Finding High-Value Agent Use Cases
A method for finding the work where agents pay off in your organization — and spotting the tempting projects that will disappoint.
Up to now, this course has mostly been about you: your tasks, your agents, your judgment. This module is about everyone else. Getting one person to use agents well is a skill problem. Getting a team or a company to do it is a different problem, made of priorities, habits, policy, money, and shared infrastructure.
It starts with a question most organizations answer badly: where should we use agents first? Pick well and you get a visible win that funds the next project. Pick badly and you spend a quarter on an impressive demo nobody uses, and "AI doesn't work here" becomes the official story. This lesson gives you a method for finding the work where agents pay off, a scorecard for comparing candidates, and a list of the tempting projects that tend to disappoint.
What this module covers
Module 12 walks through bringing agents into an organization in roughly the order you'll face each problem:
- Finding high-value use cases (this lesson): where to point agents first.
- Rolling out agents to a team: pilots, champions, shared setups.
- Training your team: changing how people actually work.
- Writing an AI usage policy: what data goes where and what needs review.
- Measuring ROI: proving it's worth it, honestly.
- Building an internal agent platform: shared infrastructure for teams that build.
- The agent-native team: how roles and culture shift.
You don't need to be a manager to use it. Plenty of agent adoption is driven by one practitioner who proves something works and then gets asked to "do that for the whole team." This module is for that moment too.