Kim W. Chan, Mauborgne Renee, Ji Mi
Artificial intelligence (AI) has experienced explosive growth in recent years. With its applications increasingly integrated into production processes and commercial services, companies across industries are eager to adopt AI in their businesses. This has created an AI frenzy reminiscent of the internet boom of the late 1990s when many companies viewed the internet as a guaranteed path to business success, only to be proven wrong by reality. The ongoing fervor over AI also raises the following questions: Is AI truly a silver bullet? Can simply adopting AI provide a magic formula to create competitive advantages or even open new markets? Or are there deeper factors at play-factors beyond the technology itself-that determine whether AI adoption leads to meaningful outcomes? To answer these questions, this case examines several real-world examples of AI adoption-Senseye, Duolingo, Yunji Technology's hotel delivery robots, Quibi, Microsoft's Cortana, and Baidu's Apollo Go, presenting both successes and failures in an effort to uncover the factors that shaped their outcomes. In the meantime, the case also explores whether the widespread adoption of AI in business will inevitably disrupt existing markets and human jobs, as many believe.
Publication Date: May 2025
Product Number: IN2053-PDF-ENG
Length: 13 page(s) (27 minutes)
Languages: English
Strategy
Feng Zhu, Harold Zhu, Adina Wong
Headquartered in Singapore, DBS Bank, one of Asia's leading financial services groups, embarked on a multi-year digital transformation under CEO Piyush Gupta in 2014. It was then that DBS also began experimenting with AI to drive value for the business and customers. As the bank scaled the use of AI, it developed an internal P-U-R-E framework for ethical AI governance. In 2022, DBS started experimenting with Generative AI use cases. It had to consider how best to leverage its existing capabilities and adapt its governance frameworks in deploying Gen AI to drive additional value while managing emergent risks.
Publication Date: Aug 2024 (Revised May 2026)
Product Number: 625053-PDF-ENG
Length: 28 page(s) (58 minutes)
Languages: Chinese, English, Portuguese
Operations Management
Antoine Desir, Ville Satopaa, Eric Sibony, Laura Heely
White lies (inflated claims) cost the insurance industry billions of dollars every year. After investing heavily to automate workflows (from policy subscription to claims processing), digitization has ironically made fraud easier to commit and harder to catch. To an industry drowning in data and paying out millions per day for fraudulent claims, artificial intelligence and machine learning offer new hope. The case introduces what it calls "explainable AI" seen through the eyes of a senior operations executive at Shift, an insurtech unicorn company whose algorithm is used by global insurers such as Generali France and Mitsui Sumitomo to fight fraudulent claims. The focus is on strategy making (following a private equity funding round) and algorithm-level decisions. With an anonymized dataset of more than 10,000 claims and a guided coding exercise in statistical computing softwares R and Python, students are able to backtest their strategies on historical data. Beyond the exercise there is ample material to drive case discussion.
Publication Date: Jan 2023
Product Number: IN1889-PDF-ENG
Length: 11 page(s) (23 minutes)
Languages: English
Operations Management
Sunil Gupta, Rajiv Lal, Allison M. Ciechanover
In December 2022, Adobe CEO Shantanu Narayen faced a pivotal strategic decision due to the rapid rise of generative AI image models from OpenAI, Midjourney, and StabilityAI. Adobe, a leader in digital media and marketing software with a 40-year legacy of innovation and successful adaptation recognized GenAI as both an opportunity and a threat to its core business. Many creative professionals, a critical Adobe customer segment, expressed deep concerns over GenAI models trained through data mined from the Internet, raising ethical and trust-related issues. The case explores Narayen and his leadership team's deliberations: Should Adobe partner with or acquire an existing player, or build its own GenAI model? If Adobe chose to build, how might it responsibly train the model to protect its trusted relationship with enterprise and creative professional customers, while keeping its leadership position in the industry?
