Choose One Case Study and Read a Brief Overview

1. Select your targeted case study, e.g., 

   • Alibaba’s supply chain optimization

   • Spotify’s music recommendation

   • JPMorgan Chase’s fraud detection

   • Tesla’s self-driving / Autopilot

   • Airbnb’s price prediction

2. Skim a summary focusing on:

   • Goals: what problem were they solving?

   • Methods: did they use ML, Deep Learning, recommendation algorithms, NLP, etc.?

   • Outcomes: were there improvements in user retention, cost savings, or new features?

Take lightweight notes to present a concise overview.

Prepare a Short Slide or Bullet List

1. Title / Header

• “Case Study: [Name of Company/Project]”

2. Goals

• Summarize the main objective: e.g., “Reduce shipping times by 30%,” or “Improve user music recommendations.”

3. Methods / Approach

• Outline the AI technique:

  • “Used a collaborative filtering approach with user-listening data.”

  • “Deployed a deep reinforcement learning model for route optimization.”

4. Outcomes / Results

• Concrete metrics: e.g., “Increased user engagement by 20%,” “Cut costs by $2M annually.”

• Additional intangible benefits: “Better customer satisfaction,” “Faster product iteration.”

5. Key Takeaways

• Note any challenges faced (data quality, model complexity, adoption) and how they overcame them.

• Reflect on how this could inspire your own environment.

Aim for 3–5 bullet points under each heading, so it’s brief and easy to present.

EXAMPLE

Below is a hypothetical example for “Spotify’s music recommendation” scenario:

Case Study: Spotify’s Music Recommendation

• Goals:

  • Enhance user retention by delivering highly personalized playlists (e.g., Discover Weekly).

  • Provide real-time suggestions for new songs and artists based on listening behavior.
    
    

• Methods / Approach:

  • Employed a collaborative filtering algorithm combined with NLP for analyzing track metadata.

  • Leveraged user behavior data (skips, favorites, replay frequency) to refine recommended tracks.
    
    

• Outcomes / Results:

  • Increased daily active users (DAU) by ~15% due to more engaging “Discover Weekly” lists.

  • Found that personalized recommendations drastically cut user churn rates.
    
    

• Key Takeaways:

  • Domain-specific data (song metadata, user logs) is crucial for accurate recommendations.

  • Challenge: balancing server costs with near-real-time updates for millions of songs.

  • Potential future expansion: deeper integration of user context (time of day, user mood data).

Tips for Presenting

1. Keep it short: 1 slide or 1 short bullet list is enough.

2. Focus on goals, methods, outcomes.

3. Explain briefly why the AI approach was beneficial (e.g., scale, cost reduction, personalization).

4. Optional: mention a challenge or limitation that might have been encountered.

Share the information with your EM!