Enterprise AI: Infrastructure Gaps & Solutions for Leaders

A significant gap exists between organizations wanting enterprise AI agents and having the necessary infrastructure to run them safely. This is according to four stories from ERP Today. Here's the thing: Databricks' high valuation points to the data layer, not the AI models themselves, as the real battleground. Also, most enterprise APIs were not built for agents to discover and execute against, as highlighted by Jentic's API scoring tool. What's more, the physical infrastructure for AI workloads is under pressure, with US data center compute spending up nearly 70% in one year. Anthropic's expansion into critical sectors like power and healthcare shows the security stakes for AI-connected enterprise systems are now at a critical-infrastructure level. The bottom line is that an AI agent acts inside enterprise systems, querying APIs, triggering workflows, and writing records. This differs fundamentally from a chatbot, which just responds to prompts. This difference in capability demands a fundamentally different infrastructure foundation, including governed data platforms, robust API layers, scalable compute capacity, and specialized security architecture. This matters because reliable and safe enterprise AI depends on understanding and building this new infrastructure.