Publication Date: Apr 2025 (Revised Nov 2025)
Product Number: 525052-PDF-ENG
Length: 26 page(s) (54 minutes)
Languages: English
Strategy
Case Protagonist: Andrew Yan
Former Google Search product manager Andrew Yan is the co-founder and CEO of AthenaHQ, a Y Combinator-backed startup that helps brands monitor, protect, and optimize their visibility across conversational AI engines like ChatGPT, Claude, and Gemini. Founded in 2025, AthenaHQ built a platform that enables companies to track their share of voice, correct factual errors in AI responses, and manage their reputation across generative answer engines. The startup serves over 100 enterprise clients, including major consumer brands, B2B software companies, and marketing agencies. Facing a call with a global automaker whose newest vehicle has quietly vanished from chatbot recommendations, Yan evaluates the platform's expansion as he confronts core industry dilemmas: how AI disintermediates traditional marketing, who should own generative engine optimization inside an organization, and how to prove value on an unattributable channel.
Publication Date: Aug 2026
Product Number: 527016-PDF-ENG
Length: 23 page(s) (48 minutes)
Languages: English
Entrepreneurship
Tsedal Neeley
Felix Zhang, founder and CEO of the Beijing-based enterprise AI company Bairong, had a workforce of more than 1,200 human employees and 200,000 AI agents. These AI agents, carried job titles, KPIs, and reporting lines; together, they processed approximately 100 million customer interactions and facilitated up to $12 billion in transactions daily for more than 8,500 clients. Zhang's pioneering model rested on a Results-as-a- Service (RaaS) pricing structure that tied revenue directly to client outcomes, a decision he made against those who favored the conventional subscription model. Having proven the outcome pricing and business model across financial services, telecom, and insurance, and increasingly in international markets, Zhang faced a harder question: which parts of Bairong's advantage were durable as AI capability became cheaper and more widely available?
Publication Date: Aug 2026
Product Number: 427023-PDF-ENG
Length: 29 page(s) (1 hour, 1 minute)
Languages: English
Organizational Behavior
Protagonists: Doug McMillon, John Furner
When Doug McMillon stepped aside as Walmart's CEO in early 2026, the transition barely registered on Wall Street - a calm that stood in sharp contrast to the company he had inherited in February 2014. Walmart was then the world's largest retailer at roughly $476 billion in revenue, but same-store sales had stalled, e-commerce accounted for under 5% of revenue, and the market had written it off as a stodgy legacy retailer: too large to pivot and too distant from the digital-first consumer. By February 3, 2026, Walmart had crossed $1 trillion in market capitalisation, becoming the first traditional brick-and-mortar retailer to join Microsoft, Nvidia and Amazon in that club. The case traces how that reversal was engineered. It opens with the Everyday Low Price operating system Sam Walton built and follows its extension through David Glass, Lee Scott and Mike Duke, before examining McMillon's refusal of an 'Amazon-style' pivot in favour of a dual transformation - 'powering the present' while 'future-proofing the company'. Five decisions anchor the analysis: the $2.7 billion frontline wage and training investment of 2015 that triggered a near-20% single-day fall in the stock; the conversion of more than 4,600 U.S. stores into omnichannel fulfilment hubs enabling same-day delivery to about 95% of households; the $3.3 billion Jet.com acquisition, bought for speed and culture rather than revenue; the unplanned migration of $100,000-plus households to Walmart and the role of Walmart+; and the embedding of agentic AI through Sparky and Wally. Exhibits covering FY2015-FY2024 show revenue rising from $482 billion to $681 billion with gross margins held at 24-25%. The case closes with the handoff to John Furner and a 'Looking Ahead' section on AI disintermediation, margin pressure, succession, Amazon's response and marketplace execution risk, inviting students to judge whether the 'And' company resolves strategic tensions or merely defers them.
Publication Date: Jul 2026
Product Number: IMU072-PDF-ENG
Length: 16 page(s) (34 minutes)
Languages: English
Strategy
Case Analysis
Persistent Systems (hereinafter Persistent), a leading digital engineering firm, faces critical talent fulfillment challenges in 2025 due to regulatory changes and industry disruptions. The company's leadership aims to ensure resilient, cost-effective staffing while maintaining growth and ensuring seamless client delivery. To address these pressures, Persistent leverages agentic-AI-based systems, Fulfillment-as-a-Service (FaaS), internal gig work (Gig Garage), and advanced analytics to optimize recruitment, onboarding, and talent management practices. These innovations reduce the cost per hire, improve diversity and employee engagement, and increase the utilization of billable talent. However, changes in US visa regulations restricting entry for foreign workers and increased volatility in technology skills intensified the war for talent, leading to moonlighting by highly skilled workers. All these factors were affecting talent availability for Persistent, which needed to find innovative solutions to ensure talent availability and support its growth initiatives.
