In many ways, total knee arthroplasty remains an art for which a surgeon is expected to make multiple judgment calls during surgery to address different situations that may arise. Although the use of robotics has allowed for more objective quantica­tion of ligamentous laxity and provided a precise instrument for component place­ment, the way in which each surgeon “solves” different problems that arise is a matter of philosophy and clinical judgment. AI may be part of the next evolution of knee replacement techniques. AI involves the use of computing techniques to learn patterns from a large quantity of data inputs. In the same way that surgeons cur­rently process different features of a patient’s knee during surgery, AI can be used to quickly determine an ideally positioned knee replacement based on a patient’s preoperative and intraoperative characteristics. The development of a decision sup­port tool that automates portions of implant planning may serve to improve repro­ducibility with knee arthroplasty. There are multiple challenges to the development of such a tool. First, data acquisition needs to be streamlined as most platforms are not designed for large-scale computing research in mind. Second, there is a need for better outcome measurements, whether through patient-reported outcomes or with the use of wearable and other biometric technologies. In the future, it may be pos­sible for an AI system to automatically process a patient’s preoperative features, anatomy, deformity, range of motion, ligamentous laxity, bone density, and other parameters to determine an optimal alignment strategy, implant position, and implant type. Robotics lays the groundwork for capturing the data necessary to cre­ate such an instrument and much work is needed to achieve this goal.