Artificial intelligence is now creating significant efficiencies and cost savings in late-stage clinical trials for cancer treatments, expanding beyond its role in early-stage drug development. AI-powered tools are poised to reduce the time-consuming aspects of clinical trials, such as patient recruitment, monitoring, and data interpretation, potentially freeing up resources for more studies and lowering drug failure rates. A Tufts Center for the Study of Drug Development analysis revealed that AI agents can accelerate cancer drug development by approximately 10 weeks and slash direct operating costs by up to $5.6 million in late-stage trials. These efficiencies grow substantially with the number of tumor targets a drug has, demonstrating potential net benefits of hundreds of millions of dollars for multi-use treatments.The analysis applied a clinical monitoring agent from Medable to an oncology drug program, resulting in efficiencies like fewer on-site visits, faster trial enrollment, and quicker data lock-in. This marks the first time predictive modeling based on actual use and benchmark data has quantified the financial impact of an agentic AI solution in drug development. Experts anticipate AI agents becoming standard in clinical trials within three to five years, handling essential record-keeping like self-driving cars. Beyond efficiency, AI can track trial population diversity and enable earlier understanding of drug safety and effectiveness, which is crucial for complex cancer drug trials with large monitoring budgets. However, AI does not guarantee trial success, as challenges like patient consent and drug distribution remain. Additionally, human verification of AI's work could partially offset time savings.