Publication Date: Jun 2026
Product Number: ISB553-PDF-ENG
Length: 15 page(s) (32 minutes)
Languages: English
Human Resource Management
Case Analysis
This case examines BMW Group's transformation toward an "agentic enterprise" as generative AI and autonomous agents begin to reshape how work is organized across the company. Following the internal adoption of an AI platform, BMW achieved significant productivity gains in areas such as procurement, where tools like the Tender Assistant and Offer Analyst reduced manual effort and improved efficiency. However, leadership recognized that these "intelligent islands" delivered only localized improvements while remaining disconnected from one another and from surrounding enterprise systems. The case contrasts two potential strategic paths: continuing incremental deployment of AI within the existing operating model (Operating Model 1.0), or committing to a more fundamental system-level redesign of end-to-end workflows in which AI agents are embedded into streamlined, autonomous processes (Operating Model 2.0). This decision is complicated by concurrent pressures, including BMW's capital-intensive Neue Klasse transformation, intensifying global competition (especially from China), and an industry shift toward electrified and automated driving.
Publication Date: Jun 2026
Product Number: 726494-PDF-ENG
Length: 22 page(s) (46 minutes)
Languages: English
Strategy
Protagonist: Rohan Murty
In 2026, Rohan Murty, cofounder and CEO of Workfabric, faced a strategic decision about the future direction of his company. Workfabric had developed a platform that learned how employees worked and leveraged that behavioral data into organizational context for AI systems. Building on this foundation, the company created AI twins-AI agents capable of simulating how specific employees prioritized tasks, made decisions, and collaborated. Early deployments demonstrated substantial gains in productivity, revenue generation, and AI effectiveness. Yet as customer demand accelerated, Murty faced an important choice: should Workfabric focus on selling ContextFabric as enterprise infrastructure, or should it build application-layer products such as AI twins and organizational simulations? The case explores emerging questions around context engineering, enterprise AI adoption, pricing strategy, organizational knowledge, privacy, and the future relationship between humans and AI systems.
Publication Date: Jun 2026
Product Number: 126087-PDF-ENG
Length: 12 page(s) (25 minutes)
Languages: English
Strategy
Case Analysis
Set in 2025, this case examines Bank of America's (BofA) development and adoption of Erica, an artificial intelligence (AI) powered virtual assistant across its multiple business lines. Developed in-house and launched in 2018, Erica by 2025 had evolved from addressing basic account queries to enabling transactions, issuing fraud alerts, and delivering proactive and personalised financial insights to over nearly 50 million active users. BofA's first-mover lead, large scale proprietary data, sustained investment, in-built offramps to human support, and disciplined AI governance had led Erica to become a strategic asset. The bank expanded it beyond retail banking into its wealth-management businesses, supporting select functions such as proactive alerts, intelligent scheduling, document search, and workflow assistance. By Q3 2025, Erica supported three billion interactions and customer satisfaction was at an all-time high, service cost had significantly lowered, while employee productivity had increased manifolds. Yet, as BofA prepared to establish Erica as an enterprise-wide "AI super-platform" with deeper inroads into its commercial banking and wealth management businesses, many concerns emerged. Wealth management, as a "high-trust, high-touch" business, represented a fundamentally different environment from retail banking with clients expecting tailored financial advice, discretion, and risk assessments. Moreover, interactions typically involved multiple stakeholders and multi-product portfolios along with higher regulatory exposure with more stringent fiduciary standards and privacy obligations. Did Erica's existing architecture provide a strong enough foundation to expand into wealth management or was a fundamentally different model required? At the same time, the rise of generative AI (gen-AI) had created a new challenge. Could BofA use gen-AI to enhance Erica without compromising on its accuracy and reliability?
