Start with the work, not the model.
Generative AI becomes useful when it is attached to a real workflow with a clear failure mode. If the task has no owner, no review path, and no measurable consequence, the implementation will usually become a demo rather than a system.
The first question is not which model is newest. The first question is where language, judgment, retrieval, drafting, classification, or coordination is currently slowing the organization down.
Where it tends to work.
The strongest use cases often sit in messy middle layers: turning scattered knowledge into usable drafts, helping teams compare options, routing information, supporting review, and making expert work faster without pretending the expert disappears.
That requires infrastructure, permissions, evaluation, and training. Without those, AI adoption becomes a collection of isolated experiments that are impressive for a week and untrusted a month later.