This presentation showcases how Knowledge Graphs, Artificial Intelligence (AI), and Graph RAG can significantly improve operational efficiency across the clinical trial lifecycle. By semantically structuring and linking biomedical concepts, protocol elements, site capabilities, patient characteristics, operational and logistical inputs, these technologies enable more informed decision-making and intelligent automation (document writing/checking and structured data transformation). Practical use cases span site feasibility assessments, staff readiness, and procedural fit; modeling sample handling and clinical supply forecasting; and data-enabled decentralized trial strategies to boost patient recruitment and retention. Protocol authoring and study design benefit from intelligent tools that leverage historical endpoint approvals and optimize data collection frameworks. Budget management is streamlined through automated RFP generation and dynamic tracking aligned with trial assumptions and performance metrics. Lastly, sustainability is addressed by incorporating environmental impact analysis into trial method selection, in line with cross-industry standards. Together, these capabilities accelerate trial execution while improving accuracy, consistency, and strategic foresight.