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Top 40 Most Popular Case Studies of 2019

In the 2019 top 40 list, cases centered around food dominated the top 10, with the 2016 Coffee case retaining the top spot.

According to the Yale School of Management Case Research and Development Team (SOM CRDT) 2019 top 40 list, cases centered around food dominated the top 10, with the 2016 Coffee case retaining the top spot, a case on Cadbury taking second, and a case about Shake Shack taking third. A 2018 case about the Volkswagen emissions scandal made the Top 40 this year, shooting up to number four. Cases about Aadhaar , India’s universal ID project, the Nathan Cummings Foundation’s move to all-impact investing , business efforts to bridge the divide between Israel and Palestine , and New Haven’s own 360 State Street development all joined the list this year. Other popular topics in the top 10 include the marketing of financial services, organizational structure, and entrepreneurship.

SOM CRDT compiled its third annual Top 40 list by combining data from publishers, Google Analytics, direct sales, and other measures of interest and adoption. Other year-end data for 2019 showed:

• Over 30K users from 161 countries viewed 133 “raw” cases online.
• Just over a third of raw case users were from the U.S.
• Customers purchased 127 different case titles from the online store.
• Twenty-five percent of this year’s cases in the top 40 featured women protagonists.
• Traffic to the SOM CRDT case directory increased by 30% over 2018, with almost 150K page views.
• Sixty percent of those who perused the SOM case directory were from outside the U.S.
• The top 40 cases were supervised by 33 different Yale SOM faculty members.

All cases listed here are available for purchase through our online store . 

The top 10 cases of 2019 included:

#1 - Coffee 2016

Faculty Supervision: Todd Cort

Coffee 2016 asks students to consider the coffee supply chain and generate ideas for what can be done to equalize returns across various stakeholders. The case draws a parallel between coffee and wine. Both beverages encourage connoisseurship, but only wine growers reap a premium for their efforts to ensure quality, while coffee farmers barely eke out an existence. The case describes the history of coffee production across the world, the rise of the “third wave” of coffee consumption in the developed world, the efforts of the Illy Company to help coffee growers, and the differences between “fair” trade and direct trade. Faculty have found the case provides a wide canvas to discuss supply chain issues, examine marketing practices, and encourage creative solutions to business problems.

#2 - Cadbury: An Ethical Company Struggles to Insure the Integrity of Its Supply Chain

Faculty Supervision: Ira Millstein

The case describes revelations that the production of cocoa in the Côte d’Ivoire involved child slave labor. These stories hit Cadbury especially hard. Cadbury's culture had been deeply rooted in the religious traditions of the company's founders, and the organization had paid close attention to the welfare of its workers and its sourcing practices. The US Congress was considering legislation that would allow chocolate grown on certified plantations to be labeled “slave labor free,” painting the rest of the industry in a bad light. Chocolate producers had asked for time to rectify the situation, but the extension they negotiated was running out. Students are asked whether Cadbury should join with the industry to lobby for more time?  What else could Cadbury do to ensure its supply chain was ethically managed?

#3 - Shake Shack IPO

Faculty Supervision: Jake Thomas and Geert Rouwenhorst

From an art project in a New York City park, Shake Shack developed a devoted fan base that greeted new Shake Shack locations with cheers and long lines. When Shake Shack went public on January 30, 2015, investors displayed a similar enthusiasm. Opening day investors bid up the $21 per share offering price by 118% to reach $45.90 at closing bell. By the end of May, investors were paying $92.86 per share. Students are asked if this price represented a realistic valuation of the enterprise and if not, what was Shake Shack truly worth? The case provides extensive information on Shake Shack’s marketing, competitors, operations and financials, allowing instructors to weave a wide variety of factors into a valuation of the company.

#4 - Volkswagen: Engineering a Disaster

Faculty Supervision: David Bach

In September 2015, Volkswagen admitted to installing defeat switches in their diesel cars to fool environmental regulators. The fraud could cost the company billions in fines and lost revenues, leaving all to wonder, "How could this have happened?" The case considers the company’s history and culture, as well as looking at how regulatory environments in Europe and the U.S. differed. What combination of these factors led to this wide-spread ethical disaster?

#5 - Netflix

Faculty Supervision: Arthur Swersey

The case describes how Netflix optimized the rental of DVDs by mail to become a major media company. In 2007, the company faced two challenges. The first was increased competition in the DVD-by-mail business. The second was challenges from various streaming services. For all its operational savvy, had Netflix’s time passed?

#6 - Ant Financial

Faculty Supervision: K. Sudhir in cooperation with Renmin University of China School of Business

In 2015, Ant Financial’s MYbank (an offshoot of Jack Ma’s Alibaba company) was looking to extend services to rural areas in China by providing small loans to farmers. Microloans have always been costly for financial institutions to offer to the unbanked (though important in development) but MYbank believed that fintech innovations such as using the internet to communicate with loan applicants and judge their creditworthiness would make the program sustainable. Students are asked whether MYbank could operate the program at scale? Would its big data and technical analysis provide an accurate measure of credit risk for loans to small customers? Could MYbank rely on its new credit-scoring system to reduce operating costs to make the program sustainable? What are the social costs of introducing credit scoring to China?

#7 - Mastercard

Faculty Supervision: Ravi Dhar, Vineet Kumar, and Amy Wrzesniewski

When Raja Rajamannar became Mastercard's CMO in 2013, the company had already created one of the most successful brand-building campaigns in history. Yet he decided to substantially change the marketing strategy – and transform his department. The case examines the premises behind “experience marketing” and considers the dynamics of a department where traditional marketers must work with new internet marketers who bring new skills and sensibilities.

