AI+ Agile Project Management Fundamentals™

Course code: AP110002

Transform Project Delivery with AI+ Agile Project Management Fundamentals

Smart Sprint Planning: Discover how AI-powered insights improve backlog prioritization, sprint forecasting, and resource allocation for predictable delivery.
Adaptive Workflow Optimization: Learn to use AI tools to track progress, identify bottlenecks, and automate routine tasks to keep projects moving smoothly.
Data-Driven Decision Making: Gain the ability to analyze real-time project metrics, risks, and team performance with AI support for faster, smarter decisions.
Enhanced Team Collaboration: Master intelligent communication and reporting tools that improve stakeholder alignment, transparency, and cross-functional teamwork.
Predictive Risk Management: Use AI to anticipate delays, budget overruns, and scope creep, enabling proactive planning and effective mitigation strategies.

Price of the certification exam is included in the price of the course.

175 EUR

212 EUR including VAT

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Prerequisites
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Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 175 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		Self-paced	8 hours	en	175 EUR	Register
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Course description

Rising Demand for Agile AI Leaders:

Organizations adopting AI need project managers who can deliver faster, adaptive, and tech-driven outcomes.

Faster Project Delivery:

Agile frameworks help teams iterate quickly, reduce delays, and consistently meet evolving stakeholder expectations.

Smarter Decision-Making with AI Insights:

AI-powered analytics improve planning, forecasting, and risk assessment for more accurate project execution.

Improved Collaboration and Transparency:

Agile practices foster cross-functional teamwork, clear communication, and real-time progress visibility.

Reduced Costs and Risks:

Continuous feedback, automation, and incremental releases minimize rework, optimize budgets, and control project risks.

ChatGPT
Google Gemini
Microsoft Copilot
Trello AI
Jira Free Tier
ClickUp
Notion AI
GitHub Copilot
Google Sheets with AI Add-ons
Power BI
Tableau Public
Python
Pandas
Scikit-learn
TensorFlow
AutoML Tools
Miro AI
Zapier
Slack AI Integrations
Burndown & Sprint Analytics Dashboards

Target group

Project Managers: Professionals looking to integrate AI tools into Agile workflows to improve planning, forecasting, and delivery outcomes.

Scrum Masters: Individuals aim to enhance sprint efficiency, automate reporting, and enable smarter decision-making using AI-driven insights.

Product Owners: Professionals interested in leveraging AI for better backlog prioritization, customer insights, and value-based product development.

Business Analysts: Those seeking to use AI to analyze requirements, predict risks, and optimize stakeholder communication across projects.

Agile Team Members: Developers, testers, and designers wanting to streamline collaboration, automate repetitive tasks, and boost productivity with AI support.

Course structure

Module 1: Fundamentals of AI in Agile Project Management

1.1 Introduction to AI Concepts for Project Managers
1.2 Synergy Between AI and Agile Methodologies
1.3 Case Study: AI-Enhanced Sprint Planning
1.4 Hands-On Session: AI Tools Walkthrough for Sprint Planning and Backlog Grooming

Module 2: Data Literacy for Agile Project Managers

2.1 Understanding Project Data Types and Sources
2.2 Data-Driven Decision Making in Agile
2.3 Case Study: Data-Led Sprint Retrospectives
2.4 Hands-On Simulation Exercise: AI-Driven Sprint Prediction and Metrics Analysis

Module 3: AI for Resource and Team Management

3.1 Predictive Resource Allocation
3.2 AI-Driven Agile Metrics and Performance Tracking
3.3 Use Cases: Smart Scheduling and Workload Balancing
3.4 Hands-On Session: Managing Team Capacity and Task Distribution Using AI Dashboards

Module 4: Predictive Analytics in Agile Project Management

4.1 Foundations of Predictive Modelling
4.2 Forecasting Delays and Resource Shortages
4.3 Case Studies: Early Risk Detection in Agile Projects
4.4 Hands-On Simulation Exercise: Resource Shortage and Timeline Forecasting

Module 5: AI in Project Monitoring and Reporting

5.1 Real-Time Monitoring with AI
5.2 Intelligent Reporting and Stakeholder Communication
5.3 Use Cases: Automated Status Updates and Performance Reviews
5.4 Hands-On Session: Creating AI-Powered Reports and Visual Dashboards

Module 6: Ethics, Bias, and Regulation in AI for Project Management

6.1 Ethical AI in Decision-Making
6.2 Bias and Risk in Predictive Models
6.3 Regulatory and Compliance Considerations
6.4 Hands-On Exercise: Evaluating AI Outputs for Fairness and Responsible Use

Module 7: Evaluating and Implementing AI Tools in Agile Projects

7.1 Selecting the Right AI Solutions
7.2 Change Management and Stakeholder Adoption
7.3 Case Study: AI-Automated Reporting and Risk Forecasting in Consulting Projects
7.4 Hands-On Simulation Exercise: Tool Evaluation and Vendor Comparison
7.5 Hands-On Exercise: Measuring AI Effectiveness with Project Analytics Platforms

Module 8: Future Trends and AI in Agile Project Management

8.1 Autonomous and Self-Optimising Projects
8.2 AI for Remote and Distributed Agile Teams
8.3 Case Studies Inspired by Industry Trends
8.4 Hands-On Simulation Exercise: Designing an AI-Augmented Agile Workflow

Prerequisites

Basic understanding of project lifecycle and management principles alongside the awareness of agile methodologies like Scrum and Kanban. Introductory knowledge of artificial intelligence and its applications, and ability to address challenges in dynamic environments.