A useful AI conversation starts with your data

Start with a decision or a task

Identify a specific task you want to improve. Explain who performs it, what makes it difficult today and how you would assess improvement. This gives the discussion a practical focus.

Understand the data foundation

Check where the required information lives, how it is maintained and who can access it. Missing, inconsistent or poorly understood data can become a larger constraint than the model itself.

Keep responsibility clear

Define where human review is needed and who owns the outcome. Plan how users can question an output and how the organisation will respond when a result is wrong.

Learn through a bounded use case

Choose a manageable first scope, agree evaluation criteria and review results with the people who will use the solution. Expand only when the evidence supports the next step.