Most AI failures do not start with the model. They start with the use case.
Organizations often begin with tool enthusiasm, broad ambition, and too little pressure-testing. They choose work that is hard to measure, poorly structured, or not important enough to justify the change effort around it. Then they discover that the workflow, the information, and the trust conditions are not strong enough to support the result.
The real problem
AI does not create value in a vacuum. It depends on the business outcome, the information foundation, the workflow design, and the organization's willingness to operate differently.
The best first step is usually not more tooling. It is sharper fit, stronger focus, and a more realistic understanding of what needs to be true before AI will help.