What Makes a Good AI Use Case?
Strong early AI use cases often share four traits:
<ul>
<li>the workflow is repeated often enough to matter</li>
<li>the task contains messy text, images, or judgment-heavy work</li>
<li>partial assistance creates value even if the model is imperfect</li>
<li>there is a clear fallback when the AI is wrong</li>
</ul>
This is why AI is often strongest as an assistant before it becomes an autonomous actor.

Which candidate is the strongest first AI use case?

If a workflow has zero tolerance for mistakes and simple deterministic rules, traditional software may be a better first choice than AI.

Start from the User Problem, Not the Model
Weak AI ideas start with: "Where can we add AI?"
Strong AI ideas start with:
<ul>
<li>what feels slow, repetitive, or cognitively heavy for users?</li>
<li>where would a draft, recommendation, or prediction actually help?</li>
<li>what would better look like in measurable terms?</li>
</ul>

Which idea is the weakest PM framing for an AI project?

A good AI use case should have a measurable task-level outcome, not just novelty value.

Use a Value x Feasibility Lens
A practical PM filter includes:
<ul>
<li>user value</li>
<li>frequency of the task</li>
<li>data or context availability</li>
<li>error tolerance</li>
<li>review and fallback path</li>
<li>technical and economic feasibility</li>
</ul>
If you cannot explain recovery from a bad output, the idea is still underdesigned.

Which pilot is most likely to succeed first?

If you cannot describe how users recover from a bad AI output, the use case needs more design work.