Goals shape what you get rewarded for. If your AI goals are about learning tools, you will be evaluated on effort. If they are about outcomes and reach, you will be evaluated on impact. Here is how to set the second kind.
Write goals as outcomes, not activities
"Build an AI workflow adopted by two other teams" is a promotion goal. "Get better at prompting" is not. Frame every goal around a result someone else can verify.
Make each goal measurable up front
Decide how you will measure success before you start, so the win is undeniable when you hit it. Ambiguous goals produce ambiguous reviews.
Align them to the next level
Look at what the role above you is expected to do, and set AI goals that demonstrate you already operate there. Promotions confirm what you are already doing, so make the evidence early.
Most AI wins evaporate by review season because no one wrote them down. The work happened, the value was real, and by the time it matters everyone has forgotten. Keeping a running record is the cheapest career insurance you can buy.
The right goals turn a year of AI work into a documented case for the next level.
Make your AI impact impossible to ignore
Oberhahn shows how your AI work actually moves through your organization: the workflows you built, how many teams adopted them, and the results they produced. It turns work that usually disappears into a record you can put in front of your manager. Get started free at oberhahn.com.