How Starbucks Is Using Data And AI To Deliver Joy And Connection To Its Customers

0 0 7 minutes read

📝 usncan Note: How Starbucks Is Using Data And AI To Deliver Joy And Connection To Its Customers

Disclaimer: This content has been prepared based on currently trending topics to increase your awareness.

How does a venerable American brand known for creating the ultimate coffee experience tap into the innovative power of data and AI to create the next generation of customer service?

This is the question I posed to Bhagyesh Phanse, chief data and analytics officer at Starbucks, the American multinational chain of coffeehouses and roasteries originally founded at Seattle’s historic Pike Place Market in 1971.

Ever since its founding, Starbucks has distinguished itself by its capacity for innovation. The company opened its first store, offering fresh-roasted coffee beans, tea and spices from around the world. Under the leadership of Howard Schultz, who first served as chief executive officer of Starbucks from 1986 to 2000, the company expanded into coffeehouses serving premium espresso-based drinks. Starbucks would ultimately grow to serve millions of customers globally, becoming a part of the fabric of neighborhoods across the world. Today, Starbucks operates over 41,000 stores in more than 87 markets, with over 17,000 located in the United States.

Data & AI leadership at Starbucks

As the chief data and analytics officer at Starbucks, Bhagyesh Phanse brings extensive experience in the application of data and analytics in retail businesses, including leadership tenures at CVS Health and Macy’s. With a mission to strengthen how Starbucks uses data, analytics, and AI at scale, Phanse and his team are focused on delivering measurable and immediate business impact while building for the future. His multidisciplinary team spans strategy, engineering, technology, platforms, tools, data science, and analytics functions, with a remit that includes:

Improving data quality, literacy, and access across the enterprise.
Scaling analytics and data science to drive performance and innovation.
Accelerating AI adoption, including Generative AI and Agentic systems, in ways that are responsible, human-centered, and aligned with Starbucks values.

Data, analytics, and AI are seen as strategic enablers of growth, differentiation, and operational excellence for Starbucks. Phanse explains, “At Starbucks, data, analytics, and AI play a critical role in advancing our mission to deliver meaningful moments of joy and connection for our customers and partners.” He continues, “This belief is deeply embedded across the Starbucks leadership team, with senior leaders actively championing initiatives that unlock value for partners, customers, and the business.”

Phanse notes, “We operate at the intersection of two rapidly evolving spaces: technology and consumer retail. That makes our work both dynamic and deeply relevant, requiring us to move at the pace of innovation while staying grounded in the needs of our partners and customers.” He adds, “Data and AI will be foundational to how we scale innovation, empower our partners, and deliver the elevated experiences our customers expect.”

Delivering business value from Starbucks data and AI investments

Starbucks recognizes that data, analytics, and AI are strategic functional assets essential to how the company operates, innovates, and grows. Starbucks has a track record of delivering value with data and analytics through its proprietary AI platform, DeepBrew. To continue to deliver business value from its data & AI investments, Starbucks is focusing on three foundational pillars:

Clarity of purpose—aligning teams on what matters most to customers and the business.
Trusted access—making high-quality, secure data available across the organization.
Impact measurement—using transparent scorecards to evaluate outcomes and guide decisions.

To support these foundational pillars, Starbucks has introduced impact scorecards for major initiatives. These scorecards track tangible outcomes—whether it’s improving a key operational metric, deepening customer engagement, or enabling faster, more confident decision-making. They also help Starbucks learn, iterate, and celebrate success stories across the organization.

Starbucks data and analytics leaders help prioritize efforts based on value and complexity, taking a portfolio approach that tracks value and guides where and how the business invests. At the core of this approach is a simple question: Are investments helping the company focus, decide, and deliver impact at scale?

As an example, Starbucks is preparing its data ecosystem for the next generation of AI—including Generative and Agentic systems. The company is building a community of embedded analysts and decision scientists who work closely with functional leaders to drive business impact. Phanse notes, “Within data and analytics, we apply a consistent prioritization framework to evaluate which products to build—balancing potential value with investment complexity.”

Phanse elaborates, “The primary function of data and AI within Starbucks is to anticipate needs, simplify work, and personalize customer experiences – whether that’s through smarter store operations, more intuitive digital journeys, or tools that empower our partners to deliver exceptional service.” He adds, “Ultimately, our goal is to scale what’s effective, pivot when needed, and stay accountable to outcomes—all in service of elevating the human experience and strengthening the core of our business.”

Innovating with data and AI at Starbucks

Starbucks commitment to innovation is manifested through continuous learning. Phanse explains, “Starbucks is integrating data and AI into the daily rhythm of the business—helping teams focus on what matters most, from the front of the coffeehouse to the back office.”

Starbucks data and analytics function plays a critical role in shaping enterprise AI and Generative AI capabilities in close partnership with engineering teams. The team supports “test-and-learn” initiatives. Phanse explains, “We are embedding data, analytics, and AI into the way we operate—ensuring these capabilities support our core promise of handcrafted beverages, welcoming coffeehouses, and meaningful human connection.”

