The practice of radiation oncology is changing at a rapid pace. As models of care and department reimbursement change, departments are evaluating an increasing number of patients with complex medical backgrounds which must be acknowledged as increasing difficult treatment plans are developed. With altered fractionation strategies, compressed fractionation treatment delivery, and highly sophisticated radiation therapy treatment technology, radiation therapy has evolved into a highly rigorous discipline applying multiple overlapping image datasets to define targets of interest with sophisticated image guidance tools to ensure accuracy of therapy. Integrated with biomarkers associated with treatment response and therapeutic resistance, radiation therapy can be delivered to tumor with altered doses to image-guided subsets of disease. The datasets now required to generate radiation oncology treatment plans are becoming increasingly large and complex. Ultimately, artificial intelligence models will mature to ensure accuracy and consistency to department workflows and therapeutic decisions including contouring and treatment planning. Artificial intelligence can and will be applied to all elements of daily patient care as well as clinical translational research. In this paper, we explore how multiple components of artificial intelligence will support the radiation oncology work force and therapy-guided clinical trials moving forward.