AI use‑case prioritizer
Not every AI idea deserves to be built first. Rate yours on impact, effort, and risk to see which to start with.
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Your use cases
Rate each idea from 1 to 5. Impact is the value if it works. Effort is how long it takes to build and roll out. Risk is the harm if the AI gets it wrong, from sensitive data and customer impact to compliance. The examples are only samples, so change them to fit your work.
Ranked
- Draft first-pass sales quotes79 out of 100
- Summarize support tickets for agents55 out of 100
- Automate refund approvals47 out of 100
- Train a custom model on internal data35 out of 100
- Rewrite meeting-room signage20 out of 100
Where they land
Quick wins
Real value for modest effort. Start here.
- Draft first-pass sales quotes
- Summarize support tickets for agents
Big bets
Worth doing, but plan the investment.
- Automate refund approvals
- Train a custom model on internal data
Fill-ins
Easy, but small. Do them when there’s slack.
- Rewrite meeting-room signage
Reconsider
Hard for what they return.
Nothing hereHow the ranking works
- How do you prioritize AI use cases?
- Score each candidate on the value it would create, the effort to build and adopt it, and the risk if it gets something wrong. Start with high-value, lower-effort ideas, plan the high-value, high-effort ones as deliberate bets, and drop the rest.
- How is the score calculated?
- Impact sets the ceiling: an impact of 5 can score up to 100, and an impact of 1 can score at most 20. Effort then discounts the score by up to 35%, and risk by up to 15%. That way a trivial idea can’t outrank a major one just because it’s easy. The weights are a starting point, so use the ranking to start a conversation rather than to settle one.
- What counts as a quick win?
- An idea with impact of 3 or more and effort of 3 or less. These are the best first projects because they show value quickly and build the team’s confidence.
- Why does risk only discount the score a little?
- A high-risk idea can still rank well on value and effort. That is why anything rated 4 or 5 for risk gets a flag: someone needs to stay accountable for the output, with a review step designed in from the start.
- What does this tool leave out?
- Dependencies between projects, data readiness, cost, and who owns the work. It also doesn’t save your list, so copy the ranking before you leave the page.
I’m Courtney Stephenson, an AI strategy and implementation leader, and I built this tool. If you’re deciding where to start with AI, let’s talk.
Want a second opinion on your results?
Book a free 15-minute call and we’ll talk through what this means for your team.