AI cannot optimize a company it cannot understand

08.31.2026

Author - Enrique Dans

To start optimizing a company with artificial intelligence, AI needs to understand what you want to optimize; AI cannot properly optimize what it cannot represent. The paradox is that companies are giving increasingly sophisticated and capable models access to the tools and workflows they use, but they have only a fragmentary representation of the map of the organization they are trying to optimize.

Even if a large language model is extremely knowledgeable about management, marketing, or logistics (after all, LLMs have “read” pretty much every book about it), it usually knows nothing about your specific customers, your dependencies, your bureaucratic processes, any possible exceptions, your risk tolerance, or the cascading implications of changing a process. We need to separate plain memory from what constitutes a real data model; while memory can recover what happened at a certain time, a data model can formally represent identities, relationships, permissions, constraints, and valid states. 

Nowadays, companies try to bridge this gap by providing as much context as they can, but context does not answer something as relevant as how the organization works. We have to move from context towards a model of organizational dynamics. 

First wi have to start with ontology: representing real-world entities as objects, their relationships as links, and the company’s actions as verbs. An operational layer that couples “nouns” with “verbs,” in a way that things such as factories, orders, workers, or customers are in the first group, and concepts such as launching promotions, adjusting prices, changing distributors, or executing a workflow are in the second. 

However, an ontology just tells you what’s in the company and what we can do with it. The next step is a world model, an internal representation capturing the dynamics of an environment, sufficiently well as to allow predicting consequences and supporting planning.

Once you have your company model, you can make it more and more valuable by feeding actions and outcomes back into it. Capturing decisions and their outcomes allows future decisions to be framed in the context of previous choices, and can feed retraining or fine-tuning. That way, with these loops, the company starts to own its own learning, as Satya Nadella said. 

Oganizations are complex, dynamic, and politically loaded, so the model will have to remain partial, open, revisable, and governed. The goal is not to build some omniscient oracle, but a representation that improves across time with its own context, rules, history, and accumulated learning.

Before a company can become optimizable, it has to become understandable for the intelligence trying to optimize it.