Pick the right use case

Select the problem before choosing the implementation. A technically successful build still fails its purpose if the use case is unsupported by process evidence, difficult to control, or too small to justify its operating cost.

​

The screens

​

Account for uneven model capability

AI can perform well on one task and fail on an adjacent one. Do not assume a use case is feasible because a similar one worked. Carry several candidates into testing.

​

Account for the required accuracy

Early candidates are easier to test when the work is frequent, the inputs already appear in the Company Brain, and a person can catch likely errors before they cause harm. Work that requires near-perfect accuracy needs stronger deterministic controls and may not suit an agent.

​

Qualify each candidate

Apply three tests:

​

Know when to decline

Decline a candidate when the work depends on irreducible human judgment, regulations require human review, or the expected benefit cannot justify the build and operating cost.

​

Apply an enterprise bar

Prioritize work with a durable, named owner and a measurable effect on a team, value stream, or system of record. Separate that from improvements that benefit only one person’s working style.

​

Finding the candidates

The screens above tell you what to keep. To generate the candidates in the first place, Find transformation opportunities covers the tool chain for scanning the process tree and ranking what comes back. Form a transformation thesis goes deeper on a single candidate, including its blast radius. Once you know what to solve, choose the shape it should take in What you can build.