Day 3 (17 June 2026) - WorkshopsWednesday, 17 June 2026 - AT (Arabian Time, GMT+03:00)

Module 1 – AI Fundamentals for PM Work

Topics

• What an LLM is/does (practical mental model)
• Safe-use for PM content (confidentiality + redaction)
• Prompt structure: Task + Context + Constraints + Output format + QA
• Prompt precision (“missing details” = wrong outcome)
• Micro-demo: turn messy notes into a clean PM update

Capstone Step1 : Providing Entity Context and Challenges

Exercise: Map your daily work activities, data sources, and stakeholders to frame your AI use case.

Module 2: PM Workflow Frictions + AI Opportunity Mapping

Topics

• Choosing the right PM workflow (high-frequency, high-friction)
• Workflow deconstruction (steps, owners, handoffs)
• Opportunity mapping: where AI supports PM work (drafting, synthesis, QA, coordination)
• Risks/constraints (policy, data, approvals)
• Define “before vs after” success measures

Capstone Step 2: Define the Problem Statement

Exercise: List and prioritize your team’s top three AI-ready pain points as challenge statements.

Module 3: Problem Statement + Use Case Brief

Topics

• Problem statement: specific, measurable, owned
• Inputs/outputs: what the LLM will receive + produce
• Constraints: what can’t be shared, what must be approved
• Acceptance criteria: what “good” looks like
• Quick peer critique loop

Capstone Step: Ideation and Selection

Exercise: Using the 4-dimension canvas, plot current vs. AI-enhanced state for one core process.