#8 - Golden Agri Resources and Sustainability

Golden Agri, a major Indonesian palm oil producer and exporter, and Greenpeace reached an unprecedented agreement to limit deforestation in Indonesia in 2012. But would the agreement survive and increase sustainability standards in the palm oil industry?

#9 - AgBiome

Faculty Supervision: James Baron

Can a company grow while eschewing hierarchy in favor of commitment? From its founding, AgBiome has adopted a flat structure, where committees of employees make the key decisions. The case describes the structures and principles that make this agricultural R&D firm run. With a few successful innovations under its belt, can the company grow while maintaining its commitment model?

#10 - Bovard and Majid

Faculty Supervision: AJ Wasserstein

Lia Majid had spent nearly a year and a half searching for a business to acquire and thought she’d finally found a deal worth pursuing. She spent months negotiating with the firm’s sellers and believed she was on the verge of a purchase. However, at the last minute, her backers and mentors at the Search Fund Accelerator (SFA) wanted her to completely restructure the deal. Faced with their concerns, Majid had to decide whether to re-engage the target firm or move on to investigate other prospects.

Complete list of the 40 Most Popular Case Studies of 2019

W                     Case features a woman in a major leadership role

Click on the case title to learn more about the dilemma. A selection of our most popular cases are available for purchase via our online store .

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HBS Case Selections

Revlon India's Turnaround: Navigating Online-Offline Decisions Using a Balanced Scorecard

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Urban Company

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RMZ 4.0: "How fast do we want to run?"

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Roblox: Virtual Commerce in the Metaverse

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Hugging Face: Serving AI on a Platform

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SmartOne: Building an AI Data Business

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Hometown Foods: Changing Price Amid Inflation

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Elon Musk's Big Bets

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Elon Musk: Balancing Purpose and Risk

Tesla's ceo compensation plan.

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China Rapid Finance: The Collapse of China's P2P Lending Industry

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Forbidden City: Launching a Craft Beer in China

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Racial Discrimination on Airbnb (A)

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AT&T, Retraining, and the Workforce of Tomorrow

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Eve Hall: The African American Investment Fund in Milwaukee

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United Housing - Otis Gates

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The Home Depot: Leadership in Crisis Management

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Insurer of Last Resort?: The Federal Financial Response to September 11

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Li & Fung 2012

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Sony and the JK Wedding Dance

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United Breaks Guitars

David dao on united airlines.

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Marketing Reading: Digital Marketing

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Social Strategy at Nike

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The Tate's Digital Transformation

Social strategy at american express, mellon financial and the bank of new york.

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The Walt Disney Company and Pixar, Inc.: To Acquire or Not to Acquire?

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Dow's Bid for Rohm and Haas

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Finance Reading: The Mergers and Acquisitions Process

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Apple: Privacy vs. Safety? (A)

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Sidewalk Labs: Privacy in a City Built from the Internet Up

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Data Breach at Equifax

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Apple Inc. in 2012

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Home Nursing of North Carolina

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TCS: From Physical Offices to Borderless Work

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Starbucks Coffee Company: Transformation and Renewal

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JCPenney: Back in Business

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(Re)Building a Global Team: Tariq Khan at Tek

Managing a global team: greg james at sun microsystems, inc. (a).

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Organizational Behavior Reading: Leading Global Teams

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Anthony Starks at InSiL Therapeutics (A)

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Alex Montana at ESH Manufacturing Co.

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Michelle Levene (A)

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John and Andrea Rice: Entrepreneurship and Life

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Uber in 2024: From Industry Disruption to Creating Value For All Stakeholders

Dara Khosrowshahi became the CEO of Uber in August 2017, following internal turbulence and serious headwinds related to the company’s governance and reputation. Five short years later, Uber was clearly back on course, building on the success of its technology platform to reach 150 million monthly active platform users—and a market cap of $125 billion by the end of 2023.

This case study traces the remarkable transformation of Uber from its early innovation as a ride-hailing pioneer in a handful of cities, to the global expansion of Uber mobility services that required close attention to local operational and regulatory practices, to solving the complex technical challenges to drive Uber’s food delivery services forward. Interviews with Uber leadership reveals the strategic approach to work on the engineering, data science, product management, and product design challenges involved in building and maintaining a customer-friendly app and create an optimized user experience—and scale this on a global basis while factoring in local conditions and practices.

Key to this success was a culture reboot within Uber, and a renewed focus on collaboration and value creation for all stakeholders. The company that had found its initial footing by disrupting and transforming the taxi industry, more than a decade earlier, now faced a future where artificial intelligence and autonomous vehicles would likely disrupt the mobility sector once again—but Uber was preparing intensively for what the future might bring.

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• Top 10 International Business Negotiation Case Studies

International business negotiation case studies offer insights to business negotiators who face challenges in the realm of cross-cultural business negotiation.

By PON Staff — on November 14th, 2024 / International Negotiation

If you engage in international negotiation , you can improve your odds of success by learning from these 10 well-known international business negotiation case studies:

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• Apple’s Apology in China

When Apple CEO Timothy D. Cook apologized to Apple customers in China for problems arising from Apple’s warranty policy, he promised to rectify the issue. In a negotiation research study, Professor William W. Maddux of INSEAD and his colleagues compared reactions to apologies in the United States and in Japan. They discovered that in “collectivist cultures” such as China and Japan, apologies can be particularly effective in repairing broken trust, regardless of whether the person apologizing is to blame. This may be especially true in a cross-cultural business negotiation such as this one.

• Bangladesh Factory-Safety Agreements

In this negotiation case study, an eight-story factory collapsed in Bangladesh, killing an estimated 1,129 people, most of whom were low-wage garment workers manufacturing goods for foreign retailers. Following the tragedy, companies that outsourced their garment production faced public pressure to improve conditions for foreign workers. Labor unions focused their efforts on persuading Swedish “cheap chic” giant H&M to take the lead on safety improvements. This negotiation case study highlights the pros and cons of all-inclusive, diffuse agreements versus targeted, specific agreements.

