We will have an HR case study competition in Winnipeg, Manitoba, in
early November 2025. Participants are from University of Winnipeg and
Red River College. The topic is about the impact of AI on HR
management and how to solve HR management issues with AI. Our team
members are not from HR or technical major, but they have a business
management background. The judge team is made up of local company
managers and professionals. What kind of preparation do you suggest
our 4-person team get prepared? Including but not limited to cases
preparation, acknowledgement, hot spots, etc.
A. HR Management Domains Affected by AI
Get familiar with how AI transforms these key HR functions:
Recruitment & Selection – AI résumé screening, chatbots for interviews,
predictive hiring.
Employee Engagement – Sentiment analysis from surveys or emails, AIbased feedback tools.
Performance Management – Predictive analytics for turnover, automated
KPIs.
Learning & Development – Personalized AI learning paths (e.g., Coursera
AI recommendations).
Diversity, Equity & Inclusion – AI bias detection in hiring algorithms.
HR Analytics – Forecasting workforce needs, attrition risk modeling.
B. AI Tools & Trends (2024–2025 hot spots)
Familiarize with current AI technologies used in HR, such as:
Chatbots & virtual assistants (for candidate communication)
Natural Language Processing (NLP) (for résumé screening, sentiment
analysis)
Predictive analytics tools (for turnover prediction)
Generative AI (for crafting job descriptions, training materials, or employee
surveys)
AI ethics and bias control — very important for HR cases
C. Ethical and Legal Context (especially in Canada)
AI transparency – Explainable AI in hiring decisions
Data privacy – Protection of employee data (PIPEDA compliance)
Bias mitigation – Avoid discriminatory outcomes in AI algorithms
� 3. Develop a Problem-Solving Framework
Case competitions love structured thinking. Prepare a team framework to use for any
HR-AI problem:
Step 1: Problem Identification
Define the HR issue clearly (e.g., high turnover, poor engagement, hiring
inefficiency).
Step 2: Root Cause Analysis
Use tools like Fishbone diagram or 5 Whys to identify underlying issues.
Step 3: AI Application Design
Choose AI solution types (predictive analytics, automation, chatbots, NLP,
etc.).
Explain how it addresses the problem and why it’s suitable.
Step 4: Implementation Plan
People: Who will manage it?
Process: How to integrate into HR workflow?
Technology: Build, buy, or partner?
Cost–Benefit Analysis: Simple ROI model (time saved, accuracy improved,
cost reduced).
Step 5: Risk and Ethics Check
Consider potential biases, data security, and employee acceptance.
Step 6: KPIs & Evaluation
Define measurable outcomes (e.g., recruitment time ↓ 40%, turnover ↓ 10%).
� 4. Team Role Preparation
Divide your team strategically:
Role
Focus
Skills to Develop
Team Lead / Presenter
Overall flow, coordination, Executive
storytelling
communication
Analyst / Strategist
Case breakdown, frameworks, Structured problemkey insights
solving
Researcher / AI
Explorer
Find AI use cases and tools
Applied AI
awareness
Designer / Visual
Communicator
Slide design, charts,
storytelling visuals
PowerPoint, Canva,
clarity
Have each person practice 2–3 mock presentations for timing and confidence.
� 5. Research & Acknowledge Current Hot Topics (as of late 2025)
Keep your examples fresh and local/global relevant. Some hot topics likely to appear:
AI-driven employee wellness tools (mental health monitoring with consent)
Bias in AI recruitment tools – fairness and accountability
HR automation vs human empathy balance
AI for hybrid work optimization
Upskilling for an AI-driven workforce
Ethical AI governance in organizations
Tip: Review recent HR and AI publications (e.g., SHRM, Deloitte Insights, McKinsey
AI & Talent Reports).
� 6. Practice with Sample Case Scenarios
Try 2–3 mock cases before competition, such as:
AI in Hiring: A company faces bias claims due to automated résumé
screening. How to fix it ethically?
Employee Retention: Use predictive analytics to identify at-risk employees.
AI Upskilling: Design an AI-powered learning program for employees in a
mid-sized firm.
Have teammates present a solution and critique each other using the judging criteria.
� 7. Presentation and Delivery Tips
Keep slides clean: 8–10 slides max.
Use a 3-act narrative: Problem → AI Solution → Impact.
Show local awareness (e.g., relevance to Manitoba’s mid-sized companies).
Use data visuals, not text-heavy slides.
Practice 2-minute introductions for each team member.
End with a strong, confident conclusion: “This solution bridges technology
and human value.”
� 8. Optional but Powerful Add-Ons
Mention emerging Canadian AI regulation (Artificial Intelligence and Data
Act – AIDA).
Highlight ethical AI frameworks (e.g., “Responsible AI by Design”).
Include a brief ROI projection (financial and human impact).
Use at least one real-world example (like IBM Watson HR or HireVue).
Would you like me to help your team design a prep plan calendar (2–
3 weeks timeline) showing what to do each week before the competition
— including topics to research, practice sessions, and presentation
building steps?
It would make your preparation more organized.
WEEK 1 – Foundation & Knowledge Building (Oct 20 – 26)
Goal: Build solid understanding of HR + AI concepts and recent trends.
� Learning Focus
HR fundamentals: Recruitment, performance, engagement, learning,
retention.
AI basics in HR: Predictive analytics, chatbots, résumé screening, sentiment
analysis, generative AI.
Ethics & governance: AI bias, privacy (PIPEDA, AIDA Act), transparency
in HR tech.
� Team Activities
Each teammate picks one HR function and researches 1–2 AI applications in
that area.
