Agile Project Management with AI

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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

 

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        Target Audience  
  • Warehouse supervisors, team leaders, and managers.
  • Inventory controllers and stock control personnel.
  • Logistics and distribution supervisors.
  • Internal auditors and operational compliance personnel.
  • Supply chain and operations professionals.
  • Personnel responsible for reviewing warehouse controls and performance.
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Course Outline

  Day 1: Warehouse Processes, Layout, and Storage Controls Module 1: Warehouse Operations and Process Controls  
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  • End-to-end flow: receiving, put-away, storage, replenishment, picking, packing, and dispatch.
  • Physical stock movement and corresponding system transactions.
  • Responsibilities, authorization, and segregation of duties.
  • Common operational weaknesses and audit evidence.
  Module 2: Warehouse Layout and Storage Systems  
  • Functional zones, movement paths, and space utilization.
  • Receiving, storage, picking, staging, and dispatch areas.
  • Storage options: floor stacking, shelving, pallet racking, and bin storage.
  • Fixed and dynamic location systems.
  • Product slotting based on movement, size, handling needs, and compatibility.
  • Layout bottlenecks and their effects on productivity and inventory control.
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  • Put-away rules and location confirmation.
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  • Timely recording of receipts and stock movements.
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  • Storage discipline and location integrity.
  • FIFO and FEFO principles, applications, and limitations.
  • Batch, lot, and expiry-date management.
  • Controls for slow-moving, obsolete, damaged, and expired stock.
  • Replenishment of picking locations.
  • Supervision and review of stock rotation compliance.
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  • Discrete, batch, zone, and wave picking.
  • Selecting picking strategies for different order profiles.
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  • Picking verification and error prevention.
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  • Item master data, units of measure, and location records.
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  • Common causes of physical and system stock mismatches.
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  • Inventory reconciliation and supporting documentation.
  • Measuring inventory accuracy and interpreting discrepancies.
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  • Cycle counting versus full physical inventory.
  • ABC classification and risk-based count priorities.
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  • Count preparation, blind counting, and movement controls.
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  Module 8: Shrinkage Reduction and Inventory Protection  
  • Sources of shrinkage: loss, damage, theft, misplacement, and recording errors.
  • Access controls and accountability for stock movements.
  • Controls for high-value and sensitive inventory.
  • Returns, write-offs, scrap, and disposal controls.
  • Exception reporting and unusual transaction patterns.
  • Investigating recurring losses and unexplained adjustments.
  • Preventive and detective controls for shrinkage reduction.
    Day 3: Performance Management, Process Review, and Improvement Module 9: Warehouse Productivity and Inventory KPIs  
  • Receiving productivity and dock-to-stock time.
  • Put-away productivity and replenishment performance.
  • Picking productivity and order accuracy.
  • Inventory accuracy and cycle count variance.
  • Space utilization and storage capacity.
  • Shrinkage, damage, and expiry indicators.
  • Defining KPI formulas, data sources, and reporting boundaries.
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  Module 11: Warehouse Improvement Planning  
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  Module 12: Capstone — Warehouse Operations and Inventory Improvement Plan  
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