Publication Date: Apr 2026
Product Number: 282SMU-PDF-ENG
Length: 17 page(s) (36 minutes)
Languages: English
Strategy
Case Analysis
This case explores PepsiCo's enterprise-wide artificial intelligence (AI) transformation, highlighting how the company built a strong digital foundation and scaled AI across marketing, supply chain, finance, and governance. Beginning with a global cloud and data modernization effort, PepsiCo established the infrastructure and governance needed to treat data as a strategic asset. It then applied AI to drive consumer insights (via Pepviz™), hyper-personalized marketing, supply chain automation, and predictive analytics. The case also examines the financial trade-offs between CapEx-heavy automation and OpEx-driven generative AI initiatives, as well as the strategic and ethical challenges of deploying AI responsibly at scale.
Publication Date: Apr 2026
Product Number: W48500-PDF-ENG
Length: 15 page(s) (32 minutes)
Languages: English
General Management
Case Analysis
This case examines the evolution of mobile payments in China, focusing on the shift from cash transactions to cashless payments. While this transformation has driven small and medium-sized enterprises (SMEs) toward digitalization, many face challenges due to cost and technological limitations. AIMT Co., Ltd. (hereinafter referred to as "AIMT") swiftly seized market opportunities. By offering aggregated payment solutions and premium services, the company not only supported SMEs in their digital transition but also empowered them with added business value. This case encourages students to explore how AIMT capitalizes on market opportunities and how it can continue leveraging the application of big data and AI tools to sustain growth and explore new avenues of expansion.
Publication Date: Apr 2026
Product Number: CB0395-PDF-ENG
Length: 20 page(s) (42 minutes)
Languages: English
Marketing
Protagonists: Ted Sarandos, Greg Peters
The case protagonists are Netflix co-CEOs Ted Sarandos and Greg Peters as they confront the company's most consequential corporate strategy decision since the pivot to streaming. After two decades of disciplined organic growth and selective licensing, Netflix launches an $82.7 billion (enterprise value) bid to acquire the studio and streaming assets of Warner Bros. Discovery. The deal is structured to leave legacy cable networks behind through a planned spin-off. However, it still forces Netflix to grapple with a jarring new reality: integrating a century-old Hollywood studio system and iconic intellectual property (Harry Potter, DC, HBO) into a platform built on speed, data, and algorithmic discovery. - - - The bid triggered a hostile counterattack from rival bidder Paramount Skydance, backed by the Ellison family and sovereign wealth funds. With market odds split and regulatory uncertainty stretching toward 2027, Netflix must prepare in parallel for two sharply divergent futures: an integration scenario that tests whether Netflix can combine tech-platform logic with legacy creative institutions, and a content cliff scenario in which a consolidated Paramount-Warner competitor pulls back licensed libraries that Netflix's data show are critical for retention. The case frames competition not only as a battle for subscription dollars against Disney and Amazon, but as a broader battle for time and attention as YouTube and TikTok reshape consumption habits among Gen Z and Gen Alpha. A central theme is the "Suits Effect": Netflix's ability to unlock disproportionate value from undervalued library content through personalized recommendations and global distribution, raising the provocative question of whether platform power, not content creation, has become the primary engine of value in modern media. This case is designed for courses in strategic management, corporate strategy, tech/digital strategy, platform competition, media and entertainment, innovation, and organizational behavior.
