At Credal, they've observed that enterprises adopting AI often go through four distinct stages, from initial excitement and experimentation with open-source libraries to common challenges such as privacy, security, and compliance. Enterprises tend to adopt one of two strategies: either building their own internal wrapper around a third-party AI provider or buying an existing platform. The former approach is typically used for core business processes, while the latter is preferred for simpler use cases. Credal emphasizes the importance of having a multi-LLM strategy and working with a model-agnostic platform that allows for easy configuration and customization. However, enterprises face several challenges, including data security, use case discovery and value quantification, duplicate use cases and data fragmentation, legal and regulatory barriers, human resources challenges and training, and unpredictability in RAG and difficulty of debugging.