AI Use Case Scorer: Which Task Should AI Do First?
Lists of generative AI use cases are everywhere. The hard part is picking the one that will actually pay off in your business. Score up to three of your own tasks on six factors and the scorer ranks them, so your first AI project is the one most likely to save real time.
Why most AI use case lists don't help
Search for gen AI use cases and you'll find hundreds: chatbots, content, forecasting, coding. They're possibilities, not decisions. Whether a use case pays off depends on your task: how much time it takes, how repeatable it is, how clean the inputs are and what happens when the AI is wrong. Many AI projects stall because they started from the list rather than the work, a theme in our write-up of the MIT GenAI Divide report.
The AI use case scorer
Rate each task from 0 to 5 on each factor. Hours and repetitiveness carry the most weight, because time saved is where the return comes from. Nothing is stored or sent.
Got a winner? Put real numbers on it with the AI ROI calculator: minutes per task today, minutes with AI including review, and tool cost.
How to read your score
- 75+: strong first candidate. Frequent, repeatable work with clean inputs and checkable output. Test it on past examples this month.
- 55 to 74: worth a measured test. Set an end date and a before number before you start.
- 35 to 54: fix the process first. Messy inputs or unclear steps usually need mapping before AI can help. Our process mapping page shows how.
- Under 35: leave it for now. High stakes or little time to save. Revisit later.
AI use cases that often score well in small firms
These come up repeatedly as good first candidates for AI tools for small business owners. Score them against your own work rather than taking them on trust:
- First drafts of proposals, letters and reports from your own notes and templates.
- Document extraction: pulling details from invoices, forms and PDFs into your systems.
- Intake and triage: reading enquiries and routing them to the right person.
- Meeting notes and action lists from calls.
- Internal search and summaries of long documents, policies or past work.
Identifying and scaling AI use cases
Once your first use case proves itself, scaling is the same loop again: write the working version into the procedure, train the people who do the task, measure cost per task for a month, then score the next candidate. Run one at a time in a small firm. Three parallel pilots usually means none gets proper attention.
Keep every live use on a simple register with an owner and a review date. Our AI governance checklist includes an AI use case template for that. If you're unsure whether the firm is ready, start with the AI readiness assessment.
Frequently asked questions
What is an AI use case?
A specific task where AI does or supports part of the work, such as drafting client letters or extracting invoice details. A useful use case names the task, the inputs and who checks the output.
What are the best generative AI use cases for a small business?
Usually repeated drafting, document extraction, intake sorting and meeting notes. Which is best for you depends on how much time each takes and how easily errors are caught.
How do I prioritize AI use cases?
Score each task on time spent, repetitiveness, input quality, ease of checking, stakes and fit with existing tools, then price the top one in cost per finished task.
Should we build or buy for our first AI use case?
Try the AI features in software you already pay for first. Build only when a proven task needs something they can't do.
How many AI use cases should a small firm run at once?
One, until it's measured and written into the procedure. Then the next.
Is my data saved when I use the scorer?
No. The scorer runs in your browser and nothing is stored or sent.