For public sector teams exploring AI, a clear service need is a stronger starting point than a technology demonstration. The early work should establish who benefits, what information is involved and how the team will judge whether the idea is useful.
Define the service problem
Describe a recurring task in plain language. Who performs it? Who depends on it? What makes it difficult today? Internal knowledge search or assistance with drafting routine material may offer a more bounded starting point than a system that makes consequential decisions.
Understand the information boundary
Identify approved sources and the people accountable for them. Consider access, accuracy, retention and the treatment of sensitive information with the appropriate specialists. Use an approved environment and suitable test material before connecting a pilot to operational information.
Make oversight operational
Name the person responsible for reviewing outputs and describe what that review requires. A generic instruction to keep a human involved is insufficient if the reviewer has no time, context or supporting evidence. Decide how users report problems and how the team responds.
Evaluate different needs and difficult cases
Test more than the easiest questions. Include ambiguous wording, incomplete information and requests outside the intended scope. Consider accessibility and whether different users can understand both the output and its limitations. Record observations so decisions can be revisited.
Make the next step evidence-led
Agree the criteria for continuing before the pilot begins. Assess quality, review effort, service usefulness and operational ownership. If the evidence is weak, refine the scope or stop. A small, well-understood result can be more useful than a broad rollout with unclear accountability.
Start with a bounded service need, approved information and a clear plan for human review.
A general perspective for planning discussions. Your organisation’s requirements and specialist advice should guide implementation.
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