The most common way an AI initiative dies is being too ambitious on day one. The flagship project has the most eyes, the least slack, and the highest cost of being wrong — which is exactly why it’s the worst place to learn.
Boring is a strategy, not a compromise
Pick something small, well-bounded, and reversible. A task where “good enough” is genuinely good enough, where a mistake is caught cheaply, and where success is measurable in weeks. You’re not trying to transform the business yet. You’re trying to build the muscles — evals, monitoring, human-in-the-loop, deployment — you’ll need when you do.
What a good first project looks like
- The output is checkable by someone who already does the work.
- Failure is annoying, not catastrophic.
- It touches real production data, not a sandbox.
- You can ship it in a month and know whether it worked.
The reward for a boring project done well is the credibility to attempt an ambitious one.
Do the boring thing first. Earn the right to be interesting.