AI BootCamps (7 Courses)
Career Oriented
Course Category Career Oriented
Course Category 
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Learners
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Enterprises
Generative AI and Agentic AI for Project Management is a 12-hour hands-on course where you build and deploy 6 real AI automation tools for your projects. Using Python, OpenAI API, n8n, and LangGraph, you will automate charter generation, WBS planning, EVM reporting, meeting minutes, scope change control, and weekly status reporting. Every session is a live build, write the code, test it, take it back to your project on day one.
The full PM lifecycle covered with Gen AI and Agentic AI in every session
6 Python scripts calling OpenAI API for real PM tasks
6 n8n workflows running Gen AI use cases visually
6 n8n agents that automate PM tasks — no prompting needed
6 LangGraph agents in Python for full code control
Agents connected to Drive, Jira, Slack, Gmail, Sheets. $10 of OpenAI credit covers the full course
Learning Objective:
Bridge Python basics to LLM development. Every participant sets up their OpenAI API connection in Python and n8n and makes a live GPT-4o call before M1 begins.
Topics and Subtopics:
OpenAI API setup
First API call in Python
n8n setup and first workflow
Prompt structure for PM use cases
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Every participant has a working OpenAI API call in Python and a 3-node n8n workflow — ready to build from minute one. 100% of setup confusion eliminated before use cases start.
Learning Objective:
A two-paragraph project brief becomes a complete charter, stakeholder map, and initial risk register in minutes. The agent fires automatically when a new brief lands in Google Drive.
Topics and Subtopics:
Gen AI — Charter generation in Python
Gen AI — Charter workflow in n8n
Agentic AI — Initiation agent in n8n
Agentic AI — LangGraph agent in Python
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Working Python charter generator + n8n agent that triggers on Google Drive upload and delivers the full initiation pack to the PM’s inbox. Time saved: 4 hrs → 10 min.
Learning Objective:
A scope document becomes a complete WBS to level 3 with effort estimates and scheduling risks. The agent recalculates automatically when scope changes.
Topics and Subtopics:
Gen AI — WBS generation in Python
Gen AI — WBS workflow in n8n
Agentic AI — Planning agent in n8n
Agentic AI — LangGraph agent in Python
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Working Python WBS generator + n8n agent that detects scope changes in Google Sheets and recalculates the WBS automatically. Time saved: 6 hrs → 15 min.
Learning Objective:
Raw actuals become a complete EVM analysis — CPI, SPI, EAC, TCPI, RAG status, and a plain-English sponsor narrative — in under 2 minutes. The agent runs every Monday and emails the report automatically.
Topics and Subtopics:
Gen AI — EVM analysis in Python
Gen AI — EVM workflow in n8n
Agentic AI — EVM agent in n8n
Agentic AI — LangGraph agent in Python
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Working Python EVM analyser + n8n agent that reads actuals from Sheets every Monday at 8am and emails the sponsor narrative — zero PM involvement. Time saved: 3 hrs → 2 min.
Learning Objective:
A meeting transcript becomes structured minutes, decisions, and action items with owners and due dates in under 60 seconds. The agent processes every meeting the moment it ends — no PM involvement.
Topics and Subtopics:
Gen AI — Minutes extraction in Python
Gen AI — Minutes workflow in n8n
Agentic AI — Meeting agent in n8n
Agentic AI — LangGraph agent in Python
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Working Python minutes extractor + n8n agent connected to Otter.ai that fires when a meeting ends, creates Jira tasks per action item, and emails minutes to all attendees. Time saved: 45 min → 60 sec per meeting.
Learning Objective:
A change request becomes a complete impact analysis across schedule, cost, risk, and quality in 5 minutes. The agent monitors the project inbox and flags scope creep before the PM notices.
