Spotting AI Use Cases Worth Funding
Turn a long wish list of AI ideas into two or three use cases you can defend with evidence
A taste of a lesson
My boss wants us to use AI for customer service. Is that a good first use case?
Customer service is a department, not a use case, so let's narrow it. Which questions arrive most often? If, say, delivery date questions are a big share, drafting first replies to those is a strong candidate: frequent, text based, and an agent can check a draft in seconds. Refund disputes are a weaker start, because a wrong answer costs money and trust. Your turn: name the single most common question your team answers, and tell me how quickly a person could spot a wrong draft.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Break a role into concrete tasks and spot the ones suited to current AI
- Score ideas on value, feasibility and risk with a simple shared template
- Write a one paragraph use case with an owner, a baseline and a success measure
- Explain why some popular ideas should be dropped or postponed
Lesson plan
- 1 From jobs to tasks List the recurring tasks in one team and note frequency, time and pain for each. Start
- 2 What current AI is actually good at Match tasks to the kinds of work generative AI handles well and badly. Start
- 3 Scoring value, feasibility and risk Apply a three part score to every candidate task and rank the list. Start
- 4 The baseline you will need later Record how the task performs today so any improvement can be shown. Start
- 5 Writing the use case card Turn each top idea into a short card a manager can approve or reject. Start
- 6 Saying no well Explain clearly why some ideas are dropped, parked or need other work first. Start
Try asking
About this tutor
For managers, team leads and business owners who keep hearing that they should be using AI but cannot tell which ideas are worth time and money. You will learn to describe work as tasks rather than jobs, spot the tasks where current AI is genuinely useful, and score ideas on value, feasibility and risk. Each lesson uses examples from your own team: we list the recurring tasks, look at where time and errors go, and test a few ideas against simple questions. By the end you have a short, ranked list of use cases, each with a clear owner, a measure of success and a reason it beats the alternatives, including the option of doing nothing.
Reviews
4.7
3 ratingsSample
- Marta K.Sample
Breaking my team's work into tasks was the part that clicked. We went from 'AI for HR' to two specific ideas, one of which I already got approved.
- Hyun-woo P.Sample
It talked me out of my flashiest idea, politely, and I think it was right. The baseline lesson is something nobody at work had mentioned.
- Daniel O.Sample
Very practical scoring template. I wanted a bit more on how to estimate time savings, but the tutor was honest that you only know after measuring.
About the teacher
Helps leaders choose AI work that pays for itself and drop the projects that do not
9 tutors 353 lessons taught Sample
I teach managers and executives how to make sound decisions about AI without needing to become engineers. My background is in strategy and operations work inside companies, where I spent a lot of time turning vague ambitions into projects with a budget, an owner and a way to tell if they worked. I teach with real decisions: a use case...
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