Agile Project Management with AI empowers project managers and Agile teams to integrate artificial intelligence into project planning, sprint execution, decision-making, and performance monitoring for faster and more effective project delivery.
Duration 5 days – 35 hrs
Overview
This 5-day program equips participants with practical Agile project management skills (Scrum + Kanban) and shows how to use AI responsibly to accelerate discovery, planning, delivery, and continuous improvement. It blends frameworks, hands-on workshops, and real-world templates so teams can deliver faster with better clarity, flow, and customer focus—aligned with the demands of the 4th Industrial Revolution and Business Agility.
Objectives
- Explain Agile principles, mindset, and how Agile differs from Waterfall.
- Apply Scrum roles, artifacts, and events in end-to-end delivery.
- Apply Kanban practices to improve flow, limit WIP, and manage work visibility.
- Build a product vision and roadmap using UX and customer-centric discovery.
- Create and refine a product backlog (Epics → Features → User Stories + Acceptance Criteria).
- Use AI to support discovery, decomposition, estimation, risk signals, and tech debt visibility.
- Run Sprint Planning, Daily Scrum, Review, and Retrospective with AI-augmented workflows.
- Produce a usable set of delivery outputs: vision canvas, personas, roadmap, backlog, and release plan.
Target Audience
- Project Managers, Product Owners, Business Analysts, Scrum Masters, Team Leads
- Product/Delivery teams transitioning to Agile or scaling Agile practices
- PMO, Transformation, Innovation, and Operations leaders supporting delivery
Prerequisites
- Basic understanding of project delivery concepts (scope, schedule, stakeholders)
- No coding required
- Participants should bring a sample project/use case (preferred) for workshops
Course Outline
DAY 1 — Agile Foundations, Business Agility, and the 4th Industrial Revolution
Module 1: AGILE + Business Context
- Agile in the 4th Industrial Revolution (speed, automation, customer expectations)
- Business Agility: operating model, responsiveness, value delivery
- Agile Overview
- Agile Manifesto and Principles
- Agile vs. Waterfall (when to use what)
- Agile Mindset (learning, adaptability, transparency)
- Overview of Agile Frameworks (Scrum, Kanban, hybrid)
Workshop Outputs:
- Agile vs Waterfall decision guide for your context
- Team working agreements (Agile mindset behaviors)
DAY 2 — AI in Agile + Kanban for Flow
Module 2: AI IN AGILE (Foundations + Transformation)
- AI foundations for Agilists (what AI can/can’t do)
- AI as a catalyst for Agile transformation
- AI-enhanced transformation path (people/process/technology)
- Responsible AI use in delivery (privacy, bias, IP, governance)
Module 3: KANBAN (Flow-Based Delivery)
- Kanban Overview
- Kanban Roles (service delivery focus)
- Kanban Cards (work item definition)
- Kanban Events (replenishment, delivery planning, service review)
- The Flow: lead time, cycle time, throughput
- WIP limits and bottleneck management
- Kanban Boards and Tools (digital + physical)
Workshop Outputs:
- Kanban board design + WIP policy
- Flow metrics baseline + improvement targets
- AI prompts to identify blockers and propose flow improvements
DAY 3 — Scrum Framework + Pre-Sprint Product Discovery (UX + Strategy)
Module 4: SCRUM (Core Framework)
- Scrum Overview
- Scrum Roles (PO, SM, Developers)
- Scrum Events (Planning, Daily, Review, Retro)
- Scrum Artifacts (Product Backlog, Sprint Backlog, Increment)
- Commitments (Product Goal, Sprint Goal, Definition of Done)
Module 5: PRE-SPRINT (Product Vision + Customer Centricity)
- Product Vision and outcomes thinking
- Customer centricity through UX design process
- Understand: Market Research
- Design: User Research
- Product Vision Canvas
- User Personas
- User Journey
- Product Roadmap
AI for Strategic Discovery (Hands-on):
- User persona synthesis (from notes/data)
- Synthetic user interviews (questioning + insight extraction)
- Automated epic decomposition (structured breakdown)
- Predictive market analysis (trend scanning + assumptions mapping)
Workshop Outputs:
- Vision canvas + initial roadmap
- Persona + journey map
- AI-assisted discovery insights + assumptions log
DAY 4 — Product Backlog, Refinement, Estimation, and Release Planning
Module 6: Building the Product Backlog
- Product Backlog Item (PBI)
- Epic / Features / User Stories
- Product Roadmap → User Story Map
- Writing user stories (INVEST)
- From Epic to User Story
- Acceptance criteria (Given–When–Then)
- Test case writing aligned to acceptance criteria
Module 7: Product Backlog Refinement
- Definition of Ready (DoR)
- Definition of Done (DoD)
- Estimation techniques (story points, relative sizing)
- Prioritisation methods (MoSCoW, WSJF-lite, value vs effort)
- Ordering and dependency awareness
Module 8: Release Planning
- Release planning concepts (incremental value releases)
- Risk-based release planning and milestones
- Roadmap alignment and stakeholder expectations
Workshop Outputs:
- Story map + prioritized backlog
- DoR/DoD + acceptance criteria + sample test cases
- Draft release plan
DAY 5 — Implementing Scrum Events + AI-Augmented Delivery + Toolkit
Module 9: IMPLEMENTING SCRUM (Events in Practice)
Sprint Planning
- Presentation, discussion, estimation, commitment
- Sprint Goal and Sprint Backlog creation
- Capacity basics and tasking
AI: Smart Sprint Planning
- Predictive capacity planning
- Dependency mapping
- AI for estimation & risk
- Historical estimation engines (pattern-based guidance)
- Early warning systems (risk signals)
- Tech debt detection (signals, heuristics, recurring defects)
Daily Scrum / Standup
- Did yesterday / Do today / Impediments
- BONUS: 16th Minute (post-standup problem-solving)
- The AI-augmented daily standup (summary, risks, action items)
Mid-Sprint Refinement (Optional Event)
- Keeping backlog healthy during execution
Sprint Review and Demo
- Presentation of increment
- Inspect increment and adapt product backlog
Sprint Retrospective
- Went well / needs improvement / action items
- Next sprint commitments
- READY user stories
- Release plan refinement + release schedule
Module 10: Agile + AI Toolkit (Practical Set-Up)
- AI use cases by activity: discovery, planning, execution, quality, reporting
- Prompt patterns: summarize, decompose, compare options, risk scan, acceptance criteria generator
- Templates and guardrails (data privacy checklist, “human-in-the-loop” rules)
- Team adoption plan (30/60/90 day rollout)
Capstone Outputs (End of Day 5):
- End-to-end Agile delivery pack:
- Product Vision Canvas
- Persona + Journey
- Roadmap + Story Map
- Prioritized Backlog (Epics → Stories)
- DoR/DoD + AC + sample test cases
- Sprint plan + release plan
- Retro action plan
