What does AI Strategy mean?

An AI strategy sets out which tasks an organisation wants to use AI for, in what order, and under what conditions. It names the use cases, the responsibilities, the budgeted resources, and the criteria for stopping a project. It differs from AI governance in that it sets direction, while governance sets the rules for implementation.

It starts with a stocktake of tasks that have a high case volume and a clear structure. For each use case, four factors are estimated: the state of the data, the legal basis, the expected relief in workload, and the effort required to operate it. Comparing these produces a sequence that starts with the cases best supported by evidence and least critical from a legal standpoint. Added to this are decisions on the operating model, on upskilling staff, and on which models must run in-house.

A strategy pays off once several departments are planning their own initiatives and need the same infrastructure. It prevents three tools being procured in parallel for the same task, each requiring its own separate operation. Where an organisation is instead trialling a single initiative, a project plan with a fixed review date and a named budget is enough.

The advantage of a defined sequence lies in bundling. Procurement, training, and operations can be planned across several years because demand is known in advance. An agreed review point turns ending an initiative into a normal option rather than an admission of failure.

A strategy remains a statement of intent as long as operations are left unresolved. Who runs the models, who updates them, and who bears the ongoing cost must be spelled out in it. This includes specifying the legal jurisdiction in which the data processing takes place.