Publication Date: Mar 2026
Product Number: MH0087-PDF-ENG
Length: 17 page(s) (36 minutes)
Languages: English
Strategy
Protagonist: Josh D'Amaro
The case protagonist is Josh D'Amaro, newly appointed CEO of The Walt Disney Company, moments before a critical Global Town Hall with all employees. Since Bob Iger's return in 2022, Disney's stock has underperformed the S&P 500 by over 90 percentage points, and its market capitalization has collapsed from a peak of $370 billion to $220 billion. The central conflict is the breakdown of Disney's famed flywheel, the strategic logic where intellectual property flows seamlessly from studios to parks to merchandise. Students must grapple with deep structural fractures: the rapid collapse of the linear TV cash cow (ESPN/ABC), the struggle to achieve profitability in a saturated streaming market, and a subsidy trap in which aggressive pricing at theme parks risks eroding the brand's core equity to fund losses elsewhere. - Facing aggressive activist pressure to break up the company, D'Amaro must choose between two divergent futures. Option 1 (Integration) doubles down on the conglomerate model, betting on a massive tech-forward pivot that includes a controversial $1 billion partnership with OpenAI to leverage Disney's totems (IP) for generative AI. Option 2 (Disintegration) acknowledges that the synergy era is over and proposes spinning off linear networks and pivoting from a closed IP platform strategy (walled garden) to a high-margin, arms-dealer content model. The case forces students to evaluate whether Disney's historic diversification premium has turned into a permanent conglomerate discount. It explores the tension between preserving creative magic and adopting AI asset management, the battle for attention against algorithmic platforms (TikTok, YouTube), and the succession trap that complicated the transition following the company's legendary 75-year-old architect. This case is designed for courses in strategic management, corporate strategy, technology strategy, media and entertainment, corporate governance, organizational behavior, and organizational design.
Publication Date: Mar 2026
Product Number: MH0088-PDF-ENG
Length: 19 page(s) (40 minutes)
Strategy
Protagonist: Sunand Menon
In September 2025, Sunand Menon, an Operating Partner at biotechnology venture capital firm Flagship Pioneering and newly appointed Chairman of Flagship's portfolio company CIBO Technologies, faces a pivotal career decision. After conducting an intensive review of CIBO at the request of one of its co-founders and outgoing Chairman, Menon is unexpectedly asked to step in as acting CEO and lead the company's growth strategy. The opportunity fits a three-decade pattern of "leaps" across industries-from consumer goods and engineering to financial services and media to industrials, technology, life sciences, and now agriculture-animated by Menon's belief in curiosity, comfort with change, and continuous learning. Yet Menon has never served as a CEO before, and taking on the leadership role in the ag-tech space, unfamiliar territory, makes him feel both "nervous and excited." As Menon reflects on his non-linear career journey, he must decide whether to take on the "completely different job" of leading CIBO through its next phase of transformation or focus instead on scaling strategic relationships and AI ventures at Flagship-and, more broadly, consider what the optimal role of the human leader should be in an increasingly AI-informed world.
Publication Date: Mar 2026 (Revised Jun 2026)
Product Number: 426055-PDF-ENG
Length: 19 page(s) (40 minutes)
Languages: English
Organizational Behavior
Meg Rithmire, Bradley Holland
AltanaAI was founded in 2019 with a thesis that globalization was under terrific scrutiny, if not existential threat, and that better data could help companies and governments manage supply chains. Through its "Altana Atlas" platform, the company focused its first few years on using artificial intelligence to billions of data points to help governments, logistics providers, and major enterprises manage compliance, risk assessment, carbon tracking, and supply chain optimization. Notable customers included Boston Scientific, Maersk, L.L.Bean, U.S. Customs and Border Protection, and the UK Department of Business and Trade. In July 2024, Altana raised $200 million in Series C funding achieving a $1 billion valuation. But the Series C funding also brought a pivot, as the company under founder Evan Smith moved to develop a "product passport," a digital product identity that would document the entire supply chain of a product at a border.
Publication Date: Mar 2026
Product Number: 726032-PDF-ENG
Length: 19 page(s) (40 minutes)
Languages: English
Business & Government Relations
Aurelia Durand, Ananya Yadav, Muhammad Aljukhadar
Set in India, this case study examines how two leading beauty brands - Lakmé, a domestic brand acquired by Unilever, and Maybelline, owned by L'Oréal - leveraged AI technology in the fast-growing Indian beauty market. The case explores the strategic tension between globalization and localization, highlighting the pivotal role of AI in operationalizing these competing approaches. Maybelline targeted India's young, trend-conscious demographic by integrating AI-powered tools such as virtual beauty studios, augmented reality filters, and AI avatars. In contrast, Lakmé focused on personalized beauty solutions designed for Indian skin tones and preferences. Lakmé adopted AI more slowly, using it primarily to enhance the customer experience with features like skin analysis and virtual makeup try-ons. As AI continues to influence consumer behaviour, Lakmé's and Maybelline's contrasting approaches highlight the importance of balancing innovation with local relevance. This raises the question of whether Lakmé should accelerate its AI adoption to remain competitive.
