AI strategy · 7 min read
Is your AI product ready to build?
An AI demo can be persuasive long before the surrounding product is ready. The build decision should include the user, evidence, data, failure path, and human responsibility — not only model capability.
01
Name the decision or task
Start with the work a person is trying to complete and the improvement the AI capability might make. Broad goals such as “add an assistant” do not define a useful product boundary.
A strong use case identifies the input, expected output, source of truth, time or judgment involved, and what a person does next.
02
Design for uncertainty
Model output is probabilistic. The product has to communicate uncertainty, preserve relevant sources, validate structured results, and route high-impact cases to human review.
The acceptable failure mode depends on the task. A draft marketing idea and a recommendation affecting money, health, or access require different controls.
03
Plan the operating loop
AI products need evaluation examples, feedback, model and prompt change control, cost visibility, incident handling, and owners who can interpret what the system is doing.
If those responsibilities are invisible, the prototype may work while the product remains impossible to operate responsibly.