Starbucks Chief Technology Officer, Deb Hall Lefevre, has been a strong advocate for building human-centered, operationally grounded tools. This includes investments in intelligent forecasting to better align inventory and labor with demand, and data-driven platforms that empower coffeehouse leaders and support teams to make faster, more informed decisions.

Starbucks is applying machine learning to personalize the digital ordering experience in the app. This helps customers discover items that match their preferences and can inspire them to try new or limited-time offerings.” Green Dot Assist is a generative AI-powered assistant deployed on coffeehouse tablets to support partners in real time with conversational answers on drink recipes, seasonal ingredients, or troubleshooting equipment issues. Built by Starbucks, this platform lays the groundwork for scalable, responsible innovation.

Data and AI innovation is also used in support of the Back to Starbucks strategy. Phanse comments, “These efforts reflect our broader ambition: to embed intelligence into the rhythm of the business. By doing so, we’re enabling more personalized, efficient, and meaningful experiences across every touchpoint.” He adds, “We partner across Starbucks teams to scale platforms and practices that bring analytics and AI into the organization—from digital to retail to enterprise functions—ensuring that innovation is not only scalable, but also responsible and aligned with our mission to uplift the human connection.”

Transforming the Starbucks customer experience through Data and AI

Starbucks is focused on applying data and AI to enable strategic decision-making through customer-centric, data-driven products that directly support the core brand promise—elevating handcrafted beverages, enhancing the coffeehouse experience, and strengthening partner and customer connection.

To ensure focus and impact, Starbucks has established a set of ten high-priority initiatives designed to deliver value today while setting the foundation for long-term future growth. These efforts are aligned to operational and customer experience goals. Key initiatives include:

Staffing models that support Green Apron Service by ensuring the right partners are in the right places at the right times.
Channel Prioritization Algorithms that optimize service time and customer experience.
Smarter food availability forecasting models to improve inventory accuracy for customers and partners.
Targeted Marketing Campaigns: Leveraging machine learning and recommendation algorithms to personalize digital ordering experiences, enabling personalization, and emotional connection.
Algorithmic models and external data partnerships to optimize pricing strategies.
Voice of Customer and Partner listening tools that help Starbucks understand and respond more effectively to feedback and sentiment.
Enhanced analytics for store development and operations, enabling better tracking, analyzing, and acting on where and how Starbucks shows up in local communities, including advanced Test-and-learn analytics to uncover key performance drivers.
Enterprise data readiness for Generative and Agentic AI, ensuring Starbucks infrastructure can support next-generation capabilities in a secure, scalable, and actionable way.

Phanse comments, “These are just a few examples of how we’re aligning data and AI to the core moments that define our brand and differentiate the Starbucks Experience., notes Phanse. “We’re proud of the role data and analytics is playing in helping us deliver on our Back to Starbucks strategy—with intelligence embedded, outcomes measured, and people at the center.” Outcomes include:

Equipping retail and support partners with actionable insights.
Optimizing service models like Green Apron Service for speed and personalization.
Enhancing mobile ordering and digital engagement.
Informing innovation and product development.
Simplifying operational complexity across the supply chain.

Data and AI are foundational to how Starbucks is scaling innovation, empowering partners, and delivering the elevated experiences that customers expect. Phanse comments, “Together, these initiatives reflect our commitment to using data and AI to simplify operations, empower partners, and deliver elevated customer experiences—core to our transformation and the next chapter.” He concludes, “When we’re at our best, data and AI help us in many ways.”

Looking ahead to a Data and AI future with Starbucks

Starbucks is building a future where data and AI are integrated into all aspects of customer experience. A cornerstone of Starbucks data and AI efforts will be a continuing commitment to the responsible application of AI — ethical use of data and AI that ensures innovation is always in service of partners, customers, and communities.

Responsible AI means ensuring high-quality, well-governed data, embedding human-in-the-loop practices, and applying clear principles that guide model development and deployment. Phanse comments, “We believe AI should enhance—not replace—the human experience. This means designing systems that support thoughtful decision-making, reduce friction for partners, and preserve the trust our customers place in Starbucks.”

Looking ahead to 2026, Starbucks will be launching the Grow Report, a tool that will help coffeehouse leaders zero in on key drivers of sales growth. Phanse notes, “Our north star remains clear: using data and AI to elevate the human experience—removing friction, enabling focus, and amplifying what makes Starbucks unique. Our goal is to embed data and AI into the rhythm of our business—creating a win-win environment where data empowers partners, enhances decision-making, and drives meaningful outcomes.”

Phanse sums up, “I’m proud of the progress we’ve made, not only in our tools and systems, but in how our teams make decisions, serve customers, and bring the Starbucks Experience to life. We measure success by how well our data, analytics, and AI investments enable better outcomes for our customers, partners, and the business. As AI evolves, so does our approach.” He concludes, “We’re just getting started—and we’re energized by the opportunity to help shape what’s next!”