1.Most AI strategies describe opportunity themes
Most AI strategies describe opportunity themes. Fewer define the operating conditions required for those themes to become production systems. Strategy fails when it stops at aspiration and never settles ownership, data foundations, governance gates or reuse.
2.Perspective 2
A practical AI strategy answers four questions: which decisions will improve; which platforms and data products are shared; which controls apply by risk tier; and who is accountable for value after launch.
3.Perspective 3
We advise leaders to build a portfolio that mixes near-term operating improvements with platform investments. Purely opportunistic pilots create tool sprawl. Purely platform programmes delay visible value. Balance is a leadership choice, not a technology accident.
4.Strategy also needs an adoption path
Strategy also needs an adoption path. Training, change management, process redesign and measurement belong in the same plan as models and interfaces.
5.Perspective 5
When strategy, CoE and governance are designed together, organisations avoid the common stall: many proofs of concept, little production discipline and unclear evidence of impact.
