A practical framework for prioritizing AI use cases

The question we hear most often isn't "can AI do this," it's "which of these fifteen ideas should we actually build first." That's a prioritization problem, not a technology problem.

We score every candidate process on two axes: value (how costly or slow is this today, and who feels it) and feasibility (how clean is the underlying data, and how contained is the decision the agent needs to make).

High value, high feasibility processes go first — they're the ones that prove the model works and build organizational trust for the harder ones. High value, low feasibility processes usually need a data engineering pass before they're worth automating at all.

The trap is chasing the most exciting use case instead of the most tractable one. A boring win in month one buys you the credibility to tackle the ambitious project in month six.