As these processes grew in scale and complexity, it became clear that improving something highly manual and already operating at massive scale would require a fundamentally different approach.

“With BPO, we’re dealing with high-volume, high-touch processes that are core to how the business runs,” says Jonathan d’Orgee, an AI transformation lead for Microsoft Business Operations.

For many organizations, the idea of overhauling a core business process can feel like a daunting step. At Microsoft we act as our own first customer, which gives us a way to test, refine, and de-risk that transformation in our own operations before bringing those proven patterns to customers. We call this approach Customer Zero.

In this case, that meant rethinking how high-volume operations could run better with AI directly embedded into day-to-day tasks, including building solutions using tools like Microsoft Dynamics 365 and Azure AI.

“We looked at manual steps, broken workflows, and disconnected systems as opportunities for AI transformation.”

Jonathan d’Orgee, AI transformation lead, Microsoft Business Operations

Learn more

Check out more stories about our Customer Zero journey with our products and services here at Microsoft.

Identifying manual inefficiencies

On top of the complexity of handling so many transactions across the globe, Business Operations sometimes experienced periodic surges that could exacerbate inefficiencies. During these surges, the team would see a high volume of complex, time-critical transactions— especially at the end of the month or the quarter—and manual processes were too slow to keep up.

As we reviewed these inefficiencies, we looked for the most impactful use cases—places where we could integrate AI into workflows. To do this, we asked two important questions:

• What types of transactions have the highest volume?
• What parts of the process take the longest time or consume the most resources?

It was a classic case of the 80/20 rule—finding the 20% of the processes that required 80% of the work.

“We looked at manual steps, broken workflows, and disconnected systems as opportunities for AI transformation,” d’Orgee says.

An example might be where we receive an email asking to have a contract updated. In the former process, the email might sit there until a human could review it manually. Then someone would review it, direct it to the right queue, and assign it to the right person.  

“With AI in the workflow, emails and attachments are analyzed right when they arrive, and immediately assigned to the right queue and person,” d’Orgee says.

Taking these kinds of steps dramatically increased efficiency and reduced costs overall.

“With deep knowledge of our Business Operations ecosystem, we targeted high-volume, repeatable workflows across globally distributed operations. These were processes where AI could break traditional location and labor constraints, unlocking scalable automation and measurable business impact.”

Shashidhar Lanka Venkata, partner group engineering manager, Business Commerce Platforms

Configuring an AI toolkit

Once we’d identified the areas that were ripe for transformation, we set about developing an AI-driven solution on top of our existing critical workflow systems.

“With deep knowledge of our Business Operations ecosystem, we targeted high-volume, repeatable workflows across globally distributed operations,” says Shashidhar Lanka Venkata, a partner group engineering manager in the Business Commerce Platforms team. “These were processes where AI could break traditional location and labor constraints, unlocking scalable automation and measurable business impact.”

The BPO AI Toolkit is our AI operating system for business process operations. Its job is to help us with decision making. Built on Microsoft Dynamics 365 and Azure AI, it brings process mining, Microsoft 365 Copilot, Windows 365, and the Azure Marketplace together into AI-native workflows that can be reused by different vendors.

The toolkit is built on a handful of capabilities that work together:

Agentic memory turns tribal knowledge into structured operational intelligence that agents can access on demand.

Prebuilt agents provide enterprise-ready capabilities that teams can reuse instead of rebuilding workflows.

An agentic UI reduces context-switching time, helping operators focus on decisions and exceptions.

Digital Twins measures real end-to-end process performance and continuous improvement.

Agent Desktop provides secure access anywhere.

“It’s just part and parcel of working with AI, which is much different than working with more traditional ways of automating,” says d’Orgee.

He explains that because the AI is configurable, our teams are able to move faster. “The lead time is a lot shorter, and we’re able to make changes a lot more quickly.”

At the core of everything during this effort was the drive to constantly assess “the human buy-in:” How are people using this technology in a way that solves real problems at a global scale?

Keeping humans in the loop and measuring AI transformation

Integrating AI into existing workflows and processes isn’t just about the technology—it also should entail a cultural shift within an organization.

We wanted to ensure that our operations team was adopting the AI tools in the right way. That meant understanding which processes must still be human-led, such as areas where the handling of exceptions requires more discernment.

Rather than removing humans from the process, the team redefined the human role. AI now handles tasks such as data validation, case creation, and compliance checks, while our team members focus on judgment, exceptions, and continuous improvement.

“It’s really exciting for us, because operations has always been about trying to be efficient. With AI, it’s allowed for breakthroughs that we haven’t been able to achieve before.”

Jonathan d’Orgee, AI transformation lead, Microsoft Business Operations

That balance helped the team scale automation without losing the oversight and expertise needed to maintain quality.

The impact of this Frontier model has been significant. So far, we’ve been able to transform roughly a quarter of our BPO processes with AI. This has led to an 80% improvement in process quality and a 33% reduction in cost per transaction, d’Orgee says.  

