Start with a workflow, not a model

The first question is about the work

A model demo can make an AI project look deceptively simple. You provide a prompt, it produces a convincing answer, and the next step appears to be adding a user interface. The difficult questions often arrive later: whose work changed, which data the answer used, and what happens when the answer is wrong.

Start those conversations before choosing a model. Pick a workflow that already has a recognizable beginning and end. It might be preparing a weekly account review, classifying an incoming document, or assembling evidence for a pricing decision. Identify the person responsible for the outcome and the person who will use the system every day.

Describe the current process

Write down the inputs, decisions and handoffs. Which steps involve looking up information? Which require judgment? Which actions can change a business record? A system that drafts a summary has a different responsibility from one that sends it to a customer.

For a hypothetical account-review assistant, the inputs might be approved sales records and meeting notes. The output could be a review brief with links to supporting records. Sending the brief and changing an opportunity stage could stay with the account owner. That boundary makes the initial task useful without pretending it is autonomous account management.

Define what good means

Create a small set of representative examples, including incomplete and contradictory information. Ask a domain expert what an acceptable result must contain and what would make it unsafe or misleading. Write those expectations as observable checks.

A practical first test can ask whether every material claim points to a permitted source, whether missing data is acknowledged, and whether the brief can be reviewed in the normal workflow. Speed matters, but only after the output meets the standard for use.

Choose the smallest useful release

A first release should answer a meaningful question, not demonstrate every possible capability. Keep the inputs bounded, make the review step clear, and give users a way to correct the result. Capture failures as examples for the next evaluation rather than quietly adding exceptions to the prompt.

Once that workflow works, compare models and tools against the same examples. You will have a reason to choose a particular system and evidence for revisiting the choice later.

The deliverable is not just a prompt. It is a working process with a responsible owner, a useful result and a test that tells you when it stops working.