A useful AI conversation starts with the work you want to change, not the model you want to try.
It is easy to start an AI project with a feature: a chatbot, an agent, a summary button. Those can all be useful. But a feature is a proposed answer. Before building it, spend time on the question.
Describe one real piece of work.
Pick a task that someone already does. What arrives? What information do they look for? Which decisions need judgment? What do they produce, and who uses it next? Walk through an ordinary example and one awkward exception.
This turns an abstract ambition into something you can examine. It may reveal a good place for AI. It may also reveal a missing integration, unclear ownership, or a validation rule that would be better handled without a model.
Know what a useful result looks like.
Collect a small set of representative inputs. Include cases that are incomplete, ambiguous, or outside the intended scope. Write down what a good answer should contain, when the system should ask for help, and what it should decline to do.
These examples become a shared way to assess a prototype. They are more useful than deciding whether a single polished demo feels impressive.
Make room for a simple answer.
Compare the model-based approach with the simplest credible alternative. A search improvement, a better template, or a conventional integration may solve part of the problem more predictably. AI can still have an important role; it just needs a job that suits it.
Once the work and the evaluation are clear, model choice becomes a grounded engineering decision. You can discuss capability, context, latency, cost, and operating constraints against the same task.
“Which part of this workflow is hard because the information is unstructured or the judgment is difficult—and how would we know if assistance helped?”
