AI strategy only matters if it turns into AI products that people use.
The goal is to turn priorities into a delivery backlog, and to make ownership and decision rights explicit so work keeps moving after the first pilot.
At a minimum, this translation should answer what gets built next and why, what can be reused and what must be built from scratch, and who approves go-live and owns the system after launch.
AI products are the practical manifestation of strategy. They are the interfaces, applications, and systems that interact with users and operational processes.
They can look very different depending on context. Some are customer-facing. Others are internal, such as decision-support workflows.
A useful way to keep this grounded is to define each AI product in terms of the workflow it supports, the outcome it is meant to improve, and the owner responsible for adoption and iteration.
Building AI products typically combines:
The goal is not to build "smart technology." The goal is to build useful products that fit the organization and create lasting value.
Use this lightweight operating model to stop pilots from stalling and to avoid rebuilding the same assets repeatedly.
1) Decision rights (who decides what)