Publication Date: Sep 2025
Product Number: HEC417-PDF-ENG
Length: 15 page(s) (32 minutes)
Languages: English, French
Marketing
Antonio Moreno, Tonia Labruyere
In October 2025, Swiss Post Logistics Services faces a strategic inflection point. While Swiss Post continues to rank among the world's most reliable postal operators, declining letter volumes, rising parcel demand, aging infrastructure, and stringent universal service obligations are placing mounting pressure on costs and long-term viability. Swiss Post is evaluating fundamental options for redesigning the logistics network-including consolidating or closing sorting centers, rethinking service levels, and investing in automation and AI-without assuming regulatory relief. The case explores how a historically process-driven, employee-centric organization can adapt its operations, technology, and leadership culture amid profound structural change. Students must assess trade-offs between service quality, cost efficiency, workforce implications, and political constraints, and recommend how Swiss Post can reinvent its logistics operations while preserving its public-service mandate and premium brand reputation.
Publication Date: Feb 2026 (Revised Jun 2026)
Product Number: 626064-PDF-ENG
Length: 22 page(s) (46 minutes)
Languages: English
Operations Management
Shunyuan Zhang, Gizem Cihan Dincsoy
Founded in 1994 and operating as Caterpillar's exclusive dealer in Turkiye and Central Asia, Borusan Cat had relied on equipment sales in a relationship-driven market. By 2017, rising commoditization and shrinking margins prompted CEO Ozgur Gunaydın to lead a digital transformation centered on predictive maintenance and proactive service. After early internal AI tools faced resistance from the salesforce, the company launched Boom360, a customer-facing platform integrating diagnostics, service requests, parts ordering, and later e-commerce. By 2025, adoption was strong among smaller contractors but remained low among key accounts that generated most of the company's revenue. Preparing for the 2026 strategy cycle, Gunaydın faced a critical question: Should Borusan Cat focus on converting its large, reluctant customers, or double down on digitally receptive segments? More broadly, how could Boom360 drive the leap from 45% to 60-70% service revenue, while sustaining Borusan Cat's growth?
Publication Date: Feb 2026
Product Number: 526023-PDF-ENG
Length: 20 page(s) (42 minutes)
Languages: English
Marketing
Rembrand Koning, Hyunjin Kim, Nicole Tempest Keller
Fazeshift, founded in 2024, was an AI-native platform built to automate accounts receivable (AR), a function long dominated by spreadsheets, emails, PDFs, and manual work. Drawing on firsthand experience from a prior startup, the founders developed AI agents that stitched together existing finance systems and handled the unstructured, exception-heavy tasks traditional software could not. After joining Y Combinator's Summer 2024 batch, Fazeshift gained rapid traction, growing to a ten-person team, more than 30 customers, and 20%-30% monthly ARR growth by 2025. As the company prepared to scale, its founders faced a critical question: how should an AI product that replaces labor and accelerates cash flow be priced-by usage, headcount replaced, subscription, or value delivered? Beneath the decision lay a deeper question facing every AI startup: When AI can rapidly learn and automate complex workflows, how should that value be priced-and who decides what it is worth?
Publication Date: Feb 2026
Product Number: 826181-PDF-ENG
Length: 16 page(s) (34 minutes)
Languages: English
Entrepreneurship
Iavor Bojinov, Hila Lifshitz, Francois Candelon, Emer Moloney
This case follows Jean-Philippe Couturier, co-founder and CEO of Whoz, as he reflects on the company's rapid growth and an unexpected strategic dilemma. Whoz had become a leading provider of an AI-driven staffing and talent management solution, while internally developing Coday, an agentic operating system that significantly transformed how work was done across the organization. As interest in Coday grew both inside and outside the firm, Couturier faced a critical choice: spin it off, integrate it into the core Whoz product, or open-source it. The decision raised fundamental questions about product strategy, organizational focus, and the future role of AI agents in white-collar work.
