The useful-AI test: start with the decision, not the demo.
AI projects are easy to start and surprisingly hard to make useful. A strong demo can create momentum, but it cannot answer the question that matters most: what decision, piece of work, or relationship becomes meaningfully better if this succeeds?
Begin with the moment of work
Before choosing a model, a platform, or an automation, describe the moment someone is trying to improve. Who is making a decision? What information is missing, slow, inconsistent, or difficult to use? What does a better outcome look like in plain language?
If that moment is vague, the implementation will be vague too. The technology may still be impressive, but impressive is not the same as useful.
Choose a measure that belongs to the work
Model accuracy is important, but it is rarely the whole scorecard. A useful AI initiative may reduce rework, shorten the path to a sound decision, make expertise easier to reach, or help a team focus on the exceptions that deserve judgment.
The best measure is one the people doing the work already recognize. It connects the investment to a real operating improvement rather than a technical milestone.
Design for judgment, not just automation
Good systems make it clearer when a person should step in. They expose uncertainty, preserve context, and leave room for expertise. The goal is not to remove judgment from important work; it is to make judgment more available where it matters.
The useful-AI test
Before moving forward, ask: Will this improve a specific decision or action for a specific group of people—and can we tell? If the answer is yes, there is something worth building. If the answer is not yet clear, the next step is not more technology. It is a better question.
This is an editorial draft prepared for Cameron’s review before any public launch.