AI in Project Management

Building Confidence and Knowledge in Using AI

This one-day course offers a practical grounding in AI covering the essentials of how it works, where it adds value across projects, and how to use it responsibly. A hands-on lab lets participants experiment with real scenarios, so they leave with both the knowledge and the confidence to start applying AI in their projects straight away.

Major Objectives

  • Build confidence to adopt AI responsibly in day-to-day project work
  • Get familiar with data management
  • Understand AI fundamentals and relevance to day to day work
  • Identify practical AI use cases across different project
  • Apply AI tools to improve productivity and efficiency in daily tasks
  • Use effective prompting techniques for various scenarios
  • Support better decision-making using AI-assisted insights
  • Integrate AI into existing project management methodologies and tools

Takeaways and Benefits

  • Clear understanding of how AI applies to project management
  • Ability to identify where AI can add real value across the project lifecycle
  • Practical skills to use AI for everyday PM tasks
  • Improved efficiency and reduced time spent on administrative work
  • Better-informed decision-making supported by AI insights
  • More effective and tailored stakeholder communication
  • Awareness of data, risk, and governance considerations when using AI
  • Confidence to use AI responsibly and pragmatically in projects
  • A clear starting point for integrating AI into daily project management work

Workshop Agenda

Part 1

1. Setting the Scene: Why AI Matters for Project Managers

  • Why AI is different from previous automation waves
  • What is changing in how projects are designed, delivered, governed
  • Where PMs gain leverage vs. where risks increase
  • Typical misconceptions (AI as magic, AI as replacement, AI as IT-only topic)

2. How AI Works – The Essentials (Without Math or Code)

  • What AI actually is (vs. automation, rules engines, analytics)
  • Machine Learning vs. Deep Learning vs. Generative AI
  • Training vs. inference explained simply
  • Why AI behaves probabilistically (and why it hallucinates)
  • Strengths and limitations of AI systems

3. Data: The Fuel of AI (And Why Most Projects Fail Here)

  • What “good data” really means (quality, relevance, bias, lineage)
  • Structured vs. unstructured data in projects
  • The role of data governance, privacy, security, compliance (GDPR, IP)
  • Data ownership in projects and organisations
  • “Garbage in, garbage out” — practical examples

4. Key AI Technologies PMs Should Know (Technology Literacy)

  • LLMs (Large Language Models) & SLMs (Small Language Models)
  • RAG (Retrieval-Augmented Generation)
  • AI agents and workflow automation
  • Predictive models and forecasting
  • Computer vision & speech (briefly, with PM examples)
  • Where AI is embedded in enterprise tools

5. AI Use Cases Across the Project Lifecycle

  • Initiation: business cases, stakeholder analysis, risk identification
  • Planning: scope decomposition, estimates, schedules, scenario analysis
  • Execution: status reporting, issue detection, meeting synthesis
  • Monitoring & control: forecasting, trend analysis, early warnings
  • Closing & learning: lessons learned, knowledge extraction

6. Human + AI: Redefining the Role of the Project Manager

  • What PMs should delegate to AI vs. keep as human judgment
  • Augmented decision-making vs. automated decision-making
  • Critical thinking, sense-making, ethics as PM core skills
  • New expectations for PM leadership in AI-enabled organisations

7. Risks, Ethics, and Governance of AI in Projects

  • AI risks: bias, over-reliance, transparency, explainability
  • Accountability: who is responsible for AI-assisted decisions?
  • AI in regulated environments
  • Guardrails, policies, and ethical frameworks
  • Role of PMs in AI governance

Part 2 – Practical AI Lab

Hands-on AI lab: experiment with real project use cases, compare results, and learn from peers.

This session is a hands-on lab where participants work in small groups to experiment with concrete AI use cases and different AI models applied to project management. Through guided experimentation, comparison, and discussion, participants learn from real examples and from each other. The emphasis is on exploration, shared insights, and practical learning rather than on finding a single correct answer.

Trainer

Florian Ivan is the ideal trainer for “AI in Project Management,” thanks to a rare dual specialisation that perfectly mirrors the course itself.

With over 20 years of project management expertise his command of project management methodologies is second to none. Equally impressive is his technology pedigree: a data analysis degree from Oxford, formative years at a data analytics startup, and a senior Programme Manager role at Microsoft give him deep, firsthand fluency in the tech landscape that underpins modern AI tools.

Florian doesn’t just understand both worlds, he lives them, making him uniquely positioned to show professionals exactly how AI can transform the way projects are managed.

Languages

  • This course in available in English and French

Duration

  • 1 day in classroom
  • Also available as online or virtual delivery

Ready to bring your team on the AI journey?

This training has already helped many professionals take the leap — with confidence and a clear method. Whether you want to organise a session for your team or simply find out more about the course content, we’d love to hear from you.

👉 Get in touch to discuss your needs and find the format that works best for you.

© ROLF