AI Requires Understandable Company Model for Optimization

Artificial intelligence's ability to optimize company operations hinges on its deep understanding of the organization's structure and operational methods, according to Fast Company. Simply providing context to AI is insufficient; a comprehensive model of company dynamics is essential.

AI needs to comprehend what is to be optimized within a company. While large language models possess knowledge of general business areas, they lack familiarity with a company's specific customers, internal processes, risk tolerance, or the cascading implications of changes. Understanding these interdependencies requires more than just memory; it necessitates a formal data model that describes identities, relationships, and constraints.

Companies attempt to bridge this gap by providing AI with more contextual information, but this does not address how the organization functions. The proposed solution is to define an ontology, specifying the company's "nouns," "verbs," and rules. This builds the foundation for an internal company model, acting as a form of digital twin.

Following the ontology, the next step is a "world model" that learns company dynamics and predicts future events. This enables the understanding of cause-and-effect relationships, which is crucial for reliable algorithmic decision-making. Companies can continuously improve this model by feeding it information about decisions made and their outcomes, allowing the company to own its learning.

The ultimate goal is to move towards an "understandable company" before it can become truly optimizable. The next phase of corporate digitization will involve modeling companies so that AI can grasp the meaning of its actions, building upon proprietary representations that hold more value than the AI model itself.