Much of the conversation about AI focuses on ambitious, transformative use cases. In practice, the organizations that see value fastest usually start somewhere more modest — with the repetitive, document-heavy work that consumes skilled people’s time.
Look for volume, rules and friction
Good first candidates for intelligent automation share a few characteristics. They are performed frequently, follow recognisable patterns and create a backlog when demand rises.
- Extracting and validating information from documents
- Classifying and routing requests to the right team
- Drafting first versions of routine responses
- Checking records against policies before approval
Keep people in the loop
Effective automation doesn’t remove judgement — it moves it to where it matters. AI handles the preparation and the routine cases, while people review exceptions and make the final call. That balance builds trust and keeps the organization in control.
Build the foundations as you go
Each well-scoped use case strengthens the foundations for the next: cleaner data, clearer processes and governance that teams understand. That is how practical AI grows from a single workflow into an organizational capability.
