Enterprises should familiarize themselves with four key types of AI: machine learning (ML), deep learning, simple AI, and generative AI. ML is the most common form, using algorithms to make predictions based on data. Deep learning, a subset of ML, employs complex neural networks for analysis. Simple AI automates basic tasks through rule-based systems, while generative AI, often based on deep learning, can create content like text and images.

When selecting AI tools, enterprises should evaluate their security, cost, infrastructure, and network needs. Public generative AI tools like ChatGPT can assist with content creation but should be reviewed by experienced personnel. For business analytics, private generative AI tools or deep learning features integrated into analytics software are recommended. It's also crucial to test any tools thoroughly before deployment, especially for real-time applications.

Developing custom AI models presents several challenges, including data management, infrastructure requirements, and the need for skilled personnel. Many enterprises may struggle with the technical expertise necessary to utilize open-source tools effectively. As the landscape of AI evolves, organizations should stay informed about new capabilities and consider specialized tools that can help them navigate these challenges.