Most AI projects fail the same way: a demo looks magical, everyone gets excited, and six months later nothing has actually changed about how the work gets done. The gap between an impressive demo and a system people rely on is where most of the budget quietly disappears.
This is a short, practical checklist for deciding whether an AI project is worth building — and for spotting the ones that are theater, not value. It comes out of the same approach behind the Innovation or Theater decision framework and the AI implementation assessment workbook.
Before you build
- Name the decision or task it changes. If you can't point to a specific decision, output, or step that will be faster, cheaper, or more reliable, you don't have a project — you have a demo.
- Find the human who owns the result. Tools nobody owns quietly stop being used. Someone has to be accountable for the outcome, not just the rollout.
- Write down what "good enough" looks like first. Define the bar before you see the output, or every result will feel impressive enough to ship.
- Weigh the cost of being wrong. Low-stakes, reversible tasks are where AI earns its keep first. Save the high-stakes, hard-to-undo work for after you trust it.
While you build
- Keep a human in the loop where it matters. The goal is reviewed workflows, not unattended magic — decide which steps a person checks and which can run on their own.
- Measure against the old way. If you can't compare the AI path to what you did before, you can't tell whether it's actually better or just newer.
- Ship the smallest useful version. A narrow tool one person uses every day beats a broad platform nobody opens.
Signs it's theater, not value
- The win is "it's so cool" rather than a saved hour or a clear number.
- It only works in the demo — on clean inputs, with the person who built it driving.
- Nobody can say what happens when it's wrong.
- It adds a step instead of removing one.
If a project clears the checklist, it's usually worth building. If it trips on the theater signs, the honest move is to stop — or to scope it down to the one piece that genuinely earns its cost.
This is the kind of call I help with directly. If you're weighing an AI project and want a second opinion before you spend, here is how to work with me — or start from the practical AI implementation hub.