The right AI decision begins with a clearly defined business problem, a realistic view of readiness and a measurement plan. This guide provides a decision framework your team can use in early discussions.
Begin with the operating outcome
Avoid starting with a model name or a list of features. Describe the work today, who owns it, the cost of delay or error, and the improvement that would matter. This becomes the baseline for evaluation.
Assess value, feasibility and risk together
A promising use case needs more than potential value. Consider data availability, integration effort, user adoption, failure consequences, privacy and the need for human approval. A balanced score prevents attractive ideas from bypassing practical constraints.
Design the smallest useful test
Limit the first implementation to one workflow, a defined user group and explicit success measures. Test quality across representative examples, including difficult cases and safe failure behaviour.
Plan production before the pilot ends
Ownership, monitoring, permissions, escalation, support and change management are production requirements. Discuss them early so a successful test does not become a stranded prototype.
Questions to take into your next workshop
- What outcome and baseline will we measure?
- What information and systems are required?
- What happens when the AI is uncertain or wrong?
- Who approves, monitors and improves the workflow?
- What evidence is required before scaling?