The integration of artificial intelligence (AI) into point-of-care ultrasound (POCUS) signifies a crucial advancement in medical technology, reshaping bedside imaging and healthcare delivery. AI has the potential to bridge the gap between advanced technological capabilities and the varying levels of ultrasound expertise among healthcare providers. For POCUS program directors, integrating AI into their operational frameworks is becoming imperative. AI in POCUS can address core challenges in program implementation and management, offering solutions to enhance diagnostic accuracy, training efficiency, and operational workflow. However, integrating AI into POCUS presents challenges, including technical hurdles, inherent biases in AI algorithms, ethical considerations, and a gap between AI development and clinical applicability. The limited availability of diverse and accessible datasets, along with variability in ultrasound applications and equipment, further complicates the creation of universally applicable AI solutions. To navigate these complexities, a hands-on approach from program directors is essential, ensuring that AI tools are not only practical but also clinically relevant. Moreover, balancing AI capabilities with clinical oversight is crucial to prevent overreliance on automated tools and potential medical errors. Program directors must proactively navigate the evolving landscape of AI in POCUS, which involves continuous learning and preparation for technological integration while staying informed about regulatory changes and updates to ensure compliance with healthcare standards and laws. This chapter discusses some of the basic terminology and concepts of AI in POCUS and provides an overview of the different tools available on ultrasound machines and considerations when choosing AI tools.