AI-ready data: Building the foundation for enterprise AI at Microsoft

For years, organizations have treated data as a critical business asset.

Here at Microsoft, data powers our reporting, supports our decision-making, and helps our leaders understand what’s happening across our teams responsible for our enterprise data.

But agentic AI has raised the stakes, making data quality more consequential. Frontier Firms like ours depend on high-quality data to support agents, automation, and employee decision making.

Today’s AI models can produce sophisticated summaries, answer complex questions, and help employees work more efficiently. Agents and reasoning systems increasingly connect data across business functions, perform multistep tasks, and help automate decisions and workflows.

That shift has led our teams in Microsoft Digital, the company’s IT organization, to ask a new question: What does it mean to have data that’s fully AI-ready, and where are we on this journey?

Measuring data quality is just one part of the answer.

“World-class AI agents start with AI-ready data, with the right foundation. That means more than simply making data available. It requires quality, governance, and consistent standards so that AI systems can understand the data, trust it, and use it effectively.”

Naval Tripathi, principal engineering manager, Microsoft Digital

In Microsoft Digital, we’re creating a repeatable framework for AI-ready data that combines governance, discoverability, semantics, context, ownership, lineage, security, and quality into an overall foundation. This framework helps AI systems interpret enterprise data and use it appropriately.

“World-class AI agents start with AI-ready data, with the right foundation,” says Naval Tripathi, principal engineering manager in Microsoft Digital. “That means more than simply making data available. It requires quality, governance, and consistent standards so that AI systems can understand the data, trust it, and use it effectively.”

Treating AI readiness as a business capability                         

One of the key lessons from our focus on AI-ready data here in Microsoft Digital is that readiness can’t be treated as a one-time cleanup project.

“We decided to focus on continuous data readiness, rather than a one-time certification process. You really can’t say, ‘OK, it’s all done now.’ We have to keep learning about and updating metadata and governance information to improve how AI uses data.”

Aishwarya Dinde, senior software engineer, Microsoft Digital

Data changes. Systems evolve. New business requirements emerge. AI capabilities continue to advance. So, we are approaching readiness as an ongoing business capability.

“We decided to focus on continuous data readiness, rather than a one-time certification process,” says Aishwarya Dinde, a senior software engineer in Microsoft Digital. “You really can’t say, ‘OK, it’s all done now.’ We have to keep learning about and updating metadata and governance information to improve how AI uses data.”

These data-readiness signals help determine whether the data is discoverable, reliable, understandable, and appropriate for AI-driven scenarios. Rather than just looking at a data-quality score, we’re creating a systematic way to identify where investments in data will likely have the greatest impact.

AI changed the standards for quality data

Many data platforms were originally designed to support analytics and reporting, which are human-centered activities. These platforms were designed primarily for human analysis, not for AI agents that need explicit business context.

“In the past, data was something humans used to get information and provide business insights. With AI, we are expecting the models to reason over the data. There is a need to ensure the data is ready for that and represents the business correctly.”

Miguel Uribe, principal PM manager, Microsoft Digital

In that era, humans supplied the context around these applications of data. Business analysts understood company terminology. Data engineers knew where information came from. Subject matter experts filled in gaps that weren’t captured inside the data itself.

AI systems have changed all this.

“In the past, data was something humans used to get information and provide business insights,” says Miguel Uribe, a principal PM manager in Microsoft Digital. “With AI, we are expecting the models to reason over the data. There is a need to ensure the data is ready for that and represents the business correctly.”

AI systems need explicit guidance about what data represents and how it should be used. Column names, metadata, ownership information, business definitions, descriptions, lineage records, and usage guidance all help AI understand what the data means and how it should be used.

In one example from our networking environment, an internal system used “Pigeon,” an internal name for a networking system, in its title. Without that context, an AI system interpreted “Pigeon” literally and incorrectly inferred that the data set was about birds rather than network infrastructure.

This is where human effort is needed to make sure that context is present and accurate.

“There’s a lot of retrofitting that needs to be done—human effort to contextualize the data,” says Diego Baccino, principal software engineering manager in Microsoft Digital. “AI doesn’t understand the business context. That’s where the human capital becomes really valuable. Once the context is added, then AI can really take it forward.”

This doesn’t mean that AI doesn’t play a role in the data-readiness process. AI-driven tools are increasingly used to detect data problems, and people decide what to do about them. We regularly run automated checks against our readiness criteria, flagging data that is stale, incomplete, or inconsistent with its documentation..

AI can also draft data descriptions for engineers to review and correct. This approach helps teams scale readiness work while keeping people accountable for accuracy and business context.

