We’re empowering Microsoft employees with data-driven innovation including transforming experiences, modernizing functions, and optimizing infrastructure through AI and a robust data strategy.
At Microsoft Digital, our mission is to transform the digital employee experience across devices, applications, and hybrid infrastructure.
But what fuels that transformation?
Data.
Enterprise data, backed by a strong strategy and culture, powers the technologies that drive Microsoft forward.
Let’s explore some of the ways data is the fuel that powers IT at Microsoft.
Modernizing our corporate functions
In the era of AI, data plays a crucial role in our effort to modernize corporate functions like Human Resources, Finance, and Corporate, External, and Legal Affairs (CELA). For instance, employees can use our Employee Self Service (ESS) agent to retrieve information and insights in real time using natural language queries. In the past, these actions were either conducted manually or required an IT support ticket, both of which resulted in a negative user experience. Now, employees can use ESS to complete their tasks more efficiently and with the confidence that the data they’re working with is secure.
Another impact of the powerful combination of data and AI is being realized by HR generalists. The Microsoft HR Business Intelligence team manages an immense number of Power BI dashboards. Instead of manually sifting through vast amounts of information to locate required data, our Microsoft Digital team has developed new capabilities that allow HR generalists to use natural language queries to find the Power BI dashboards they need in real time. These tools use generative AI to optimize the time employees spend searching for information, so they can focus on their strategic deliverables.
“AI is not just about algorithms—it’s about the data that powers them. A well-structured enterprise data strategy ensures that AI can drive automation, insights, and business transformation at scale. At Microsoft Digital, we’re using data to redefine how employees work and innovate,” says Faisal Nasir, a principal architect in Microsoft Digital.
In Microsoft Digital, we’ve defined a data standard for our AI workloads that we refer to as “AI-ready data.” AI-ready data is data that’s available, complete, accurate, and high quality. AI-ready data allows tools like Microsoft Fabric for data management and Microsoft Purview for governance make the most of AI tools and machine learning for the company’s corporate functions. By using data meshdata mesh, a decentralized data architecture approach, data scientists and engineers build data products across organizational siloes, or workspaces, using the One Lake feature of Fabric, illuminating cross-domain insights. By using Purview’s Shortcut feature, engineering teams can more easily request and receive approval for access to the information they need to manage their organization more effectively and efficiently.
In the race to adopt machine learning and AI, organizations often focus on model sophistication while ignoring the foundation: the data. Without clean, labeled, contextualized, and well-governed data, even the most advanced algorithms will falter. AI-ready data isn’t just nice to have—it’s the make-or-break factor in every successful AI initiative. Models trained on poor-quality data will amplify bias, hallucinate patterns, and make dangerously flawed predictions. Meanwhile, organizations that invest in high-quality, AI-ready data gain not just better models, but a long-term competitive edge. They build systems that learn faster, adapt better, and scale smarter. In short, AI-ready data isn’t the backend. It is the strategy.
{Learn how we’re transforming our data governance at Microsoft with Microsoft Purview and Microsoft Fabric.}
Optimizing our employee experience
Microsoft Digital’s user-centric, coherent design philosophy puts the user—an employee or a guest—at the heart of every decision and aligns all our facility’s services—physical and digital—to the needs of people. We employ a data-driven approach to the employee experience, using AI to aid in decision-making, improving how Microsoft employees interact with technology, physical spaces, and other Microsoft resources.Faisal Nasir (left to right) Oana Nica, Damon Gray, Johnson Samuel, Naval Tripathi, and Patrice Pelland, with the help of AI, drive the data strategy for transforming the digital employee experience.
A few examples of how we’re using data and AI to improve the employee experience include:
Commute optimization: AI is being used to predict the best routes and Connector buses for employees, improving their commute experience. Microsoft Digital observes frequency and travel patterns to predict and recommend optimal routes to the desired destination, thereby decreasing the amount of time spent in transit.
Dining solutions: Microsoft Digital is actively working to enhance the dining experience through the integration of AI capabilities. We’re enabling insights based on data related to dining popularity—essentially, where and what do employees like to eat? By using AI and machine learning, we aim to offer a richer Copilot experience to users. For instance, we’re developing features that analyze cafe station popularity over time, and calculate the average fulfillment times for orders at each station. With this data, employees can use Copilot to make informed decisions about where to dine based on real-time insights into station capacity, foot traffic, and average efficiency.
Occupancy predictions: Predicting employee occupancy in on-campus facilities helps to optimize utility usage. By using AI and machine learning to understand footfall patterns, the team can adjust heating and cooling systems to save energy and improve efficiency.
{Learn more about our fresh approach to accessibility powered by inclusive design.}
Managing our network infrastructure
Managing Microsoft’s network infrastructure effectively is crucial for maintaining productivity and collaboration. A data-driven approach can provide the necessary insights and tools to ensure a seamless connectivity experience, efficient diagnostics, performance and cost optimization, and proactive security management.
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