Generative AI and Agentic AI for Project & Program Management

Gen AI is what you use when you prompt a tool to help you do something — drafting a charter, analysing risk, writing a stakeholder update. Agentic AI is what runs in the background doing those things continuously without you prompting it each time — a risk monitoring agent that reads every project communication, a reporting agent that generates the weekly status pack automatically. Both are real, both are available now, and both are covered in every module so you understand where to use each one in your actual work.

Yes — basic Python is required, which is why Python for AI (6 hours) is a prerequisite. You should be comfortable with functions, loops, and Pandas going in; this course builds directly on that to write real automation scripts and deploy AI agents. It’s not a no-code course — if you’re looking for a prompting-only introduction to AI for PMs, this is the next step up from that.

Yes — the course is specifically built for PMs, Scrum Masters, and Program Managers. Scrum Masters will find direct application in the modules on sprint execution, retrospectives, standup management, stakeholder communication, and risk identification from team signals. The agentic workflows covered — including meeting agents that auto-generate action items and risk agents that monitor team sentiment — are particularly relevant to the Scrum Master role.

You’ll work with the OpenAI API (~$10 of credit covers the full course), LangGraph, and n8n — all free or low-cost to set up. No paid subscriptions are required. Everything you build connects to tools you likely already use: Google Drive, Sheets, Gmail, Jira, and Slack.

Real, working tools you build and take back to your job. Across the course you’ll build: a charter generator, a WBS planner, a RAG-based document analyzer, a meeting-minutes extraction agent, a change-impact analysis tool, and a weekly status report generator — each one a Python script or n8n workflow connected to your actual project data, not a one-off classroom exercise.

With the right prompting — yes, consistently. The course teaches you exactly how to structure prompts so AI produces outputs that require minimal editing before they are presentation-ready. You remain accountable for the content and make the final call on every deliverable — AI drafts, you review and refine. The course includes specific modules on EVM narrative generation, leadership status reports, and stakeholder communications, with real examples of the before-and-after quality difference that structured prompting produces.

Free content shows you individual prompts. This course teaches you to write the actual Python and n8n automations behind them — 6 working agents connected to your real tools, live instruction where you can debug in context, and a repeatable methodology across the full PM lifecycle. The difference is between copying a prompt and owning the automation.

Yes. The course teaches AI techniques and prompt approaches that are tool-agnostic — they work whether your team uses Jira, Asana, MS Project, ClickUp, or any other PM platform. You will also learn how to use AI alongside your existing tools: extracting data from them, generating inputs for them, and in some cases using AI to interpret and analyse data from them. The goal is to make AI work within your current environment, not to replace it.