Topics and Subtopics:
Gen AI — Change impact analysis in Python
Gen AI — Change control workflow in n8n
Agentic AI — Scope monitoring agent in n8n
Agentic AI — LangGraph agent in Python
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Working Python change impact analyser + n8n agent that monitors Gmail, classifies every incoming message as in-scope or out-of-scope, and auto-drafts the change request for out-of-scope items. Time saved: 2 hrs → 5 min.
Learning Objective:
Raw weekly metrics become a complete leadership status narrative — RAG status, key message, top concerns, decisions needed — in 2 minutes. The agent runs every Friday at 4pm and emails the full report to all stakeholders automatically.
Topics and Subtopics:
Gen AI — Status report in Python
Gen AI — Status workflow in n8n
Agentic AI — Reporting agent in n8n
Agentic AI — LangGraph agent in Python
Gen AI — You prompt it
Python + n8n workflows
Agentic AI — Runs automatically
n8n agents + Python LangGraph
Outcome:
Working Python status generator + n8n agent that runs every Friday, reads metrics from Sheets and open issues from Jira, generates the report, and emails the stakeholder list automatically. Time saved: 3 hrs → 2 min. Sunday evenings back in your calendar.
To fast-track your career and achieve
There is no exam for this workshop.
Agilefever’s Gen AI for Project Managers training transformed the way I manage projects. AI manages my risk tracking and stakeholder communications; I’m finally ahead of the game.
The questions, tools, and case studies all felt tailor-made for a project manager like myself. Agilefever has helped me manage smoother sprints and more effective meetings.
I was hesitant about AI. Now I can’t work without it. AgileFever made Gen AI simple, useful, and surprisingly fun to learn.
No — and the evidence is clear on this. As AI handles routine operational tasks like status reporting, EVM calculations, and meeting minutes, demand for PMs who can lead, govern, and make complex decisions is growing, not shrinking. PMP-certified professionals already earn a median of $136,000 annually, with top roles above $180,000 — and that premium is increasing for those who combine certification with AI proficiency. This course positions you as the PM who uses AI as a force multiplier, not one who gets replaced by someone who does.
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.
No coding, no technical setup, and no specific tool experience required. The course uses ChatGPT and Claude — both accessible through a browser with a free account. If you can write an email and use a browser, you have everything you need. The AI Foundations course (4 hours) is the only prerequisite.
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.
The course awards 24 PDUs and 24 SEUs upon completion. PDUs are categorised under Education — Technical, which counts toward PMP and CAPM renewal with PMI. SEUs count toward CSM and other Scrum Alliance credentials, as well as SAFe certification maintenance. PMI’s own CPMAI certification (Certified Professional in Managing AI) also accepts relevant AI training toward its requirements. Always verify the current requirements with your specific certification body, but for most PMI and Scrum credentials this course qualifies directly and covers a significant portion of a full renewal cycle.
The course uses ChatGPT (free tier is sufficient for most exercises), Claude (free tier available), and Otter.ai for meeting transcription demonstrations. No paid subscriptions are required to participate fully. The prompt library and techniques taught work across any large language model, so if your organisation uses Microsoft Copilot, Google Gemini, or another tool, everything transfers directly.
Real deliverables, built during the course using a realistic project scenario. The six projects are: an AI-generated charter, stakeholder map, and risk register; a WBS, schedule baseline, and EVM budget projection; a vendor evaluation matrix and SOW first draft; a change request impact analysis and stakeholder communication package; a leadership dashboard and AI-generated status report; and a lessons learned document and personal 90-day AI adoption roadmap. These are the actual documents a PM produces on a project — the course just shows you how AI helps you produce them in a fraction of the time.
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 tips and tool demos. This course builds a complete, structured methodology across the full PM lifecycle — with both Gen AI and Agentic AI covered in every phase, 200+ tested prompts organised by PM activity, 6 real deliverables you complete during the course, and live instruction where you can ask questions in context. The difference is between knowing AI exists and knowing exactly how to use it for every type of PM work you do every week.
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.