An AI project becomes easier to discuss when it starts with a familiar task. Perhaps your team answers the same internal questions, searches through long documents or copies information between tools. Start there, with the work people already understand.
Choose a task you can describe clearly
Write down who does the task, what information they use and what a useful result looks like. “Help support staff find the right policy” gives you something to test. “Transform the business with AI” does not yet tell a delivery team what to build.
Ask the people doing the work where time is lost. The best starting point may be a small inconvenience that happens every day, rather than the most impressive demonstration. Also ask whether a simpler search tool, clearer process or ordinary automation would solve it.
Agree what good looks like
Before choosing tools, agree a small set of checks. Can people complete the task more easily? Are the answers useful? How often does someone need to correct the output? Include setup time, review effort and running cost when deciding whether the pilot is worthwhile.
- Choose representative tasks, including difficult examples.
- Record the current process so you have a useful comparison.
- Decide who will check the output and what they are checking for.
- Agree when the tool should stop and hand over to a person.
Keep the first version narrow
Use information you are authorised to process and give the application only the access it needs. Keep sensitive or consequential decisions under human review. AI can produce convincing mistakes, so useful behaviour includes admitting uncertainty and directing people to the original source.
OWASP identifies risks such as sensitive information disclosure and excessive permissions in AI applications. These are good reasons to discuss access, data handling and review points before connecting a pilot to important business systems.
Decide what happens after the trial
At the end of the pilot, choose whether to improve it, expand it or stop. An honest decision to stop can be useful if it saves your team from maintaining a tool that does not help. If you continue, name an owner and agree how quality, costs and changes will be reviewed.
A useful first project creates evidence your team can understand. That makes the next investment decision clearer, whether the answer is more AI, a better workflow or a different tool.
Further reading
