly reduce manual effort while maintaining quality standards, leading to substantial time savings in clinical trial operations. We also address critical considerations for implementing AI in clinical development, including regulatory compliance, data privacy, quality assurance, and the need for human oversight. The chapter concludes by examining future trends in generative AI applications for clinical trials, the importance of balancing automation with human expertise to enhance, rather than replace, clinical judgment in medical research.