LLMs were never built to run a company

Author - Enrique Dans

The first article opens with the uncomfortable truth behind the enterprise AI paradox: the technology that feels magical to individuals does not automatically become transformative inside organizations. Large language models are extraordinary engines for producing language, but companies do not run on language alone. They run on memory, context, feedback, constraints, permissions, incentives, workflows, and state. This is where the diagnosis begins: enterprise AI is not failing because people refuse to use it, or because the models are not powerful enough. It is failing because we mistook a conversational intelligence engine for an operational architecture. The article establishes the central gap that the rest of the series will progressively close: individual intelligence has been amplified, but collective intelligence has not.