When companies first bring in a large model, expectations centre on the model itself: can it understand the request, can it produce something usable. After a period of trial, the conversation usually moves.
The real difficulty sits outside the model. Company data is typically scattered across systems in inconsistent formats with complicated permissions. However capable the model, it cannot compensate for data that is not usable.
Redesigning the process beats swapping the tool
Dropping a model into an existing process tends to produce only local gains. The larger return comes from redesigning the process itself, so that human judgement concentrates where the model is weak, such as handling exceptions and making the final call.
That kind of change touches job descriptions, and it is far harder to push through than a technical deployment. Without organisational backing, tools end up unused, not because they work badly but because nobody has been asked to work differently.
Responsibility has to be settled in advance
Once a model participates in external communication, risk decisions or content production, the question of who answers for a mistake cannot be avoided. The safer approach is to define the boundaries within which model output may be used, and to keep an auditable record of review.
These arrangements look like added process overhead, but they are the precondition for letting a model near core operations. Without clear responsibility, the scope of use stays permanently in low-risk assistance.
