This session, led by Gonzague, a consultant and trainer with over 30 years of experience across service management, DevOps, PMO and generative AI, is a two-part practical workshop. The first part covers the current landscape and the specific pitfalls that cause professionals to use AI badly, including hallucinations, data governance failures and over-reliance on outputs they cannot verify. The second part is a hands-on session using a prompt library built specifically for project managers, applying real prompts to real project scenarios across the delivery lifecycle.
How to structure prompts that produce reliable outputs: the Role, Task, Format framework and when to use more advanced techniques including variables for reusable prompts, chain-of-thought prompting for complex multi-step outputs and chained prompts that build on each other across a workflow
The most important pitfalls to understand before relying on AI in your practice: hallucinations that present incorrect information with full confidence, memory loss in long conversations as the context window fills up, training data bias, and the fundamental principle that AI does not understand what it generates, which means human review is always non-negotiable
The data governance rules that every independent professional and project manager must apply: why personal identifiable information must never enter a public LLM, why financial and proprietary organisational data requires a private or secured platform and how to anonymise data before processing it
Practical AI use cases across the full project lifecycle: drafting project charters and scope definitions in initiation, building risk matrices and WBS structures in planning, consolidating stakeholder inputs and generating status reports in execution, and extracting lessons learned and closing documentation at project close
A live demonstration of a project-specific prompt library, including shortcuts for project charter generation, stakeholder identification, risk analysis, WBS creation, stakeholder sentiment analysis and end-of-project documentation, applied to real anonymised project scenarios
How to build an AI adoption roadmap for your own practice or your clients: starting with an honest assessment of current capability, identifying champions, creating a secure experimentation environment, evaluating use cases with domain experts and integrating AI into existing workflows rather than replacing them wholesale