AI is a broad term encompassing technologies that enable machines to mimic human intelligence. CTOs should invest in AI if their business handles significant unstructured data, needs to automate complex cognitive tasks, or has competitive landscape challenges with AI-enabled challengers. Machine learning (ML) is a subfield of AI that involves systems learning patterns from data and can power applications like recommendation engines and predictive maintenance. Deep learning (DL) uses multi-layered neural networks to automatically learn increasingly abstract representations of data and yields state-of-the-art results in areas like computer vision and natural language processing. Neural networks are the specific computational architecture underlying DL, and CTOs should invest in this if they need to evaluate AI vendor capabilities or build custom AI solutions. Reinforcement learning is a specialized branch of ML where an agent learns to make optimal decisions by interacting with its environment, and CTOs should invest in this if they need to optimize complex systems or develop autonomous systems. Agents are software entities that can autonomously execute tasks and make decisions, and their implementation introduces several important considerations, including transforming probabilistic AI into reliable production-grade systems and addressing explainability challenges. Ultimately, AI is just another strategic technology investment, and CTOs should focus on getting clear on their organization's goals, acknowledging unique technical landscapes, and extending an already stable foundation rather than serving as a band-aid for deeper engineering or process issues.