AI starts with a useful job

Find repetition

Start with a frequent task that has a clear output: inquiry classification, response drafts or documentation search. A broad instruction to implement AI is difficult to evaluate.

Prepare examples

Collect a small set of real examples you have permission to use. Label useful and poor responses. This dataset helps compare approaches and detect regressions after changes.

Keep people in control

Important decisions need confirmation and correction. The interface should expose the source of an answer and make reporting mistakes easy. Uncertain output should not become an automatic action.

Count the full cost

Alongside development, account for model usage, storage, integration maintenance and human review. Compare costs with time saved by the process, rather than the number of AI features.

Run a bounded pilot

Start with one team and a limited set of tasks. Track quality, processing time and correction rate. Use the pilot results to decide whether to expand.