Choose the job before the model
An AI demonstration can be impressive without solving an important problem. Start by describing a specific task, the person responsible for it and the outcome that would make their work better. A product brief should explain why AI is suitable, rather than treat the technology as the brief itself.
The right first use case is often a bounded workflow with clear inputs and a way to assess outputs. Drafting, finding information, organising documents or assisting a repeated process can be easier to evaluate than an open-ended promise to transform an entire organisation.
Define what good means
Before deployment, decide how the team will recognise an acceptable result. Include difficult examples, incomplete information and cases where the system should ask for help. Test with the people who understand the work, rather than evaluate only whether the output sounds convincing.
Keep human oversight proportionate to the consequences of an error. Users should understand what the system is doing, what they need to review and where its limitations affect the workflow. The appropriate level of autonomy is a product and governance decision.
Make risk management continuous
NIST describes its AI Risk Management Framework as a voluntary resource for incorporating trustworthiness into the design, development, use and evaluation of AI systems. That lifecycle view is useful: evaluation should continue as the model, data and workflow change.
For an individual product, translate that perspective into ownership, feedback, testing and a clear route for handling problems. These practices need to fit the actual use case. A document that nobody uses is a weak substitute for a team that knows how to assess and respond.
Build a system people can use
The model is only part of the experience. Permissions, data access, response time, cost, failure handling and the interface all affect whether the tool is practical. Integrating those foundations can take more work than the first demonstration suggests.
The opportunity is to make a defined job work better. Start with that job, test the assumptions and expand only when there is evidence that the product is useful and that the organisation can operate it responsibly.