• The Microsoft-Nokia Deal

Microsoft made the surprising announcement that it was purchasing Finnish mobile handset maker Nokia for $7.2 billion, a merger aimed at building Microsoft’s mobile and smartphone offerings. The merger faced even more complexity after the ink dried on the contract—namely, the challenges of integrating employees from different cultures. International business negotiation case studies such as this one underscore the difficulties that companies face when attempting to negotiate two different identities.

• The Cyprus Crisis

With the economy of the tiny Mediterranean island nation Cyprus near collapse, the International Monetary Fund (IMF), European Central Bank (ECB), and the European Commission teamed up to offer a 10-billion-euro bailout package contingent on Cyprus provisioning a substantial amount of the money through a one-time tax on ordinary Cypriot bank depositors. The move proved extremely unpopular in Cyprus and protests resulted. The nation’s president was left scrambling for a backup plan. The lesson from international business negotiation case studies such as this? Sometimes the best deal you can get may be better than no deal at all.

• Dissent in the European Union

The European Union (EU) held a summit to address the coordination of economic activities and policies among EU member states. German resistance to such a global deal was strong, and pessimism about a unified EU banking system ran high as a result of the EU financial crisis. The conflict reflects the difficulty of forging  multiparty agreements  during times of stress and crisis.

• North and South Korea Talks Collapse

Negotiations between North Korea and South Korea were supposed to begin in Seoul aimed at lessening tensions between the divided nations. It would have been the highest government dialogue between the two nations in years. Just before negotiations were due to start, however, North Korea complained that it was insulted that the lead negotiator from the South wasn’t higher in status. The conflict escalated, and North Korea ultimately withdrew from the talks. The case highlights the importance of pride and power perceptions in international negotiations.

• Canceled Talks for the U.S. and Russia

Then-U.S. president Barack Obama canceled a scheduled summit with Russian President Vladimir Putin, citing a lack of progress on a variety of negotiations. The announcement came on the heels of Russia’s decision to grant temporary asylum to former National Security Agency contractor Edward Snowden, who made confidential data on American surveillance programs public. From international business negotiation case studies such as this, we can learn strategic reasons for  breaking off ties , if only temporarily, with a counterpart.

• The East China Sea Dispute

In recent years, several nations, including China and Japan, have laid claim to a chain of islands in the East China Sea. China’s creation of an “air defense” zone over the islands led to an international dispute with Japan. International negotiators seeking to resolve complex disputes may gain valuable advice from this negotiation case study, which involves issues of international law as well as perceptions of relative strength or weakness in negotiations.

• An International Deal with Syria

When then-U.S. Secretary of State John Kerry and his Russian counterpart, Sergey Lavrov, announced a deal to prevent the United States from entering the Syrian War, it was contingent on Syrian President Bashar al-Assad’s promise to dismantle his nation’s chemical weapons. Like other real-life negotiation case studies, this one highlights the value of expanding our focus in negotiation.

• A Nuclear Deal with Iran

When the United States and five other world powers announced an interim agreement to temporarily freeze Iran’s nuclear program, the six-month accord, which eventually led to a full-scale agreement in 2015, was designed to give international negotiators time to negotiate a more comprehensive pact that would remove the threat of Iran producing nuclear weapons. As Iranian President Hassan Rouhani insisted that Iran had a sovereign right to enrich uranium, the United States rejected Iran’s claim to having a “right to enrich” but agreed to allow Iran to continue to enrich at a low level, a concession that allowed a deal to emerge.

What international business negotiation case studies in the news have you learned from in recent years?

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40 Detailed Artificial Intelligence Case Studies [2024]

In this dynamic era of technological advancements, Artificial Intelligence (AI) emerges as a pivotal force, reshaping the way industries operate and charting new courses for business innovation. This article presents an in-depth exploration of 40 diverse and compelling AI case studies from across the globe. Each case study offers a deep dive into the challenges faced by companies, the AI-driven solutions implemented, their substantial impacts, and the valuable lessons learned. From healthcare and finance to transportation and retail, these stories highlight AI’s transformative power in solving complex problems, optimizing processes, and driving growth, offering insightful glimpses into the potential and versatility of AI in shaping our world.

Related: How to Become an AI Thought Leader?

1. IBM Watson Health: Revolutionizing Patient Care with AI

Task/Conflict: The healthcare industry faces challenges in handling vast amounts of patient data, accurately diagnosing diseases, and creating effective treatment plans. IBM Watson Health aimed to address these issues by harnessing AI to process and analyze complex medical information, thus improving the accuracy and efficiency of patient care.

Solution: Utilizing the cognitive computing capabilities of IBM Watson, this solution involves analyzing large volumes of medical records, research papers, and clinical trial data. The system uses natural language processing to understand and process medical jargon, making sense of unstructured data to aid medical professionals in diagnosing and treating patients.

Overall Impact:

• Enhanced accuracy in patient diagnosis and treatment recommendations.
• Significant improvement in personalized healthcare services.

Key Learnings:

• AI can complement medical professionals’ expertise, leading to better healthcare outcomes.
• The integration of AI in healthcare can lead to significant advancements in personalized medicine.

2. Google DeepMind’s AlphaFold: Unraveling the Mysteries of Protein Folding

Task/Conflict: The scientific community has long grappled with the protein folding problem – understanding how a protein’s amino acid sequence determines its 3D structure. Solving this problem is crucial for drug discovery and understanding diseases at a molecular level, yet it remained a formidable challenge due to the complexity of biological structures.

