The application of artificial intelligence (AI) in the coaching profession is very new. We are currently applying it in a very rudimentary manner and in just a few of the many possible application areas. Since the concepts in current AI architectures and the methods of using them are so new, it is helpful to provide education to stakeholders within a schema or model that the stakeholders already know. This will facilitate a more rapid understanding of how the technology works and how each stakeholder can apply it. The presence of standards for AI coaching products can provide a useful schema and can help to raise awareness of important elements within the technology, as well as what to look for in a product available for purchase. Since software product developers are usually focused on the technology, they may not have experts in the human element of the product functionality. These experts may be coaching professionals, social scientists, or both. The human experts can put standards to good use by following the framework set out in the standards and develop appropriate test use cases and evaluating live beta tests within that framework. Some of the most important considerations for high-quality AI coaching applications are data privacy, data use beyond the overt application uses, transparency of algorithms, and methods to minimize bias. Coaches, clients, and purchasers of AI technology must educate themselves on standards and how the technology works, explore AI tools and vendors, and ultimately get experience with AI systems.