This chapter discusses the initial enterprise use cases of AI and GenAI in production. It also delves into the causes of why more than 80% of AI initiatives fail, mentioning poorly defined problems, insufficient data infrastructure, and unmet expectations as significant culprits. This chapter focuses on the importance of establishing comprehensive enterprise AI governance, which includes strategic alignment, risk management, policy advocacy, stakeholder collaboration, and holistic oversight spanning the entire lifecycle. It provides a framework for an intake process to determine AI projects’ economic feasibility and value creation while considering the expense of training and deploying models. Proactive strategies to test and secure AI systems against ethical, technical, and societal risks are covered, like AI red teaming, which stems from military simulation games. Utilizing global benchmarks and legal structures such as the EU AI Act and NIST’s AI Risk Management Framework, this chapter highlights enterprise gap analyses with interlocking ethical AI frameworks as foundational enabling components. Together, these ensure the responsible and strategically valuable execution of AI initiatives.