AI can create meaningful value, but only when it is applied to a clear business problem and supported by reliable data, sensible controls and engaged people. This checklist helps leadership teams identify where they are ready and where foundations need strengthening first.
1. Start with the business problem
The strongest AI initiatives begin with a measurable operational challenge rather than a technology experiment. Define the process, the current cost or delay, and the result you want to improve.
- Is the problem frequent and commercially meaningful?
- Can success be measured in time, cost, quality or revenue?
- Is there a clear process owner?
2. Check your data foundations
AI depends on accessible, trustworthy and appropriately governed information. Data does not need to be perfect, but teams must understand where it comes from and whether it is accurate enough for the intended decision.
- Are key data sources identified and owned?
- Is the information complete and current enough?
- Can sensitive data be protected and access controlled?
3. Assess process maturity
Automating a poorly understood process often makes the problem faster rather than better. Map the current workflow, identify exceptions and remove unnecessary steps before introducing AI.
4. Prepare people and governance
Employees need to understand how AI will support their work, where human review remains essential and how concerns will be raised. Practical governance should be proportionate to the risk of the use case.
- Named business and technical owners
- Clear human review points
- Documented privacy, security and quality controls
- Training for the people using the solution
5. Run a focused pilot
Choose one valuable, controlled use case and establish a baseline before implementation. A short pilot should prove value, expose risks and provide evidence for the next investment decision.
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