Start with the business problem
A use case should begin with an operating problem, decision or outcome. “Use generative AI” is not a use case. “Reduce manual effort in supplier quotation analysis while preserving approval control” is closer to one.
Clear problem framing makes value and feasibility assessable.
Dimension 1 — Business value
Estimate the outcome that matters: revenue, margin, cycle time, risk, productivity, customer experience or decision quality. Avoid using time saved as the only value measure where the business case depends on something else.
Dimension 2 — Feasibility
Assess whether required data, integrations, process stability, technical pathways and operational ownership exist. Feasibility is not simply whether a model can perform the task in a demo.
Dimension 3 — Readiness
Connect the use case to enterprise readiness: data, leadership, systems, people, governance and existing AI footprint. A valuable idea with weak foundations may still be a poor Wave-1 choice.
Dimension 4 — Risk and control
Consider privacy, security, regulatory exposure, model error, explainability, human oversight and the cost of a wrong decision. High-risk use cases require stronger controls and evidence.
Dimension 5 — Dependency and sequence
Ask what must happen before this initiative can succeed. Some use cases unlock others by improving data, process or adoption capability. Others consume large effort without creating reusable foundations.
Create waves, not a ranked list
A numerical ranking can hide dependencies. Convert the portfolio into waves: start now, prepare next, explore later and do not start. This forces leadership to allocate attention instead of treating every idea as a priority.