Level 1 · Entry Foundations

Goal: understand what AI management actually is, get a working non-technical grasp of how AI/ML systems get built and deployed, learn how to manage the data science and ML teams who build them, and internalize the ethics and risk basics you need before you touch a real AI project. You'll finish with a real deliverable: an AI project charter with a risk assessment.

Modules

1. What Is AI Management?
2. AI/ML Fundamentals for Managers
3. Understanding AI Project Lifecycles
4. Managing Data Science & ML Teams
5. AI Ethics & Responsible AI Basics
6. Setting AI Project Expectations
7. AI Vendor & Tool Evaluation Basics
8. Communicating AI Concepts to Non-Technical Stakeholders
9. AI Risk Basics
10. Project — AI Project Charter & Risk Assessment

By the end of this level you'll be able to scope an AI project realistically, speak the vocabulary of the data scientists and ML engineers you manage, spot the ethical and risk red flags early, and produce a project charter that sets honest expectations from day one.