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