Solution: AlphaFold, developed by Google DeepMind, is an AI model trained on vast datasets of known protein structures. It assesses the distances and angles between amino acids to predict how a protein folds, outperforming existing methods in terms of speed and accuracy. This breakthrough represents a major advancement in computational biology.

• Significant acceleration in drug discovery and disease understanding.
• Set a new benchmark for computational methods in biology.
• AI’s predictive power can solve complex biological problems.
• The application of AI in scientific research can lead to groundbreaking discoveries.

3. Amazon: Transforming Supply Chain Management through AI

Task/Conflict: Managing a global supply chain involves complex challenges like predicting product demand, optimizing inventory levels, and streamlining logistics. Amazon faced the task of efficiently managing its massive inventory while minimizing costs and meeting customer demands promptly.

Solution: Amazon employs sophisticated AI algorithms for predictive inventory management, which forecast product demand based on various factors like buying trends, seasonality, and market changes. This system allows for real-time adjustments, adapting swiftly to changing market dynamics.

• Reduced operational costs through efficient inventory management.
• Improved customer satisfaction with timely deliveries and availability.
• AI can significantly enhance supply chain efficiency and responsiveness.
• Predictive analytics in inventory management leads to reduced waste and cost savings.

4. Tesla’s Autonomous Vehicles: Driving the Future of Transportation

Task/Conflict: The development of autonomous vehicles represents a major technological and safety challenge. Tesla aimed to create self-driving cars that are not only reliable and safe but also capable of navigating complex traffic conditions without human intervention.

Solution: Tesla’s solution involves advanced AI and machine learning algorithms that process data from various sensors and cameras to understand and navigate the driving environment. Continuous learning from real-world driving data allows the system to improve over time, making autonomous driving safer and more efficient.

• Leadership in the autonomous vehicle sector, enhancing road safety.
• Continuous improvements in self-driving technology through AI-driven data analysis.
• Continuous data analysis is key to advancing autonomous driving technologies.
• AI can significantly improve road safety and driving efficiency.

Related: High-Paying AI Career Options

5. Zara: Fashioning the Future with AI in Retail

Task/Conflict: In the fast-paced fashion industry, predicting trends and managing inventory efficiently are critical for success. Zara faced the challenge of quickly adapting to changing fashion trends while avoiding overstock and meeting consumer demand.

Solution: Zara employs AI algorithms to analyze fashion trends, customer preferences, and sales data. The AI system also assists in managing inventory, ensuring that popular items are restocked promptly and that stores are not overburdened with unsold products. This approach optimizes both production and distribution.

• Increased sales and profitability through optimized inventory.
• Enhanced customer satisfaction by aligning products with current trends.
• AI can accurately predict consumer behavior and trends.
• Effective inventory management through AI can significantly impact business success.

6. Netflix: Personalizing Entertainment with AI

Task/Conflict: In the competitive streaming industry, providing a personalized user experience is key to retaining subscribers. Netflix needed to recommend relevant content to each user from its vast library, ensuring that users remained engaged and satisfied.

Solution: Netflix developed an advanced AI-driven recommendation engine that analyzes individual viewing habits, ratings, and preferences. This personalized approach keeps users engaged, as they are more likely to find content that interests them, enhancing their overall viewing experience.

• Increased viewer engagement and longer watch times.
• Higher subscription retention rates due to personalized content.
• Personalized recommendations significantly enhance user experience.
• AI-driven content curation is essential for success in digital entertainment.

7. Airbus: Elevating Aircraft Maintenance with AI

Task/Conflict: Aircraft maintenance is crucial for ensuring flight safety and operational efficiency. Airbus faced the challenge of predicting maintenance needs to prevent equipment failures and reduce downtime, which is critical in the aviation industry.

Solution: Airbus implemented AI algorithms for predictive maintenance, analyzing data from aircraft sensors to identify potential issues before they lead to failures. This system assesses the condition of various components, predicting when maintenance is needed. The solution not only enhances safety but also optimizes maintenance schedules, reducing unnecessary inspections and downtime.

• Decreased maintenance costs and reduced aircraft downtime.
• Improved safety with proactive maintenance measures.
• AI can predict and prevent potential equipment failures.
• Predictive maintenance is essential for operational efficiency and safety in aviation.

8. American Express: Securing Transactions with AI

Task/Conflict: Credit card fraud is a significant issue in the financial sector, leading to substantial losses and undermining customer trust. American Express needed an efficient way to detect and prevent fraudulent transactions in real-time.

Solution: American Express utilizes machine learning models to analyze transaction data. These models identify unusual patterns and behaviors indicative of fraud. By constant learning from refined data, the system becomes increasingly accurate in detecting fraudulent activities, providing real-time alerts and preventing unauthorized transactions.

• Minimized financial losses due to reduced fraudulent activities.
• Enhanced customer trust and security in financial transactions.
• Machine learning is highly effective in fraud detection.
• Real-time data analysis is crucial for preventing financial fraud.

Related: Is AI a Good Career Option for Women?

9. Stitch Fix: Tailoring the Future of Fashion Retail

Task/Conflict: In the competitive fashion retail industry, providing a personalized shopping experience is key to customer satisfaction and business growth. Stitch Fix aimed to offer customized clothing selections to each customer, based on their unique preferences and style.

Solution: Stitch Fix uses AI and algorithms analyze customer feedback, style preferences, and purchase history to recommend clothing items. This personalized approach is complemented by human stylists, ensuring that each customer receives a tailored selection that aligns with their individual style.

• Increased customer satisfaction through personalized styling services.
• Business growth driven by a unique, AI-enhanced shopping experience.
• AI combined with human judgment can create highly effective personalization.
• Tailoring customer experiences using AI leads to increased loyalty and business success.

