In this session, explore how AI tools, including GPT models, can boost Agile team efficiency by automating tasks like sprint transcriptions, backlog prioritization, and sentiment analysis. Tailored for Agile project managers, this session reveals how AI-driven automation enhances productivity, transparency, and team collaboration. Beginning with AI's role in Agile workflows, the presenter will showcase real-world applications and live demos, including AI-generated summaries and automated UML diagrams. Attendees will gain insights into using AI for effective decision-making, team dynamics, and streamlined communication. By session’s end, participants will have actionable strategies for integrating AI into Agile practices, enabling them to reduce repetitive tasks and optimize team workflows.

1. ProEdit. (2020, August 26). [TECHNOLOGY] History of the mouse. ProEdit.
https://proedit.com/history-of-the-mouse/
Apr 27th, 1981
Xerox's "Star" featured the first
commercial mouse.

2. April 10, 2025 - Dubai, UAE
Boosting Agile Teams with AI:
Automate Workflows &
Enhance Collaboration
Tao Chun Liu
Senior Business Consultant & Practitioner

3. SUPPORTERS
GOLD PARTNER

4. Takeaways
1️⃣ AI Automates Repetitive Agile Tasks – AI tools can handle sprint
transcriptions, backlog prioritization, and UML diagram generation, reducing manual
effort and freeing up time for strategic decision-making.
2️⃣ AI Enhances Team Collaboration & Transparency – AI-powered sentiment
analysis and real-time meeting insights improve communication, helping Agile teams
identify blockers and optimize workflows more effectively.

5. Tao Chun Liu
Senior Business Consultant and Practitioner
Certificates
Career
Highlights
• 15+ PM Experience
• 2,500+ sprints
• 2M/1CM
• 4P/1SM/1PO
#AI #Agile
(1) PMP®
(2) CBAP®
(3) NPDP
(4) PHRi
(5) DASSM®
(6) AHPP
(7) PSPOII
https://www.linkedin.com/in/taochunliu/
unparade@hotmail.com
AI Research
• PMI
• IPM
• University of Northampton
• Universiti Malaysia Pahang

6. Since 2022~
50+
Gen AI
Workshops
12
ChatGPT
Personal
Assistant
Trainings
40+
Gen AI
Corporate
Training
30+
International
Events

7. Why Gen AI is
such a big thing anyway?

8. In the Generative AI Era…
1. Fast iteration
2. Thousands of Project initiatives
3. Rapid Change Requirement
4. Flexibility
5. Collaborative leadership
6. Respect different ways of workings

9. AI Trend AI
RPA/
AIOT
Machine Learning
Deep Learning
Gen AI
AI Agent?

10. Old AI VS Gen AI?
Purpose
How It Works
Examples
Applications
Old AI Gen AI
Forecasts future events based on
past data.
Creates new content like
images, text, or music.
Analyzes historical data to identify
trends and make predictions.
(Quantitative Analysis)
Learns patterns from existing
data to produce original outputs.
Predicting weather changes, stock
market trends, and customer
behavior.
Crafting unique artwork, writing
stories, and composing music.
Business forecasting, healthcare
diagnostics, financial planning.
Art and design, content creation,
Agile entertainment.
Hassan, S. (2025, January 21). Generative AI versus Predictive AI. MarkTechPost.
https://www.marktechpost.com/2025/01/20/generative-ai-versus-predictive-ai/

11. The Evolution of Gen AI: 2023-2025

12. Different Stages of AI
2. Pre-Training
Question
Prompt Knowledge
Question Question
1.Prompt
Engineering
3. Retrieval
Augmented
Generation(RAG)
Vector Database
Amazon Web Services (AWS). (n.d.). Leading with AI , crafting Future-Proof enterprise strategies [Presentation].
2024 AWS 生成式 AI 創新產業應用日, Taipei, Taiwan. https://aws.amazon.com/tw/events/2024-genai-day/
4. AI Agent
Tools/APP
Question

13. How AI Agent Works?
Amazon Web Services (AWS). (n.d.). Leading with AI , crafting Future-Proof enterprise strategies [Presentation]. 2024
AWS 生成式 AI 創新產業應用日, Taipei, Taiwan.https://aws.amazon.com/tw/events/2024-genai-day/
Normal
Chatbot
AI Agent
Input
Output
Travel Agents
(RAGed)
Which travel
agents can
sell me a
ticket?
Guidance for
travel agents
I Want a
bubble tea
What time is boarding
for my Emirates Airlines
flight tonight?
Emirates
Airlines
Database
Search

