Skip the hype and get a clear, practical mental model. Learn what tools like ChatGPT and Claude actually do (and what they cannot), why they sometimes make things up, and where they create real leverage for a team. Understand the language your people use — prompts, models, tokens, hallucinations — so you can lead the conversation instead of nodding along. By the end you can judge where AI fits in your work, set realistic expectations, and speak about it with confidence.

Leaders drown in reports, dashboards, and long threads. Learn to use AI to summarize dense documents, pull the key points and risks, and compare options side by side. Pressure-test a decision by asking AI to argue the other side, surface what you might be missing, and translate raw numbers into a plain-language story for your team. You will also learn to question AI output and verify the facts that matter. By the end you make faster, better-informed calls without reading every page yourself.

So much of leading is writing: updates, announcements, feedback, tough messages. Learn to use AI as a first-draft partner that matches your tone and audience — turning rough notes into a clear team update, softening or sharpening a message, and preparing talking points for a hard conversation. Practice giving the AI the right context so the draft sounds like you, then edit with judgment. By the end you communicate more clearly and consistently, and reclaim hours every week.

AI pays off most when it removes drag from how your team works. Learn to spot the repetitive tasks worth delegating to AI — meeting prep and notes, status summaries, drafting standard documents and checklists — and how to set them up so quality stays high. See how to turn a messy meeting into clear notes and owners, build reusable prompt templates your team can share, and keep a human in the loop. By the end you can redesign everyday workflows so people spend time on work that matters.

Using AI yourself is one thing; leading a team through it is another. Learn to set a simple, clear AI policy: what is okay to put into a tool, how to protect sensitive and customer data, and when a human must review the output. Understand bias, accuracy, and accountability in plain terms, and how to upskill your team without fear or hype. Plan a small, safe first rollout and measure what works. By the end you can champion AI in a way that is responsible, trusted, and actually adopted.