10. Baidu: Breaking Language Barriers with Voice Recognition

Task/Conflict: Voice recognition technology faces the challenge of accurately understanding and processing speech in various languages and accents. Baidu aimed to enhance its voice recognition capabilities to provide more accurate and user-friendly interactions in multiple languages.

Solution: Baidu employs deep learning algorithms for voice and speech recognition, training its system on a diverse range of languages and dialects. This approach allows for more accurate recognition of speech patterns, enabling the technology to understand and respond to voice commands more effectively. The system continuously improves as it processes more voice data, making technology more accessible to users worldwide.

• Enhanced user interaction with technology in multiple languages.
• Reduced language barriers in voice-activated services and devices.
• AI can effectively bridge language gaps in technology.
• Continuous learning from diverse data sets is key to improving voice recognition.

11. JP Morgan: Revolutionizing Legal Document Analysis with AI

Task/Conflict: Analyzing legal documents, such as contracts, is a time-consuming and error-prone process. JP Morgan sought to streamline this process, reducing the time and effort required while increasing accuracy.

Solution: JP Morgan implemented an AI-powered tool, COIN (Contract Intelligence), to analyze legal documents quickly and accurately. COIN uses NLP to interpret and extract relevant information from contracts, significantly reducing the time required for document review.

• Dramatic reduction in time required for legal document analysis.
• Increased accuracy and reduced human error in contract interpretation.
• AI can efficiently handle large volumes of data, offering speed and accuracy.
• Automation in legal processes can significantly enhance operational efficiency.

12. Microsoft: AI for Accessibility

Task/Conflict: People with disabilities often face challenges in accessing technology. Microsoft aimed to create AI-driven tools to enhance accessibility, especially for individuals with visual, hearing, or cognitive impairments.

Solution: Microsoft developed a range of AI-powered tools including applications for voice recognition, visual assistance, and cognitive support, making technology more accessible and user-friendly. For instance, Seeing AI, an app developed by Microsoft, helps visually impaired users to understand their surroundings by describing people, texts, and objects.

• Improved accessibility and independence for people with disabilities.
• Creation of more inclusive technology solutions.
• AI can significantly contribute to making technology accessible for all.
• Developing inclusive technology is essential for societal progress.

Related: How to get an Internship in AI?

13. Alibaba’s City Brain: Revolutionizing Urban Traffic Management

Task/Conflict: Urban traffic congestion is a major challenge in many cities, leading to inefficiencies and environmental concerns. Alibaba’s City Brain project aimed to address this issue by using AI to optimize traffic flow and improve public transportation in urban areas.

Solution: City Brain uses AI to analyze real-time data from traffic cameras, sensors, and GPS systems. It processes this information to predict traffic patterns and optimize traffic light timing, reducing congestion. The system also provides data-driven insights for urban planning and emergency response coordination, enhancing overall city management.

• Significant reduction in traffic congestion and improved urban transportation.
• Enhanced efficiency in city management and emergency response.
• AI can effectively manage complex urban systems.
• Data-driven solutions are key to improving urban living conditions.

14. Deep 6 AI: Accelerating Clinical Trials with Artificial Intelligence

Task/Conflict: Recruiting suitable patients for clinical trials is often a slow and cumbersome process, hindering medical research. Deep 6 AI sought to accelerate this process by quickly identifying eligible participants from a vast pool of patient data.

Solution: Deep 6 AI employs AI to sift through extensive medical records, identifying potential trial participants based on specific criteria. The system analyzes structured and unstructured data, including doctor’s notes and diagnostic reports, to find matches for clinical trials. This approach significantly speeds up the recruitment process, enabling faster trial completions and advancements in medical research.

• Quicker recruitment for clinical trials, leading to faster research progress.
• Enhanced efficiency in medical research and development.
• AI can streamline the patient selection process for clinical trials.
• Efficient recruitment is crucial for the advancement of medical research.

15. NVIDIA: Revolutionizing Gaming Graphics with AI

Task/Conflict: Enhancing the realism and performance of gaming graphics is a continuous challenge in the gaming industry. NVIDIA aimed to revolutionize gaming visuals by leveraging AI to create more realistic and immersive gaming experiences.

Solution: NVIDIA’s AI-driven graphic processing technologies, such as ray tracing and deep learning super sampling (DLSS), provide highly realistic and detailed graphics. These technologies use AI to render images more efficiently, improving game performance without compromising on visual quality. This innovation sets new standards in gaming graphics, making games more lifelike and engaging.

• Elevated gaming experiences with state-of-the-art graphics.
• Set new industry standards for graphic realism and performance.
• AI can significantly enhance creative industries, like gaming.
• Balancing performance and visual quality is key to gaming innovation.

16. Palantir: Mastering Data Integration and Analysis with AI

Task/Conflict: Integrating and analyzing large-scale, diverse datasets is a complex task, essential for informed decision-making in various sectors. Palantir Technologies faced the challenge of making sense of vast amounts of data to provide actionable insights for businesses and governments.

Solution: Palantir developed AI-powered platforms that integrate data from multiple sources, providing a comprehensive view of complex systems. These platforms use machine learning to analyze data, uncover patterns, and predict outcomes, assisting in strategic decision-making. This solution enables users to make informed decisions in real-time, based on a holistic understanding of their data.

• Enhanced decision-making capabilities in complex environments.
• Greater insights and efficiency in data analysis across sectors.
• Effective data integration is crucial for comprehensive analysis.
• AI-driven insights are essential for strategic decision-making.

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17. Blue River Technology: Sowing the Seeds of AI in Agriculture

Task/Conflict: The agriculture industry faces challenges in increasing efficiency and sustainability while minimizing environmental impact. Blue River Technology aimed to enhance agricultural practices by using AI to make farming more precise and efficient.

