Choosing a practical AI use case for your business

Start with the problem, not the model

New tools arrive constantly, but adopting more of them is not a strategy. Look for a task that takes time and produces an output someone can review. Sorting customer enquiries or summarising non-confidential documents may be a useful starting point. These are illustrative use cases, not reported client results.

Decide which data is permitted

Use only the information the task needs. Do not send personal records or confidential contracts to an external provider without reviewing access permissions and processing terms. Establish where data is processed, who can access it and how long it may be retained.

Test representative examples

Prepare non-sensitive samples covering normal and unusual requests. Review factual accuracy, clarity and the system’s ability to admit uncertainty. One convincing answer does not establish reliability.

• Can the answer be checked against a source?
• Does it hand sensitive questions to a person?
• Can errors be detected and corrected?

Measure before expanding

Compare time and quality before and after the trial, including software costs and human review. Outcomes vary by industry, data and task. Avoid assuming a fixed saving or return; expand only when your own evidence shows the workflow is useful.