Publication Date: Feb 2026
Product Number: 626053-PDF-ENG
Length: 21 page(s) (44 minutes)
Languages: English
Operations Management
ShiaoFong Yin, Tamara Dokic
As climate change and resource constraints challenge the resilience of global food systems, new technologies are transforming the agricultural value chain from seed to shelf. This case examines the emergence of gene editing, precision agriculture, and vertical farming as responses to mounting pressure for productivity and sustainability. Each promises higher yields, reduced inputs, and localized production, yet faces significant barriers-ranging from regulatory hurdles and high implementation costs to public skepticism and uneven access across markets. By tracing how innovation diffuses through farmers, suppliers, and investors, the case invites students to evaluate both the potential and the limitations of technology-driven solutions in building a more sustainable and equitable food system.
Publication Date: Feb 2026
Product Number: SCG609-PDF-ENG
Length: 31 page(s) (1 hour, 5 minutes)
Languages: English
Entrepreneurship
Julian De Freitas, Elie Ofek, Mengjie Cheng
This case examines how the Harvard Art Museums (HAM) considered using generative artificial intelligence in a marketing campaign to increase visitor engagement and membership. Looking to boost attendance and appeal to a younger audience base, and facing intensified competition for leisure time, HAM's marketing team explored animating a canonical artwork using AI-generated avatars as part of a digital campaign. While the approach promised novelty and attention, it raised internal concerns about brand authenticity, interpretive authority, donor relations, and reputational risk-particularly given broader cultural anxieties surrounding AI and the museums' association with the Harvard brand. Students must evaluate whether and how HAM should proceed, including which audiences to target, what form the AI experience should take, which channels to use, which artwork to animate, and how success should be measured. The case highlights tensions between innovation and brand stewardship in cultural marketing, organizational barriers to new technology adoption, and the strategic use of emerging technologies in brand management.
Publication Date: Feb 2026 (Revised Mar 2026)
Product Number: 526051-PDF-ENG
Length: 32 page(s) (1 hour, 7 minutes)
Languages: English
Marketing
Rajiv Lal, Alexis Lefort
This case examines how Unilever undertook a broad marketing transformation as artificial intelligence (AI) and social platforms reshaped how consumers discovered, evaluated, and engaged with brands. Beginning in late 2022, Unilever's leadership confronted the limits of its traditional, broadcast-driven marketing model and sought to adapt to an environment in which brand meaning was increasingly co-created with consumers and creators in real time. The case follows senior executives as they rethought marketing strategy, organizational design, talent profiles, agency relationships, and performance metrics, particularly within the Beauty & Wellbeing business group, which became a testing ground for a social-first, AI-enabled approach. It highlights how AI tools were integrated into content creation, optimization, and insight generation, and examines the continued role of human judgment, creativity, and cultural understanding in brand building. The case invites discussion about the strategic, organizational, and measurement challenges of scaling AI-enabled marketing across a global consumer packaged goods company, as well as the broader implications for competitive advantage in an environment shaped by rapid technological change and shifting consumer behavior.
Publication Date: Jan 2026
Product Number: 526032-PDF-ENG
Length: 19 page(s) (40 minutes)
Languages: English
Marketing
Mohanbir Sawhney
In June 2025, Walmart launched Sparky, its agentic AI shopping assistant designed to replace keyword search with a true service experience. Instead of browsing product lists, customers could state goals, such as hosting a cookout or planning weeknight dinners, and Sparky would plan, reason, and act to deliver a complete solution. Sparky could compress the consumer journey from intent to fulfillment by working across Walmart's retail stack (catalog, inventory, pricing, fulfillment, services). Sparky represented a strategic bet for Walmart as it moved to redefine shopping search journeys, strengthen customer loyalty, and unlock new monetization models through AI. The launch came at a critical juncture. Walmart faced intensifying competition from Amazon's Rufus and Instacart's Ask Instacart, both of which were embedding generative AI into their retail operations. Walmart's differentiation rested on two pillars: agentic depth--multi-step planning and action rather than single-shot answers--and openness, by designing Sparky to interoperate with other agents rather than locking customers into Walmart alone. The case places students in the shoes of Walmart's executive team as they consider three strategic dilemmas. Should Sparky prioritize customer experience or retail media monetization through advertising revenues? How far should Walmart go in personalization without making customers uncomfortable about privacy? Should Walmart build proprietary AI capabilities to maintain control over and differentiation in its technology stack, or should it partner for speed and focus?