Solution: Blue River Technology developed AI-driven agricultural robots that perform tasks like precise planting and weed control. These robots use ML to identify plants and make real-time decisions, such as applying herbicides only to weeds. This targeted approach reduces chemical usage and promotes sustainable farming practices, leading to better crop yields and environmental conservation.

• Significant reduction in chemical usage in farming.
• Increased crop yields through precision agriculture.
• AI can contribute significantly to sustainable agricultural practices.
• Precision farming is key to balancing productivity and environmental conservation.

18. Salesforce: Enhancing Customer Relationship Management with AI

Task/Conflict: In the realm of customer relationship management (CRM), personalizing interactions and gaining insights into customer behavior are crucial for business success. Salesforce aimed to enhance CRM capabilities by integrating AI to provide personalized customer experiences and actionable insights.

Solution: Salesforce incorporates AI-powered tools into its CRM platform, enabling businesses to personalize customer interactions, automate responses, and predict customer needs. These tools analyze customer data, providing insights that help businesses tailor their strategies and communications. The AI integration not only improves customer engagement but also streamlines sales and marketing efforts.

• Improved customer engagement and satisfaction.
• Increased business growth through tailored marketing and sales strategies.
• AI-driven personalization is key to successful customer relationship management.
• Leveraging AI for data insights can significantly impact business growth.

19. OpenAI: Transforming Natural Language Processing

Task/Conflict: OpenAI aimed to advance NLP by developing models capable of generating coherent and contextually relevant text, opening new possibilities in AI-human interaction.

Solution: OpenAI developed the Generative Pre-trained Transformer (GPT) models, which use deep learning to generate text that closely mimics human language. These models are trained on vast datasets, enabling them to understand context and generate responses in a conversational and coherent manner.

• Pioneered advancements in natural language understanding and generation.
• Expanded the possibilities for AI applications in communication.
• AI’s ability to mimic human language has vast potential applications.
• Advancements in NLP are crucial for improving AI-human interactions.

20. Siemens: Pioneering Industrial Automation with AI

Task/Conflict: Industrial automation seeks to improve productivity and efficiency in manufacturing processes. Siemens faced the challenge of optimizing these processes using AI to reduce downtime and enhance output quality.

Solution: Siemens employs AI-driven solutions for predictive maintenance and process optimization to reduce downtime in industrial settings. Additionally, AI optimizes manufacturing processes, ensuring quality and efficiency.

• Increased productivity and reduced downtime in industrial operations.
• Enhanced quality and efficiency in manufacturing processes.
• AI is a key driver in the advancement of industrial automation.
• Predictive analytics are crucial for maintaining efficiency in manufacturing.

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21. Ford: Driving Safety Innovation with AI

Task/Conflict: Enhancing automotive safety and providing effective driver assistance systems are critical challenges in the auto industry. Ford aimed to leverage AI to improve vehicle safety features and assist drivers in real-time decision-making.

Solution: Ford integrated AI into its advanced driver assistance systems (ADAS) to provide features like adaptive cruise control, lane-keeping assistance, and collision avoidance. These systems use sensors and cameras to gather data, which AI processes to make split-second decisions that enhance driver safety and vehicle performance.

• Improved safety features in vehicles, minimizing accidents and improving driver confidence.
• Enhanced driving experience with intelligent assistance features.
• AI can highly enhance safety in the automotive industry.
• Real-time data processing and decision-making are essential for effective driver assistance systems.

22. HSBC: Enhancing Banking Security with AI

Task/Conflict: As financial transactions increasingly move online, banks face heightened risks of fraud and cybersecurity threats. HSBC needed to bolster its protective measures to secure user data and prevent scam.

Solution: HSBC employed AI-driven security systems to observe transactions and identify suspicious activities. The AI models analyze patterns in customer behavior and flag anomalies that could indicate fraudulent actions, allowing for immediate intervention. This helps in minimizing the risk of financial losses and protects customer trust.

• Strengthened security measures and reduced incidence of fraud.
• Maintained high levels of customer trust and satisfaction.
• AI is critical in enhancing security in the banking sector.
• Proactive fraud detection can prevent significant financial losses.

23. Unilever: Optimizing Supply Chain with AI

Task/Conflict: Managing a global supply chain involves complexities related to logistics, demand forecasting, and sustainability practices. Unilever sought to enhance its supply chain efficiency while promoting sustainability.

Solution: Unilever implemented AI to optimize its supply chain operations, from raw material sourcing to distribution. AI algorithms analyze data to forecast demand, improve inventory levels, and minimize waste. Additionally, AI helps in selecting sustainable practices and suppliers, aligning with Unilever’s commitment to environmental responsibility.

• Enhanced efficiency and reduced costs in supply chain operations.
• Better sustainability practices, reducing environmental impact.
• AI can highly optimize supply chain management.
• Integrating AI with sustainability initiatives can lead to environmentally responsible operations.

24. Spotify: Personalizing Music Experience with AI

Task/Conflict: In the competitive music streaming industry, providing a personalized listening experience is crucial for user engagement and retention. Spotify needed to tailor music recommendations to individual tastes and preferences.

Solution: Spotify utilizes AI-driven algorithms to analyze user listening habits, preferences, and contextual data to recommend music tracks and playlists. This personalization ensures that users are continually engaged and discover new music that aligns with their tastes, enhancing their overall listening experience.

• Increased customer engagement and time spent on the platform.
• Higher user satisfaction and subscription retention rates.
• Personalized content delivery is key to user retention in digital entertainment.
• AI-driven recommendations significantly enhance user experience.

Related: How can AI be used in Instagram Marketing?

25. Walmart: Revolutionizing Retail with AI

Task/Conflict: Retail giants like Walmart face challenges in inventory management and providing a high-quality customer service experience. Walmart aimed to use AI to optimize these areas and enhance overall operational efficacy.