Meet at week’s end to share findings (20 min each).
Create a shared Google Drive folder or Notion board for notes, articles, and
visuals.
� Suggested Resources
Deloitte “AI in HR 2025” report
SHRM: AI and the Future of HR
McKinsey: AI and the Workforce Transformation
Harvard Business Review articles on AI + HR ethics
WEEK 2 – Case Simulation & Strategy Building (Oct 27 – Nov 2)
Goal: Learn structured case solving, team coordination, and storytelling.
� Practice Cases
Do 2 mock cases:
Recruitment AI Bias: A company faces discrimination complaints.
Employee Retention: Design an AI-driven turnover-prediction strategy.
� Use a Framework
Apply your standard approach each time:
Define problem
Analyze causes
Design AI solution
Implementation roadmap
Risk & ethics check
Measurable outcomes
� Team Division (start fixing roles)
Role
Responsibility
Leader
Coordinate, manage time, open/close presentation
Analyst
Problem framing, metrics, logic flow
AI Researcher Provide practical tools, examples
Designer
Create visuals, storytelling slides
� Presentation Rehearsal #1
Record your mock pitch (10–12 minutes).
Watch together and critique clarity, logic, and visual flow.
WEEK 3 – Refinement & Delivery (Nov 3 – 9)
Goal: Polish presentation, sharpen delivery, and anticipate judge questions.
� Slide Deck Finalization
Limit to 8–10 slides (Title, Problem, Insight, Solution, Implementation, Ethics,
Impact, Conclusion).
Add 1 real-world example (e.g., IBM Watson Talent Framework or Workday
AI).
Add a simple cost-benefit or KPI slide for business realism.
� Rehearsal Plan
Day 1–2: Team run-throughs with timer (15 min presentation + 5 min Q&A).
Day 3: Peer or mentor feedback session (invite a professor or friend).
Day 4: Adjust slides, polish transitions, finalize speaking order.
Day 5: Final full rehearsal (recorded).
� Practice Q&A
Judges may ask:
“How do you ensure AI doesn’t introduce bias?”
“What ROI can you expect from this solution?”
“How does this apply to a small or mid-sized Manitoba company?”
“What risks or ethical safeguards would you recommend?”
Prepare 2–3 bullet answers per likely question.
Competition Week (Nov 10 – 16, if applicable)
Light review only — rest well.
Arrive early, set up slides, test tech.
During presentation: speak confidently, maintain eye contact, and tell a clear
problem → solution → impact story.
End with a strong 30-second summary:
“Our solution brings efficiency, fairness, and
insight to HR — proving that AI can enhance the
human side of management.”
Please provide us 2-3 cases to let us warm up
Case 1: “SmartHire at PrairieTech” — AI in Recruitment
Scenario
PrairieTech, a mid-sized Winnipeg software company (200 employees), recently
adopted an AI-driven résumé screening system to handle high application volumes.
Within 6 months, HR noticed a drop in female and newcomer applicants reaching
interview stage.
Your Task
You are HR consultants brought in to:
Investigate the bias issue in the recruitment AI.
Recommend a redesign strategy to make hiring both efficient and fair.
Present an implementation roadmap for ethical AI recruitment.
Data Prompts
60% of résumés screened out due to missing certain “preferred” keywords.
40% of technical hires come from one university or region.
AI vendor provides little transparency in its algorithm.
Guiding Questions
How can AI tools unintentionally create bias in recruitment?
What short-term and long-term actions would you recommend?
What metrics (KPIs) would you track to prove fairness and efficiency?
How can PrairieTech ensure compliance with Canadian employment and
privacy laws (PIPEDA)?
� Case 2: “Engage+ Analytics” — AI for Employee Retention
Scenario
A retail chain with 1,000 employees across Manitoba faces a 25% annual turnover
rate among frontline staff. HR wants to use AI-based predictive analytics to identify
employees at risk of leaving and take proactive action.
Your Task
Design a strategy showing how AI analytics can improve retention while respecting
employee privacy.
Data Prompts
Exit interviews show “lack of recognition” and “poor scheduling flexibility”
as top reasons for leaving.
HR has 3 years of employee data (attendance, performance, engagement
survey results).
HR worries that predictive analytics may “label” people unfairly.
Guiding Questions
What key variables would you use in a predictive model for turnover?
How can HR act on insights without breaching employee privacy?
What ethical concerns must be addressed in predictive HR analytics?
How would you measure the success of your retention program?
� Case 3: “AI-Learning Pathways at RedLeaf Manufacturing” —
Upskilling Workforce
Scenario
RedLeaf Manufacturing in Winnipeg wants to introduce AI-personalized learning to
upskill factory supervisors and office staff. The HR team is unsure how to start or
how to make it engaging for different employee types.
Your Task
Propose a strategic plan to implement AI-powered learning & development (L&D)
within 6 months.
Data Prompts
Workforce: 400 employees, ages 25–60, low digital literacy in some teams.
Training budget: CAD $40,000 annually.
Current system: static e-learning modules with 30% completion rate.
Guiding Questions
What kind of AI tools or platforms can help personalize training?
How can HR ensure older employees aren’t excluded from tech-based learning?
How would you track ROI and engagement improvement?
What change-management steps are needed to encourage adoption?
� How to Use These Warm-Up Cases
Assign one case per week (for practice before November).
Give your team 2–3 hours to analyze and 10–12 minutes to present.
Use your framework: Problem → Analysis → AI Solution → Implementation
→ Ethics → Impact.
Rotate presentation leadership so everyone practices