seolounge
A promising AI project begins with a repeatable task that already has an owner. Examples include finding information across approved documents, routing routine requests or summarising material for human review. Start with a measurable bottleneck rather than a tool demonstration.
Test the inputs
Check data quality, permissions, privacy obligations and the edge cases that cause errors. Define what the system may do, what it must never decide alone and when a person takes over. The workflow should still work when an AI output is missing or wrong.
Run a bounded pilot
Use representative cases and agreed measures such as review time, completion quality or error rate. Keep a record of failures and feedback. A small pilot can reveal whether the proposed automation saves work or creates extra checking.
Prepare for operation
Document ownership, monitoring, access and change control. Expand only when the benefits and safeguards hold up in real use. A useful system supports people making decisions; it does not remove accountability.