14. The Most Popular Programming Languages
Winter, J. (2025, February 21). The most popular programming languages by decade — Jeff Winter. Jeff Winter.
https://www.jeffwinterinsights.com/insights/the-most-popular-programming-languages-by-decade

15. Why AI in Agile?

16. In the AI Era
Run, don’t walk. Remember, either
you’re running for food, or you are
running from becoming food. And
oftentimes, you can’t tell which.
Either way, run.
Jensen Huang, CEO of Nvidia

17. Why AI in Agile?

18. It is NOT Fast enough

19. Product Backlog
Product Backlog
Refinement
Product
Goal
Sprint Backlog
Sprint
Planning
Daily Scrum
Sprint Review
Sprint Retro
Company Vision
Deliver
Sprint Goal
Scrum Framework
1
2
3
4
1. Enhancing Sprint
Review Effectiveness?
2. Evaluating Quality in
Daily Scrum?
3. Ensuring Product
Backlog Relevance?
4. Improving User Story
Quality?

20. Case Studies!

21. Case Study I-Challenges in Review Meeting
1️⃣ Too Much Feedback, Hard to Sort
•Stakeholders mix critical issues,
enhancements, and minor suggestions.
•Important client requests risk being
overlooked.
2️⃣ Difficult Prioritization
•What to implement now vs. later isn’t
always clear.
3️⃣ Slow Manual Backlog Updates
•Product Owners spend too much time
sorting and categorizing.
•Planning delays impact sprint efficiency.

22. Case Study I-Challenges in Review Meeting

23. Case Study I-Challenges in Review Meeting

25. Case Study I-
Meeting Minutes

26. Case Study I-
Backlog
Refinement

27. Case Study II- Sentiment Analysis for Team Health
1️⃣ Unspoken Issues
Stress, conflicts, and burnout often go
unnoticed.
2️⃣ Slow Response to Morale
Drops
Problems escalate before action is
taken.
3️⃣ Subjective Interpretation
Leaders rely on gut feelings instead of
data.

28. Case Study II- Sentiment Analysis for Team Health
Front End Programmer: John (Team B)
Situation:
• During Daily Scrum, AI shows John is consistently STRESS
and UNEASE.
• The number of bugs has increased following the release of his
code.
Issues:
• John is currently going through a divorce.
• He is struggling to manage his work responsibilities.
Solution:
• Provide John with 10 days of paid leave.
• Request support from Front End Programmers in Teams A, C,
and D.

29. Case Study II- Sentiment Analysis for Team Health
Energy
Stress
Mental Effort
Confidence
Excitement
Distress

30. Case Study II- Sentiment Analysis for Team Health

31. Case Study III- Customer Feedback Is Outdated
1️⃣ Misaligned Features & Market
Needs
The product lacks essential updates,
making it less competitive.
2️⃣ Delayed Customer Insights
Decisions rely on outdated feedback,
leading to irrelevant improvements.
3️⃣ Increased Customer Frustration
Users expect fixes and enhancements,
but their concerns remain unaddressed.

32. Case Study III

33. Case Study III- Customer Feedback Is Outdated

34. Case Study III- Customer Feedback Is Outdated

35. Case Study III- Customer Feedback Is Outdated

36. Case Study III- Customer Feedback Is Outdated

37. Case Study III

38. Case Study IV- User Stories & Product Specs
1️⃣ Unclear Requirements
Leads to misunderstandings and scope
creep.
2️⃣ Gap Between Stories &
Specs
Hard to translate into technical details.
3️⃣ Misaligned Expectations
Balancing user needs and business goals
is tricky.

39. Case Study IV- User Stories & Product Specs
User Story I User Story II

40. Case Study IV- User Stories & Product Specs

41. Case Study IV- User Stories & Product Specs

42. Case Study IV- User Stories & Product Specs
User Story I User Story II

43. Conclusion & QA

44. Tools Summarized
Case Tools
Case Study I-
Challenges in Review Meeting
GPT API, Zapier, Trello,
Mermaid
Case Study II-
Sentiment Analysis for Team
Health
Emotion Logic
Case Study III-
Customer Feedback Is
Outdated
GPT API, Python
Case Study IV-
User Stories & Product Specs
GPT API, Trello, Zapier
Tao Chun Liu, PMP®, PSPO II ®
Senior Business Consultant and
Practitioner