In 2019, Starbuck Corporation (Starbucks) launched Deep Brew, a platform driven by artificial intelligence (AI) designed to enhance personalization, optimize operations, and manage inventory across its vast global retail network. By leveraging AI, Starbucks aimed to improve customer engagement, streamline store efficiency, and strengthen its data-driven decision-making. However, the adoption of Deep Brew introduced significant challenges, including seamless technological integration across thousands of stores, employee adoption and training, data privacy concerns, and the ethical implications of AI-driven recommendations. As Starbucks scaled Deep Brew, the company faced a pivotal question: Could it successfully balance automation with human expertise, personalization with privacy, and profitability with fairness? Or would the risks of AI overreach threaten customer trust and long-term success?
星巴克Deep Brew：人工智能驱动的客户体验 (Starbucks Deep Brew: AI-Powered Customer Experience - Simplified Chinese version)
Derrick Neufeld
Product #:W49194
Supplier:Ivey
Discipline:Information Systems, Management Science
Setting:Japan, South Korea, United States, China, 2019
Your Price:$9.71
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Learning Objectives
- 阐释Deep Brew等人工智能驱动的个性化服务如何提供创新的客户互动策略
- 识别支撑Deep Brew成功及其融入星巴克零售运营的核心技术组件
- 评估星巴克企业数据分析平台如何提升跨部门数据管理与决策效率
- 分析星巴克面临的数据治理与隐私挑战及其合规策略
- 剖析人工智能驱动决策的伦理影响，包括算法偏见与公平性问题
After reading this case and working through the assignment questions and class discussion, students will be able to do the following:
- Explain how AI-driven personalization, like Deep Brew, offers innovative customer engagement strategies
- Identify the key technological components that enabled Deep Brew’s success and its integration into Starbucks retail operations
- Evaluate how Starbucks enterprise data analytics platform improved data management and decision-making across departments
- Assess the data governance and privacy challenges Starbucks faced and strategies for regulatory compliance
- Analyze the ethical implications of AI-driven decision-making, including concerns about algorithmic bias and fairness