Solution: Walmart deployed AI technologies across its stores to manage inventory levels effectively and enhance customer service. AI systems predict product demand to optimize stock levels, while AI-driven robots assist in inventory management and customer service, such as guiding customers in stores and handling queries.

• Improved inventory management, reducing overstock and shortages.
• Enhanced customer service experience in stores.
• AI can streamline retail operations significantly.
• Enhanced customer service through AI leads to better customer satisfaction.

26. Roche: Innovating Drug Discovery with AI

Task/Conflict: The pharmaceutical industry faces significant challenges in drug discovery, requiring vast investments of time and resources. Roche aimed to utilize AI to streamline the drug development process and enhance the discovery of new therapeutics.

Solution: Roche implemented AI to analyze medical data and simulate drug interactions, speeding up the drug discovery process. AI models predict the effectiveness of compounds and identify potential candidates for further testing, significantly minimizing the time and cost related with traditional drug development procedures.

• Accelerated drug discovery processes, bringing new treatments to market faster.
• Reduced costs and increased efficiency in pharmaceutical research.
• AI can greatly accelerate the drug discovery process.
• Cost-effective and efficient drug development is possible with AI integration.

27. IKEA: Enhancing Customer Experience with AI

Task/Conflict: In the competitive home furnishings market, enhancing the customer shopping experience is crucial for success. IKEA aimed to use AI to provide innovative design tools and improve customer interaction.

Solution: IKEA introduced AI-powered tools such as virtual reality apps that allow consumers to visualize furniture before buying. These tools help customers make more informed decisions and enhance their shopping experience. Additionally, AI chatbots assist with customer service inquiries, providing timely and effective support.

• Improved customer decision-making and satisfaction with interactive tools.
• Enhanced efficiency in customer service.
• AI can transform the retail experience by providing innovative customer interaction tools.
• Effective customer support through AI can enhance brand loyalty and satisfaction.

28. General Electric: Optimizing Energy Production with AI

Task/Conflict: Managing energy production efficiently while predicting and mitigating potential issues is crucial for energy companies. General Electric (GE) aimed to improve the efficiency and reliability of its energy production facilities using AI.

Solution: GE integrated AI into its energy management systems to enhance power generation and distribution. AI algorithms predict maintenance needs and optimize energy production, ensuring efficient operation and reducing downtime. This predictive maintenance approach saves costs and enhances the reliability of energy production.

• Increased efficiency in energy production and distribution.
• Reduced operational costs and enhanced system reliability.
• Predictive maintenance is crucial for cost-effective and efficient energy management.
• AI can significantly improve the predictability and efficiency of energy production.

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29. L’Oréal: Transforming Beauty with AI

Task/Conflict: Personalization in the beauty industry enhances customer satisfaction and brand loyalty. L’Oréal aimed to personalize beauty products and experiences for its diverse customer base using AI.

Solution: L’Oréal leverages AI to assess consumer data and provide personalized product suggestions. AI-driven tools assess skin types and preferences to recommend the best skincare and makeup products. Additionally, virtual try-on apps powered by AI allow customers to see how products would look before making a purchase.

• Enhanced personalization of beauty products and experiences.
• Increased customer engagement and satisfaction.
• AI can provide highly personalized experiences in the beauty industry.
• Data-driven personalization enhances customer satisfaction and brand loyalty.

30. The Weather Company: AI-Predicting Weather Patterns

Task/Conflict: Accurate weather prediction is vital for planning and safety in various sectors. The Weather Company aimed to enhance the accuracy of weather forecasts and provide timely weather-related information using AI.

Solution: The Weather Company employs AI to analyze data from weather sensors, satellites, and historical weather patterns. AI models improve the accuracy of weather predictions by identifying trends and anomalies. These enhanced forecasts help in better planning and preparedness for weather events, benefiting industries like agriculture, transportation, and public safety.

• Improved accuracy in weather forecasting.
• Better preparedness and planning for adverse weather conditions.
• AI can enhance the precision of meteorological predictions.
• Accurate weather forecasting is crucial for safety and operational planning in multiple sectors.

31. Cisco: Securing Networks with AI

Task/Conflict: As cyber threats evolve and become more sophisticated, maintaining robust network security is crucial for businesses. Cisco aimed to leverage AI to enhance its cybersecurity measures, detecting and responding to threats more efficiently.

Solution: Cisco integrated AI into its cybersecurity framework to analyze network traffic and identify unusual patterns indicative of cyber threats. This AI-driven approach allows for real-time threat detection and automated responses, thus improving the speed and efficacy of security measures.

• Strengthened network security with faster threat detection.
• Reduced manual intervention by automating threat responses.
• AI is essential in modern cybersecurity for real-time threat detection.
• Automating responses can significantly enhance network security protocols.

32. Adidas: AI in Sports Apparel Manufacturing

Task/Conflict: To maintain competitive advantage in the fast-paced sports apparel market, Adidas sought to innovate its manufacturing processes by incorporating AI to improve efficiency and product quality.

Solution: Adidas employed AI-driven robotics and automation technologies in its factories to streamline the production process. These AI systems optimize manufacturing workflows, enhance quality control, and reduce waste by precisely cutting fabrics and assembling materials according to exact specifications.

• Increased production efficacy and reduced waste.
• Enhanced consistency and quality of sports apparel.
• AI-driven automation can revolutionize manufacturing processes.
• Precision and efficiency in production lead to higher product quality and sustainability.

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33. KLM Royal Dutch Airlines: AI-Enhanced Customer Service

Task/Conflict: Enhancing the customer service experience in the airline industry is crucial for customer satisfaction and loyalty. KLM aimed to provide immediate and effective assistance to its customers by integrating AI into their service channels.

