When is an AI feature worth building?
Start with a task that costs users or staff effort and define what a useful result looks like. It may need AI, a clear rule or a simpler automation.
Define the task and inputs
Specify inputs and expected outputs: classifying a request, summarizing a document or producing a suggestion for review. Establish what data is available and permitted for use.
Choose examples that reflect real usage, including incomplete inputs and the language your audience uses.
Design for mistakes
Decide what the system may suggest and what requires human approval. Put review and correction within the working flow.
For decisions affecting people or money, review responsibility and automation limits with the relevant specialists before launch.
Trial before expanding
Compare results with the current method using a defined set of examples. Assess quality, time, cost and the effort of correcting errors.
Start within a limited scope and collect usage feedback. The name of the technology is not a measure of its value.