Generative AI is most useful when it is attached to a real workflow, a measurable business outcome and a clear operating owner. The first question should not be “Which model should we buy?” but “Where are people repeatedly spending time interpreting, searching, drafting or coordinating information?”
Start with a narrow workflow
Good first candidates are knowledge search, customer-service assistance, document summarisation, internal research, proposal drafting and structured data extraction. These use cases are easier to evaluate than broad “AI transformation” programmes.
Score value and feasibility together
Assess each idea against business value, data availability, integration effort, privacy requirements, failure cost and expected usage. A high-value idea with inaccessible data or unacceptable failure risk is not a good first pilot.
Design evaluation before production
Create representative examples, define acceptable answers and measure factuality, task completion, latency and cost. Human review should remain part of the process for workflows where errors carry meaningful consequences.
Move in stages
- Discovery and process mapping.
- Controlled prototype with representative data.
- Pilot with limited users.
- Production engineering, permissions and monitoring.
- Iteration based on measured outcomes.
The objective is not to “use AI”. It is to improve a business process with an architecture that remains useful, governable and economically sensible.