The real reason enterprise AI is stuck

The reason enterprise AI remains stubbornly artisanal is not because models are too weak. It is not because context windows are too short, or agents need better prompts, or companies are resisting adoption. Those are all visible problems. But they are not the deepest one. The deeper problem is that the industry is still building from metaphors. And metaphors do not industrialize. Over the last two years, enterprise AI has become filled with human analogies. We talk about memory, reflection, planning, delegation, feedback, even sleep. Business Insider recently described Anthropic’s “dreaming” technique for AI agents, a telling example of how naturally the industry reaches for human metaphors when describing systems that are, in reality, computational architectures. The metaphors are useful. They make complex systems easier to understand. They help product teams explain what their systems do. They help executives believe they are buying something familiar. But there is a difference between a metaphor and a model: a metaphor describes something. A model formalizes it. That distinction may explain why enterprise AI still feels trapped between astonishing demos and frustrating deployments. Software becomes industrial when it becomes formal Every major software revolution followed the same pattern: first came capability. Then came formalization. Only then came the platform. Relational databases did not emerge because someone built a better filing cabinet: they emerged because Edgar F. Codd introduced a formal relational model of data, defining a way to think about relations, operations, redundancy, consistency, and data independence. SQL, applications, vendors, and ecosystems came later. First came the abstraction. The web did not become transformative because browsers got prettier: it became transformative because resources acquired formal identities. The W3C’s Architecture of the World Wide Web defines the web as an information space in which resources are identified by