An AI initiative is easier to justify when it starts with a measurable operational problem rather than a general ambition to “use AI”. The best early candidates combine repetitive information work, clear human review and enough volume for saved time to matter.
Choose a bounded workflow
Good pilots have a clear input, output and responsible owner. Examples include classifying incoming documents, drafting a response from approved account data or finding evidence across a controlled knowledge base.
- Frequent enough to measure
- Currently consumes skilled time
- Errors can be detected
- A human can review early outputs
- Data access can be controlled
Calculate value honestly
Measure current handling time, rework, waiting and missed opportunities. Automation rarely removes every minute, so model assisted handling rather than total replacement.
Include model usage, integration, evaluation, monitoring and exception handling in the cost. A pilot that saves seconds on a low-volume task may be technically successful but commercially irrelevant.
Design controls before scale
Access permissions, retention, approved data sources and audit history should be part of the first architecture. Consequential actions need deterministic checks and explicit approval rather than unrestricted model autonomy.
Evaluate with representative cases
A polished demonstration is not an evaluation. Build a dataset containing normal, ambiguous and difficult examples, then measure correctness, unsupported claims, handling time and reviewer confidence.
Move to production in stages
Begin with suggestions, then controlled automation for low-risk cases, and expand only when monitoring shows stable results. Keep a straightforward way to pause the workflow and return to manual processing.