Designing an AI Pilot With a Clear Verdict
Plan a short AI pilot that ends with a decision, not a vague sense that it went fine
A taste of a lesson
We want to pilot an AI meeting notes tool. How do we know if it worked?
Decide now, before anyone uses it. First, what problem should it fix: time spent writing notes, missed action items, or both? Then pick measures for that problem, say minutes spent on notes per meeting and the share of action items that get an owner. Add a guardrail, for example pausing if confidential HR meetings get recorded. Measure two weeks without the tool, then four weeks with it. Exercise: write one success criterion as a sentence with a number in it.
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
- Frame a pilot around one decision and the assumptions behind it
- Set baseline, success criteria and stop criteria before launch
- Choose a representative pilot group and a sensible duration
- Collect outcome data and structured feedback, not just usage counts
- Reach a written scale, change or stop decision
Lesson plan
- 1 The question a pilot answers Turn a vague trial into a specific question and testable hypothesis. Start
- 2 Baseline and criteria Record current performance and define success and stop rules in advance. Start
- 3 People, scope and duration Choose participants and timing that give a fair, representative result. Start
- 4 Running it safely Prepare data rules, training, support and a fallback before day one. Start
- 5 Collecting evidence Gather outcome numbers and structured feedback throughout the pilot. Start
- 6 Reaching the verdict Apply the decision rule and write up a scale, change or stop decision. Start
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About this tutor
For managers and project leads about to try an AI tool or workflow with a real team. Many pilots drift for months and end with mixed opinions because nobody agreed in advance what success meant. You will learn to frame a pilot as a test of a few explicit assumptions, pick a small representative group, record a baseline, define success and stop criteria before starting, and collect both numbers and structured feedback. We also cover the practical details: consent and data rules, training, support, and what happens to users if the pilot is stopped. You leave with a one page pilot plan and a results template that forces a clear scale, change or stop decision.
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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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