Solution: KLM introduced an AI-powered chatbot, which provides 24/7 customer service across multiple languages. The chatbot handles inquiries about flight statuses, bookings, and baggage policies, offering quick and accurate responses. This AI solution helps manage customer interactions efficiently, especially during high-volume periods.

• Improved customer service efficiency and responsiveness.
• Increased customer satisfaction through accessible and timely support.
• AI chatbots can highly improve user service in high-demand industries.
• Effective communication through AI leads to better customer engagement and loyalty.

34. Novartis: AI in Drug Formulation

Task/Conflict: The pharmaceutical industry requires rapid development and formulation of new drugs to address emerging health challenges. Novartis aimed to use AI to expedite the drug formulation process, making it faster and more efficient.

Solution: Novartis applied AI to simulate and predict how different formulations might behave, speeding up the lab testing phase. AI algorithms analyze vast amounts of data to predict the stability and efficacy of drug formulations, allowing researchers to focus on the most promising candidates.

• Accelerated drug formulation and reduced time to market.
• Improved efficacy and stability of pharmaceutical products.
• AI can significantly shorten the drug development lifecycle.
• Predictive analytics in pharmaceutical research can lead to more effective treatments.

35. Shell: Optimizing Energy Resources with AI

Task/Conflict: In the energy sector, optimizing exploration and production processes for efficiency and sustainability is crucial. Shell sought to harness AI to enhance its oil and gas operations, making them more efficient and less environmentally impactful.

Solution: Shell implemented AI to analyze geological data and predict drilling outcomes, optimizing resource extraction. AI algorithms also adjust production processes in real time, improving operational proficiency and minimizing waste.

• Improved efficiency and sustainability in energy production.
• Reduced environmental impact through optimized resource management.
• Automation can enhance the effectiveness and sustainability of energy production.
• Real-time data analysis is crucial for optimizing exploration and production.

36. Procter & Gamble: AI in Consumer Goods Production

Task/Conflict: Maintaining operational efficiency and innovating product development are key challenges in the consumer goods industry. Procter & Gamble (P&G) aimed to integrate AI into their operations to enhance these aspects.

Solution: P&G employs AI to optimize its manufacturing processes and predict market trends for product development. AI-driven data analysis helps in managing supply chains and production lines efficiently, while AI in market research informs new product development, aligning with consumer needs.

• Enhanced operational efficacy and minimized production charges.
• Improved product innovation based on consumer data analysis.
• AI is crucial for optimizing manufacturing and supply chain processes.
• Data-driven product development leads to more successful market introductions.

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37. Disney: Creating Magical Experiences with AI

Task/Conflict: Enhancing visitor experiences in theme parks and resorts is a priority for Disney. They aimed to use AI to create personalized and magical experiences for guests, improving satisfaction and engagement.

Solution: Disney utilizes AI to manage park operations, personalize guest interactions, and enhance entertainment offerings. AI algorithms predict visitor traffic and optimize attractions and staff deployment. Personalized recommendations for rides, shows, and dining options enhance the guest experience by leveraging data from past visits and preferences.

• Enhanced guest satisfaction through personalized experiences.
• Improved operational efficiency in park management.
• AI can transform the entertainment and hospitality businesses by personalizing consumer experiences.
• Efficient management of operations using AI leads to improved customer satisfaction.

38. BMW: Reinventing Mobility with Autonomous Driving

Task/Conflict: The future of mobility heavily relies on the development of safe and efficient autonomous driving technologies. BMW aimed to dominate in this field by incorporating AI into their vehicles.

Solution: BMW is advancing its autonomous driving capabilities through AI, using sophisticated machine learning models to process data from vehicle sensors and external environments. This technology enables vehicles to make intelligent driving decisions, improving safety and passenger experiences.

• Pioneering advancements in autonomous vehicle technology.
• Enhanced safety and user experience in mobility.
• AI is crucial for the development of autonomous driving technologies.
• Safety and reliability are paramount in developing AI-driven vehicles.

39. Mastercard: Innovating Payment Solutions with AI

Task/Conflict: In the digital age, securing online transactions and enhancing payment processing efficiency are critical challenges. Mastercard aimed to leverage AI to address these issues, ensuring secure and seamless payment experiences for users.

Solution: Mastercard integrates AI to monitor transactions in real time, detect fraudulent activities, and enhance the efficiency of payment processing. AI algorithms analyze spending patterns and flag anomalies, while also optimizing authorization processes to reduce false declines and improve user satisfaction.

• Strengthened security and reduced fraud in transactions.
• Improved efficiency and user experience in payment processing.
• AI is necessary for securing and streamlining expense systems.
• Enhanced transaction processing efficiency leads to higher customer satisfaction.

40. AstraZeneca: Revolutionizing Oncology with AI

Task/Conflict: Advancing cancer research and developing effective treatments is a pressing challenge in healthcare. AstraZeneca aimed to utilize AI to revolutionize oncology research, enhancing the development and personalization of cancer treatments.

Solution: AstraZeneca employs AI to analyze genetic data and clinical trial results, identifying potential treatment pathways and personalizing therapies based on individual genetic profiles. This approach accelerates the development of targeted treatments and improves the efficacy of cancer therapies.

• Accelerated innovation and personalized treatment in oncology.
• Better survival chances for cancer patients.
• AI can significantly advance personalized medicine in oncology.
• Data-driven approaches in healthcare lead to better treatment outcomes and innovations.

Related: How can AI be used in Tennis?

Closing Thoughts

These 40 case studies illustrate the transformative power of AI across various industries. By addressing specific challenges and leveraging AI solutions, companies have achieved remarkable outcomes, from enhancing customer experiences to solving complex scientific problems. The key learnings from these cases underscore AI’s potential to revolutionize industries, improve efficiencies, and open up new possibilities for innovation and growth.

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