Course Format and Delivery
All sessions are delivered live by an expert instructor in a fully interactive online environment.
20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.
About this course
The future of project management lives at the intersection of human creativity and technological leverage. This immersive, hands-on three-day course prepares experienced project professionals for an AI-empowered future — equipped with actionable strategies and a set of working artifacts you build live, not just notes you take.
Designed for experienced project managers in enterprise environments, the class covers what generative AI can and can't do, how to prompt it well, and where it fits across the project lifecycle. You'll work a single realistic case study from first principles through to a final adoption plan, practicing in a persistent team throughout.
Along the way you'll build a prompt playbook, a prioritized use-case map, planning and lifecycle deliverables, an ethics-and-governance audit, and both a team adoption plan and a personal development roadmap. By the end of Day 3, you'll be ready to apply an AI-enabled project management approach across any framework or toolset.
What you will earn
- An AI for Project Management digital badge from SoftEd, awarded on successful completion.
- 14 PMI® Professional Development Units (PDUs) toward your chosen certification — 12 Ways of Working and 2 Business Acumen.
What you will learn
- Explain what LLM-based generative AI can and can't do — and recognize the "confidence trap" before it costs you.
- Prompt effectively using a structured, iterative approach (the COSTAR framework).
- Identify and prioritize AI use cases with an automation / augmentation / transformation lens and a feasibility-impact matrix.
- Evaluate AI tooling — embedded, standalone, and private LLMs — using the DVF (Desirability, Viability, Feasibility) framework.
- Apply AI across the project lifecycle in both predictive and adaptive environments.
- Use AI for data analysis, validation, summarization, and information retrieval — and verify its output.
- Manage ethics, bias, disclosure, data privacy, and compliance in AI-assisted projects.
- Build a team AI adoption roadmap grounded in change-management practice (the adoption curve, ADKAR, SMART goals, and ROI).
- Leave with a personal 30/90-day development roadmap to sustain momentum after class.
What you will need
- Foundational project management knowledge — through formal training such as our Project Management Fundamentals or Project Management Professional (PMP) courses, or relevant experience in a project management context.
- Access to at least one generative AI tool during class — ChatGPT, Claude, Copilot, or Gemini (any one is sufficient).
This course is great for
- Project Managers
- IT Project Managers
- Program Managers
- Agile Coaches / Scrum Masters
- Product Managers
- Project Directors / Portfolio Managers
- Project Coordinators
- PMO Managers
- Business Analysts
- Operations / Digital Transformation Managers
Topics Covered
Ten sections across three days, built around a single realistic case study and a persistent team. Lecture is kept tight; most of class is spent building.
Day 1 — Foundations
Section 1 — Class Intro & Orientation
What this class is — and what it isn't. An overview of the AI landscape and how generative AI is reshaping project management work.
Section 2 — What LLMs Actually Do
A clear-eyed look at capabilities and limits, and how to get real value out of these tools.
- AI capabilities, limitations, and realistic expectations
- The "confidence trap": why polished output isn't the same as correct output
- Prompting as a two-way dialectic; establishing context
- The COSTAR prompting framework
Practice: Working in teams, build a reusable Team Prompt Playbook.
Section 3 — Where AI Fits in Your Work
A structured way to decide what to point AI at first.
- Automation vs. augmentation vs. transformation
- The feasibility / impact prioritization matrix
- AI use cases across PMBOK, Agile, and hybrid approaches
Practice: Run an AI use-case hunt and produce a prioritized use-case map that carries through the rest of the course.
Day 2 — Hands on Keyboard
Section 4 — The AI-Enhanced PM Toolkit
The landscape of AI tooling and how to evaluate it.
- Embedded AI vs. standalone GenAI — strengths, tradeoffs, and a decision framework
- The internal / private LLM stack and its core components
- Evaluating a major AI decision with the DVF framework (Desirability, Viability, Feasibility)
Practice: Complete a Tool Fit Assessment for your real-world tasks.
Section 5 — AI Across the Project Lifecycle
Applying AI methodology-agnostically across predictive and adaptive work — the AI drafts the first 80%, you add the critical 20%.
- Planning: WBS decomposition and Agile story mapping
- Estimation: parametric models and velocity-based ("yesterday's weather") forecasting
- Execution and monitoring: status reporting, variance analysis, change-impact modeling
Practice: Build real planning and lifecycle deliverables with AI, then assess their quality.
Section 6 — Data, Code & Advanced Applications
The technical-augmentation toolkit — no coding background required.
- Data analysis and a validation-first "trust but verify" discipline
- Summarization and information retrieval across messy, scattered sources
- Formula, query, and script generation — verified on a sample before you trust it
- A primer on BDD and ATDD as collaboration practices
- The three core patterns: Extract, Synthesize, Compare
Practice: Put AI to work on real project data, including a hands-on data-quality validation exercise.
Day 3 — Governance, Roadmap & Future
Section 7 — Governance, Ethics & Compliance
The rules of the road for responsible, defensible AI use.
- Data privacy and security; tiered data handling and a traffic-light classification protocol
- Understanding and mitigating AI bias
- The PMI Code of Ethics applied to AI; disclosure and the "So What" test
- Legal, compliance, intellectual property, and vendor-contract considerations
Practice: Conduct an Ethics & Governance Audit of your own AI use cases.
Section 8 — Building Your Adoption Roadmap
Turning intent into a measurable, change-managed plan.
- Organizational readiness and the "In it / Can contribute / Recipient" posture
- The technology adoption curve and crossing the chasm
- Change management with ADKAR; enablers and blockers
- SMART goals, pilot design, and ROI calculation
Practice: Build a phased team AI adoption roadmap grounded in your real starting position.
Section 9 — Future Trends
A brisk horizon scan and a sustainable learning habit.
- Agentic AI, multimodal models, AI-native PM tools, and autonomous execution
- The evolving PM role and the AI credential landscape
- Resources and habits for staying current
Section 10 — Capstone
Everything comes together. You leave with two finished deliverables: a team AI adoption-plan pitch and an individual personal development roadmap. The session closes with PDU claim guidance and time for open discussion and Q&A.
Facilitated By
Christopher Hanes
Facilitator
Christopher Hanes (Chris) is a Training & Development Leader, Expert Learning Strategist, and Executive & Leadership Coach with 20+ years of experience designing and delivering transformational learning experiences across industries, cultures, and platforms.