AI Use Case Scoring Framework
A practical framework for prioritizing AI opportunities based on frequency, effort, importance, and data availability. Identify high-ROI use cases for your organization.
- Prioritized list of AI use cases
- Clear scoring criteria for future opportunities
- Framework for building business cases
- ROI estimation approach
Not all AI opportunities are created equal. This framework helps you systematically evaluate and prioritize AI use cases based on four key factors that predict success and ROI.
A look inside
Three spreads from the playbook, so you know what you are getting before you give us your email.
Overview
The scoring matrix
Interpretation guide
Who it is for
Written for the people who sponsor, own or run the work.
Executive sponsors
Deciding where AI budget goes first, and what it should return.
Operations leaders
Owning the workflows AI will change, and the team that runs them.
IT and data leads
Asked to make it safe, connected and supportable.
Organizations that have worked with Dual Logic.
Questions
The short answers. Anything else, email hello@duallogic.ai.
How do you prioritize AI use cases?
Score each candidate from 0 to 3 on four factors: how often the task happens, how much effort it takes today, how much the quality of the output matters, and how good the reference material is. Add them up for a total out of 12. Scores of 10 to 12 go first, 7 to 9 make a second wave, and 3 or below may not suit AI yet.
What makes a good AI use case?
A task that happens often, takes real effort, matters to the business, and comes with good reference material, such as templates, style guides or past examples. The more of those four it has, the higher the likely return. Reference material often decides success: if a promising task scores low on it, document what your experts know and gather good examples first.
How many AI use cases should we start with?
A few, not a long list. Dual Logic’s AI Integration Blueprint ends with three to five prioritized opportunities, and this guide suggests piloting with one team or workflow first. Pick candidates that score 8 or more, have a clear measure of success and a willing owner, then measure the real time saved before you scale to more teams.
How does Dual Logic’s scoring framework estimate the return on an AI use case?
By multiplying the time a task takes by how often it happens in a year, then by the share of that time AI should save. That gives the hours saved a year; multiply by a blended hourly rate to put a value on them. Then check the estimate in a pilot, and count quality gains too: fewer errors, more consistent work and faster turnaround.
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