AI consultant for practical implementation
A practical AI consultant should be able to work from a real business decision through to the information, workflow, review point, and measurable outcome required to support it. A list of tools or a broad opportunity map is not the same as an implementation plan.
( The Detail )
Questions worth asking before you engage
Ask how the work will establish source-of-truth information, how risks and approvals will be handled, who owns the system after handover, and how the first result will be measured. The answers reveal whether the work is grounded in operations or simply in technology selection.
How to approach it
Begin with the decision rather than the tool. Name the recurring judgement this affects, the information it depends on, and the person accountable for acting on the result. That framing keeps the first build small enough to inspect and useful enough to matter.
Keep a human review point in the loop until the quality and the failure modes are understood. A system that shows its working - what it drew on, where it is uncertain, and what it deliberately left alone - is one a business can keep running after the initial build.
( Next Step )
Start small enough to review, but on a workflow important enough to show whether a better system is worth building.