Publication Date: Jan 2026
Product Number: KE1435-PDF-ENG
Length: 23 page(s) (48 minutes)
Languages: English
Marketing
Kannan Ramaswamy, William E Youngdahl
The case focuses on the precision agriculture industry, an emerging combination of technologies and solutions targeted at improving the economics and sustainability of the agricultural industry. Within the context of the industry transformation, the case specifically examines the strategic decisions made by John Deere, the leading agricultural machinery and equipment manufacturer in the world as it charts its course towards digitally transforming the company. The case provides an ideal platform to explore a broad spectrum of topics ranging from ecosystems and platforms, defining and delivering a unique customer value proposition, and leveraging contemporary tools such as artificial intelligence (AI), internet of things (IoT), data analytics, and deep learning to revolutionize the old and tradition-bound agricultural business. John Deere has introduced a set of leading-edge tractors, sprayers, and farm equipment that promise to personalize agriculture at the plant level. The technologies embedded in its tractors along with cloud-based analytical capabilities provide farmers with real-time actionable data and advice. As it launches a new way of farming, the company confronts skeptical customers who think the equipment is too expensive and hard to repair, and the data streams too complex to comprehend and apply to their field-level decisions. There are other issues relating to data privacy and ecosystem management that Deere confronts as it aspires to emphasize its software platform as the way to the future. The reader is asked to assess the way the Leap Ambitions strategy has played out in the marketplace and suggest course corrections as needed to guarantee that Deere can ensure it derives the maximum value from the ecosystem.
Publication Date: Nov 2023
Product Number: TB0702-PDF-ENG
Length: 10 page(s) (21 minutes)
Languages: English
Strategy
Kevin McTigue, Theo Anderson
Machine learning has been used to create value in various ways across a broad swath of industries. In this case, students will explore uses for machine learning in the context of the launch of the Disney+ streaming service in November 2019. At the time of the case, Disney already operated two streaming platforms, Hulu and ESPN+. Its new streaming service would launch with an archive of roughly 7,500 TV episodes and 500 films, including wildly popular titles from Marvel, Star Wars, and Pixar. Yet Disney+ would be entering an increasingly competitive industry dominated by Netflix. Since its pivot from mail-order video to streaming in the early 2000s, Netflix had extensively used machine-learning algorithms to optimize customer experience and retention. In this case, students will assume the (fictitious) role of Margaret Gupta, a senior data scientist, as she ideates machine-learning use cases for Disney's management team. In addition to background information on Disney and Netflix, the case provides students with basic information on use cases and data sources for machine learning. The overarching goal is to give students a general understanding of how machine learning works, learn to recognize potential use cases from a managerial lens, the data required to fuel it, and the possible sources of bias that can arise from that data.
Publication Date: Jan 2023
Product Number: KE1251-PDF-ENG
Length: 13 page(s) (27 minutes)
Languages: English
Marketing
Jill Avery, Ayelet Israeli, Emma von Maur
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women's fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on her style preferences, size, and budget. When a woman downloaded THE YES app, she embarked upon an interactive shopping journey that leveraged a fun, easy, gamified user experience (UX) reminiscent of dating apps to collect a stream of data from her that could be used to dynamically curate an ever-changing product assortment just for her. The founders had to decide whether to continue developing the UX and the algorithm to deliver on the company's customer value proposition, or to focus on new customer acquisition via paid media, with the idea that more users on the app improve the algorithm's performance. Several ideas for further monetizing the platform were on the table, including the development of social shopping features to make the shopping experience more viral, the design of an influencer program to bring fashion influencer voices onto the platform, and the construction of a customer loyalty program. Should THE YES invest in improving the algorithm, enhancing the UX with new functionality, or on customer acquisition?
Publication Date: Jan 2021 (Revised Mar 2021)
Product Number: 521070-PDF-ENG
Length: 29 page(s) (1 hour, 1 minute)
Languages: English
Marketing