How Diageo Scales Enterprise AI with a Governed Semantic Layer

Diageo is a global leader in premium spirits with a portfolio that includes Johnnie Walker, Guinness, Tanqueray, Don Julio, and many more iconic brands.  

With over 29,000 employees across more than 180 countries, Diageo operates at true enterprise scale. Its D&T team relies on data-driven insights to support decision-making and maintain performance across global markets. 

The bulk of Diageo’s data analytics strategy is built upon three pillars:  

• Consumers: Individuals interacting with Diageo’s digital experiences across applications, websites, and targeted campaigns
• Customers: Restaurants, retailers, bars, and physical locations generating data across promotions, pricing, and sales performance
• Supply Action: The “grain-to-glass” concept enabling Diageo to produce, package, and distribute products globally.

At every stage, data is generated continuously and consolidated within the Diageo data hub, a centralized data warehouse. But consolidation isn’t enough. 

At enterprise level, the real challenge is coordination and consistency. 

With operations spanning more than 180 countries, Diageo relies on a wide range of applications, BI tools, and systems across markets. Each region generates insights in its own way, often using different tools and definitions. 

Without alignment, this creates a risk: the same metric can be interpreted in multiple ways across the business. Diageo’s priority is to establish a shared, governed data foundation that enables seamless communication and consistent decision-making across markets. 

To achieve this, the company is making a fundamental shift in how it approaches data. 

To support enterprise AI at scale, Diageo is shifting from a traditional project-driven approach to a product-driven operating model. 

The reason? Consumer trends are evolving too quickly for long, linear delivery cycles. Organizations can no longer afford to spend 15 to 18 months building a solution before seeing results.  

Instead, Diageo operates in continuous cycles of ideation, scoping, prototyping, piloting, and scaling — enabling faster adaptation and more responsive decision-making. 

This transition turns data products into reusable, scalable, and continuously evolving assets that are managed and designed to deliver business impact over time. 

It also lays the foundation for governed data, consistent definitions, and scalable analytics, all of which are critical for enabling reliable enterprise AI. 

To scale enterprise AI reliably, Diageo is focused on operationalizing AI through four strategic focus areas. These priorities define how AI is implemented across the enterprise, ensuring that innovation is grounded in consistency, governance, and real business impact: 

• Unified Enterprise Semantic Layer: Establishes a single, governed source of truth, enabling both human users and AI-driven analytics to operate on consistent definitions across markets.
• Natural Language with Guardrails: Improves the accuracy of conversational analytics through intent clarification and structured query routing, helping users arrive at trusted answers faster while maintaining consistency.
• Fast Iteration of Next-Gen AI: Enables continuous evaluation and introduction of new AI capabilities, while ensuring output quality is monitored and controlled as models evolve.
• Insight Automation: Embeds AI directly into decision-making workflows through capabilities such as anomaly detection, trend identification, and automated insight generation, accelerating how business performance is interpreted.

While these areas define Diageo’s approach to enterprise AI, their effectiveness depends on a strong underlying data foundation. 

This is where Strategy plays a critical role: providing a unified semantic layer, governed data, and native AI capabilities that enable these initiatives to operate consistently across the enterprise. 

Diageo’s AI strategy is already operational within its Global Business Performance Management (BPM) product, which tracks P&L, daily sales, and volume, mix, price, and cost drivers across markets. 

Using Strategy, Diageo embedded an AI-powered Autobot directly into this environment, allowing users to query governed data through natural language instead of navigating dashboards. 

Because Strategy ensures semantic consistency, users can ask questions such as what is driving gross profit or which brand families are underperforming and receive immediate, reliable answers. 

The result: Increased adoption across roughly 400 users, reduced reporting preparation time, and faster access to insights.  

These outcomes are not driven by AI alone. They are enabled by the governed data foundation and unified semantic layer that ensure accuracy, consistency, and trust. 

To scale enterprise AI across a complex, global environment, Diageo requires a stable and governed data foundation. With daily sales data, global usage, and multiple systems in play, consistency becomes critical.  

Strategy enables this by providing a unified semantic layer and embedded AI capabilities within Diageo’s existing analytics environment. This allows Diageo to: 

• Maintain a single, governed source of truth across its entire BI ecosystem
• Embed enterprise AI directly into operational workflows
• Deliver faster analytics through consistent, reusable data models
• Support both technical and non-technical users with self-service access to governed data
• Scale data access and insight generation across regions without fragmentation

Instead of rebuilding its architecture around AI, Diageo can extend its existing data foundation to introduce, adapt, and accelerate enterprise AI across the organization. 

Diageo’s long-term vision is a unified, product-driven data ecosystem built on shared, governed foundations. 

Instead of each market creating isolated assets, data becomes a reusable resource across regions, teams, and decision-making. A collaborative model replaces siloed ownership, positioning data products as enterprise-wide assets rather than local solutions. 

Strategy provides a unified data foundation that supports Diageo’s enterprise AI initiatives. 

With standardized data, governed definitions, and a shared semantic layer in place, Diageo can move from insight generation to decision-making faster while maintaining consistency and accuracy. 

As a result, enterprise AI becomes an extension of Diageo’s data strategy, embedded within everyday workflows and enabling faster, more reliable decision-making.