This realization about the importance of context led the Microsoft Digital data council and AI Center of Excellence—two vital aspects of our Frontier Firm move into agentic AI—to expand their focus beyond traditional governance and data-quality efforts. The goal was to create a common understanding of what AI-ready data means for the different teams that are responsible for our data across the enterprise.

“Metadata and context are part of the product at this point,” says Aira Agrawal, a product manager in Microsoft Digital. “Just having the data available isn’t enough anymore. Without that context, it’s not usable in the age of AI.”

As a result, our teams in Microsoft Digital are increasingly working to make data context discoverable and reusable, reducing the dependence on tribal knowledge that previously existed only in conversations and scattered documentation.

Measuring readiness before scaling AI

As groups across Microsoft have increased their investments in AI, our leaders are now asking questions about data readiness. Our mission in Microsoft Digital is to come up with accurate ways to make this readiness measurable.

“Our progression towards AI-ready data is going to be one of those key metrics that get looked at month over month. It’s becoming a super-critical metric for the organization, to ensure that all of our solutions are leveraging AI-ready data.”

Myron Wan, principal group product manager, Microsoft Digital

We now evaluate our data across multiple dimensions, including quality, governance, discoverability, interoperability, accessibility, observability, and contextual understanding. These dimensions help teams understand where gaps exist and where remediation efforts should be prioritized.

Historically, data quality was more of a technical concern discussed by engineering teams. Microsoft Digital is elevating data readiness to a business conversation tied directly to AI outcomes.

“Our progression towards AI-ready data is going to be one of those key metrics that get looked at month over month,” says Myron Wan, principal group product manager in Microsoft Digital. “It’s becoming a super-critical metric for the organization, to ensure that all of our solutions are leveraging AI-ready data.”

If a particular data set isn’t ready for its intended AI use, leaders can weigh the business value against the investment needed to address the gaps. “If your data score is telling you it’s not ready, then you can pose the question: Should we fast-track this and invest more in making the data AI-ready?” Uribe says. “That enables us as business decision-makers to prioritize where we’re going to put our money and our AI investments.” 

Using AI to breakdown business silos

Across Microsoft, teams are exploring scenarios where AI systems help identify problems, gather information, and support operational decision-making. Those scenarios depend heavily on trusted data foundations.

“We’re moving into a world where our data sets are not going to be in silos anymore, especially as all our organizational data moves into Microsoft Fabric. Networking, HR, finance, they’re all going to have their data in Fabric. This means we can tell stories across the business with connectors and APIs, unlocking more advanced analytics.”

Aira Agrawal, product manager, Microsoft Digital

Future AI systems may increasingly operate across domains, drawing information from disparate groups such as IT, HR, finance, operations, and other areas. That requires data that’s accessible and that also meets this new, higher bar for readiness.

We’re already seeing opportunities to use AI to connect information across previously separate domains, creating richer business insights and supporting more advanced forms of analysis.

“We’re moving into a world where our data sets are not going to be in silos anymore, especially as all our organizational data moves into Microsoft Fabric,” says Aira Agrawal, a product manager on the Infrastructure Data Lake team. “Networking, HR, finance, they’re all going to have their data in Fabric. This means we can tell stories across the business with connectors and APIs, unlocking more advanced analytics. It’s a really cool thing we’re enabling.”

Our long-term goal is to give business teams trusted data and shared foundations so they can create AI-enabled workflows without depending on central teams for every solution. Instead, we want individual business teams to be able to create their own AI-enabled workflows that operate across domains, knowing that the data they’re relying on is AI-ready.

The future of AI-ready data

Our work with AI-ready data continues to evolve.

The company is still refining readiness models, governance approaches, access patterns, and future capabilities. Our subject matter experts acknowledge that some challenges remain unresolved, including how best to provide business context at scale and how readiness should continue evolving alongside AI technologies and regulatory requirements.

“Whatever technologies you’re using, don’t miss the boat on contextualizing your data and making it AI-ready. The longer you wait, the longer it takes to realize AI value.”

Diego Baccino, principal software engineering manager, Microsoft Digital

Yet several key lessons have already emerged:

• Visibility matters. Organizations need to know what data they have and who owns it.
• Governance remains critical, but governance alone isn’t enough.
• Semantics and business context increasingly determine whether AI can use data effectively.
• Readiness must become repeatable across teams rather than dependent on individual experts.

And perhaps one of the most important lessons: Don’t wait to get started. Data readiness is too crucial a factor for business success in the age of AI to put off dealing with the challenge.

“Whatever technologies you’re using, don’t miss the boat on contextualizing your data and making it AI-ready,” Baccino says. “The longer you wait, the longer it takes to realize AI value.”