The OKR framework — Objectives and Key Results — keeps ambitious work measurable, and AI initiatives need that discipline more than most. It is easy to set an objective like “use AI to improve support” and impossible to know if you succeeded. This generator takes your objectives, current baseline metrics, and a time horizon, and produces properly structured OKRs: a qualitative, motivating objective paired with two to four measurable key results that move a number from where it is today to a target by a deadline.
How it works
You enter one or more objectives — the directional, qualitative outcomes you care about this quarter or half. For each objective you list baseline metrics (where each number stands today) and pick a time horizon. The tool pairs every objective with key results phrased as “move metric X from baseline to target by [horizon]”, suggesting sensible targets relative to your baseline and flagging where a baseline is missing. The output is formatted text you can paste into Notion, a spreadsheet, or any OKR tool. All processing is local — your metrics never leave the browser.
What makes AI OKRs different from standard ones
AI projects are particularly prone to poorly written OKRs because the work feels inherently hard to measure. “Improve our AI” or “leverage machine learning” describe activity, not outcomes. The discipline of writing a key result forces the question that matters: what will a user or the business be able to do, at what rate, compared to what baseline?
Good AI OKRs also tend to need two kinds of key results in parallel: usage metrics (adoption, engagement, task completion) and quality metrics (accuracy, error rate, human-override rate, user satisfaction). An AI feature that many people use but that produces wrong answers is not a success, and one that performs perfectly but nobody adopts has not delivered value. Capturing both dimensions in the key results catches both failure modes.
A worked example
Objective: Make our AI writing assistant genuinely useful to the sales team.
For this example, say the current baseline is that 12% of the sales team uses the assistant weekly and the output quality score (a thumbs-up rate collected in the product) is 55%.
Three illustrative key results might be:
- Increase weekly active users in the sales team from 12% to 40% by end of quarter.
- Raise the assistant’s thumbs-up quality rating from 55% to 75% by end of quarter.
- Reduce average time-to-first-draft for customer proposals from 4 hours to 1.5 hours as measured by the sales team’s self-reported survey at quarter end.
These are illustrative targets only. Realistic targets for your context will depend entirely on your real baselines and team capacity.
Tips and examples
Keep objectives qualitative and inspiring, and keep key results brutally numeric — “Objective: make our AI assistant genuinely trusted” pairs with “KR: raise thumbs-up rate from 62% to 80%”. Aim for two to four key results per objective; one is too fragile, five dilutes focus. Stretch targets are healthy in OKRs, so landing at 70% of an aggressive key result is often a win — but only if the baseline is real. Re-run the generator at the start of each period and archive the previous set so you can grade what you committed to last time.