{"id":25717,"date":"2026-10-15T09:00:00","date_gmt":"2026-10-15T16:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/insidetrack\/blog\/?p=25717"},"modified":"2026-10-14T16:07:37","modified_gmt":"2026-10-14T23:07:37","slug":"ai-ready-data-building-the-foundation-for-enterprise-ai-at-microsoft","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/insidetrack\/blog\/ai-ready-data-building-the-foundation-for-enterprise-ai-at-microsoft\/","title":{"rendered":"AI-ready data: Building the foundation for enterprise AI at Microsoft"},"content":{"rendered":"\n

For years, organizations have treated data as a critical business asset.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

That shift has led our teams in Microsoft Digital, the company\u2019s IT organization, to ask a new question: What does it mean to have data that\u2019s fully AI-ready, and where are we on this journey?<\/p>\n\n\n\n

Measuring data quality is just one part of the answer.<\/p>\n\n\n\n

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\u201cWorld-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.\u201d<\/p>\nNaval Tripathi, principal engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

In Microsoft Digital, we\u2019re 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.<\/p>\n\n\n\n

\u201cWorld-class AI agents start with AI-ready data, with the right foundation,\u201d says Naval Tripathi, principal engineering manager in Microsoft Digital. \u201cThat 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.\u201d<\/p>\n\n\n\n

Treating AI readiness as a business capability                         <\/h2>\n\n\n\n

One of the key lessons from our focus on AI-ready data here in Microsoft Digital is that readiness can\u2019t be treated as a one-time cleanup project.<\/p>\n\n\n\n

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“We decided to focus on continuous data readiness, rather than a one-time certification process. You really can\u2019t say, \u2018OK, it\u2019s all done now.\u2019 We have to keep learning about and updating metadata and governance information to improve how AI uses data.”<\/p>\nAishwarya Dinde, senior software engineer, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

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

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AI-ready data characteristics<\/h3>\n\n\n\n
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Interoperability<\/strong><\/p>\n\n\n\n

Assesses whether data can be easily joined or reasoned upon across different domains and systems<\/p>\n<\/div>\n\n\n\n

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Observability<\/strong><\/p>\n\n\n\n

Detects when pipelines break, drift occurs, or anomalies arise, so that issues surface before they impact AI models<\/p>\n<\/div>\n\n\n\n

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Ethical and responsible use<\/strong><\/p>\n\n\n\n

Considers whether datasets are evaluated for bias, representation, and consent, ensuring responsible AI deployment<\/p>\n<\/div>\n\n\n\n

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Structure and modelability<\/strong><\/p>\n\n\n\n

Determines whether data is organized for immediate model consumption, minimizing manual feature engineering<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Documentation and context<\/strong><\/p>\n\n\n\n

Evaluates whether datasets carry clear business definitions, assigned owners, and documented usage guidance readable by AI models\u202f<\/p>\n<\/div>\n\n\n\n

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Accessibility and availability<\/strong><\/p>\n\n\n\n

Checks whether datasets meet the latency and uptime standards that AI systems require for reliable, performant access\u202f<\/p>\n<\/div>\n\n\n\n

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Governance and security<\/strong><\/p>\n\n\n\n

Ensures that data is used in compliance with policies and protected from misuse via labeling, access control, and audit logging<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

“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\u2019t say, \u2018OK, it\u2019s all done now.\u2019 We have to keep learning about and updating metadata and governance information to improve how AI uses data.”<\/p>\n\n\n\n

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\u2019re creating a systematic way to identify where investments in data will likely have the greatest impact.<\/p>\n\n\n\n

AI changed the standards for quality data<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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“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.”<\/p>\nMiguel Uribe, principal PM manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

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.<\/p>\n\n\n\n

AI systems have changed all this.<\/p>\n\n\n\n

“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.”<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

In one example from our networking environment, an internal system used \u201cPigeon,\u201d an internal name for a networking system, in its title. Without that context, an AI system interpreted \u201cPigeon\u201d literally and incorrectly inferred that the data set was about birds rather than network infrastructure.<\/p>\n\n\n\n

This is where human effort is needed to make sure that context is present and accurate.<\/p>\n\n\n\n

\u201cThere’s a lot of retrofitting that needs to be done\u2014human effort to contextualize the data,\u201d says Diego Baccino, principal software engineering manager in Microsoft Digital. \u201cAI 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.\u201d<\/p>\n\n\n\n

This doesn\u2019t mean that AI doesn\u2019t 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..<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

This realization about the importance of context led the Microsoft Digital data council<\/a> and AI Center of Excellence<\/a>\u2014two vital aspects of our Frontier Firm move into agentic AI\u2014to 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.<\/p>\n\n\n\n

“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\u2019t enough anymore. Without that context, it\u2019s not usable in the age of AI.”<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Measuring readiness before scaling AI<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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\u201cOur progression towards AI-ready data is going to be one of those key metrics that get looked at month over month. It\u2019s becoming a super-critical metric for the organization, to ensure that all of our solutions are leveraging AI-ready data.”<\/p>\nMyron Wan, principal group product manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

“Our progression towards AI-ready data is going to be one of those key metrics that get looked at month over month,\u201d says Myron Wan, principal group product manager in Microsoft Digital. \u201cIt\u2019s becoming a super-critical metric for the organization, to ensure that all of our solutions are leveraging AI-ready data.”<\/p>\n\n\n\n

If a particular data set isn\u2019t ready for its intended AI use, leaders can weigh the business value against the investment needed to address the gaps. \u201cIf 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?\u201d Uribe says. \u201cThat enables us as business decision-makers to prioritize where we’re going to put our money and our AI investments.” <\/p>\n\n\n\n

Using AI to breakdown business silos<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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\u201cWe\u2019re 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\u2019re 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.\u201d<\/p>\nAira Agrawal, product manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

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\u2019s accessible and that also meets this new, higher bar for readiness.<\/p>\n\n\n\n

We\u2019re already seeing opportunities to use AI to connect information across previously separate domains, creating richer business insights and supporting more advanced forms of analysis.<\/p>\n\n\n\n

\u201cWe’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,\u201d says Aira Agrawal, a product manager on the Infrastructure Data Lake team. \u201cNetworking, HR, finance, they\u2019re 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\u2019s a really cool thing we\u2019re enabling.\u201d<\/p>\n\n\n\n

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\u2019re relying on is AI-ready.<\/p>\n\n\n\n

The future of AI-ready data<\/h2>\n\n\n\n

Our work with AI-ready data continues to evolve.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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“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.”<\/p>\nDiego Baccino, principal software engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n

Yet several key lessons have already emerged:<\/p>\n\n\n\n