How to Prioritize AI Use Cases (Impact vs Feasibility)
The hardest part of an AI roadmap is not coming up with ideas. It is choosing which one to do first. Most leaders have a list of twenty things AI "could" do. Twenty is the same as zero until you pick.
Here is a simple way to rank them, so you start with the project most likely to win.
Score on two things: impact and feasibility
For each idea, rate it on a scale of 1 to 5 on two questions:
- Impact. If this worked, how much would it move a goal you care about? Hours saved, cost cut, revenue added. High impact means a real, sizable number.
- Feasibility. How hard is it to actually ship? Consider the data you need, the complexity, and how easily people would adopt it. High feasibility means you could do it soon, without a heroic effort.
Multiply or just plot the two. The projects you want first are high impact and high feasibility. They prove value fast and build momentum.
How to read the four quadrants
- High impact, high feasibility: Do these first. This is where you start.
- High impact, low feasibility: Worth it, but later. Earn the runway with quick wins, then tackle these.
- Low impact, high feasibility: Easy but not worth much. Skip unless they are nearly free.
- Low impact, low feasibility: Ignore. These are the ideas that quietly drain teams.
A quick example
Say you are weighing three ideas: an AI tool to draft support replies, a custom model to predict churn, and an AI assistant for the sales team's research.
Drafting support replies is high impact (high volume, daily) and high feasibility (the data is your own tickets). That goes first. The churn model might be high impact but low feasibility right now if your data is messy. It waits. The sales research assistant lands somewhere in between, so it goes second.
You did not need a committee. You needed two scores and a moment of honesty.
The traps
- Scoring by excitement. The flashiest idea is rarely the highest feasibility. Rate it honestly.
- Ignoring adoption. A project people will not use is low feasibility, no matter how clever. Count that in the score.
- Refusing to skip things. A good roadmap has a "not now" pile. Protect it.
Where this fits
Prioritizing is step three of building a roadmap, which we lay out in Building an AI Strategy and Roadmap. Scoring opportunities by impact and feasibility is also the Prioritize phase of the Fractional CAIO Sprint.
Book a call and we will score your top AI ideas together and pick the first one.
Frequently asked questions
- Q: How do I choose which AI project to do first?
- A: Score each idea on impact (how much it moves a goal) and feasibility (how easily you can ship and adopt it). Start with the one that is high on both. It proves value fast and builds momentum for bigger projects.
- Q: What makes an AI use case high impact?
- A: It maps to a goal you already care about and moves a real number: significant hours saved, cost cut, or revenue added. High volume and frequency usually mean higher impact.
- Q: Should I start with the hardest, most valuable AI project?
- A: Usually no. High-value but low-feasibility projects are worth doing later, after quick wins have built trust and freed up runway. Starting with the hardest project is how AI efforts stall.