More than 75% of the cases our teams work on are processed utilizing the AI toolkit. These gains are measured with Digital Twins, a process-mining model that monitors each workflow live, allowing teams to continuously track and improve. Building on this momentum, the team has plans to transform 80% of the BPO process with AI by fiscal year 2028.

D’Orgee urges organizations that want to apply our Customer Zero learnings to their own workflows to look for high-volume, high-effort, highly manual work. This will lead you to the best opportunities for automating your processes at scale and deliver the most benefit.

From finance to sales operations, teams across Microsoft have turned to the BPO AI toolkit to prove how reusable AI capabilities can drive enterprise-wide transformation.

“It’s really exciting for us, because operations has always been about trying to be efficient,” d’Orgee says. “With AI, it’s allowed for breakthroughs that we haven’t been able to achieve before. I’ve just been thrilled to come to work on that front.”

The post Streamlining business operations at Microsoft with an AI toolkit appeared first on Inside Track Blog.

At first glance, helping sellers improve the way they work looks like a data challenge because we tend to believe more data equals better decisions. However, when we explored how they use data to prepare for customer meetings and internal business reviews, we saw a different pattern.

The amount of data they had available to them had increased—but so had the manual effort needed to turn it into useful and actionable insights. In fact, our sellers were spending more time looking for relevant information than they were spending engaging with customers to improve their experience.

As part of our Customer Zero approach at Microsoft, we apply our technologies internally and use that experience to understand what works at scale so we can pass that knowledge onto our customers. In this case, our discoveries unearthed a deeper issue with how our sellers interact with data in their day-to-day work.

Too much data, not enough insight

The increased amount of data available to our sellers gave them more options but also more information to process. It also introduced fragmentation.

“I think the big feedback pain point we got was that we have way more data than our sellers were used to. You give us a gigantic well of data, but how I use that data to execute my job is fragmented.”

Michael Toomey, revenue insights lead, Finance Data and Experience

A significant portion of our sellers’ time went into finding, interpreting, reconciling, and analyzing the information before any actual decisions or customer interactions were taking place. In some cases, sellers were navigating hundreds of reports and dashboards across multiple systems. In others, they were exporting data into spreadsheets, building their own analyses, and assembling presentations manually. They told us they were overwhelmed by the amount of data they had access to.

“I think the big feedback pain point we got was that we have way more data than our sellers were used to,” says Michael Toomey, a revenue insights lead on our Finance Data and Experience team. “You give us a gigantic well of data, but how I use that data to execute my job is fragmented.”

This tedious manual user experience didn’t match the fast pace of our work. Sellers needed to move quickly, and we needed a way to empower them to find the data and insights they needed to be able to make actionable decisions immediately.

Instead of continuing to expand the amount of data available to our sales force, we concentrated on making the existing data easier to access, understand, and use.

Beginning with a trusted data foundation

The starting point was the data itself. Our sales organization draws from many systems, each of which has its own structure, definitions, and refresh cycles. Without alignment across these systems, even seemingly simple seller questions could produce different answers depending on the data source. This created more work as our sellers hunted down which answer was the correct one.

We consolidated all this data into a unified model on Microsoft Fabric, creating a shared data foundation across the organization. It included standardized definitions, consistent metrics, and common hierarchies. Data from more than 70 systems was brought together into one governed environment.

This step required coordination across teams and ongoing attention to evolving data governance. It established trust in our data that made it easier to streamline the user experience for our sales teams down the line.

Streamlining how sellers work with data

With a consistent data foundation put into place, we turned our attention to how sellers could most effectively access and use the information.

Instead of asking sellers to navigate a portal of reports, we designed role-based dashboards that reflect how people actually work. Individual dashboards are curated based on role, workflow, and responsibilities, helping our sellers quickly find what they needed without searching across systems. We transformed the work surrounding actionable insights, too: Power BI surfaces intelligent insights to our sellers’ dashboards every morning, automatically giving them their priorities for the day and providing an overview of their funnel.

Before this transformation, building PowerPoint presentations for customers and summarizing reports were routine but tedious parts of the preparation process for our sellers before they spoke to customers. These preparatory steps have been streamlined or automated wherever possible, reducing seller time spent on repetitive manual tasks.

Empowering sellers using AI

With the data organized and cleaned and the user experience transformed, we introduced AI to overhaul how our sellers interact with the information available to them.

“A lot of what we’re trying to do is get to a native Microsoft 365 Copilot experience where we meet people where they’re working every day. And then work is optimized to be able to reason over our day to pull the data in.”

Michael Toomey, revenue insights lead, Finance Data and Experience 

To find the information they’re looking for, sellers can now ask questions in natural language and receive answers directly in the tools they already use. Insights powered by Power BI Copilot, Fabric, and AI Foundry are delivered proactively, helping sellers prioritize their day, generate summaries, and assemble materials for meetings without working through each step manually.

Insights that previously required a great deal of time to uncover are surfaced directly. The relevant reports are automatically routed to sellers based on their job description and client base. Sellers can also subscribe to continuously updated reports and have them surfaced each morning to their inbox.

These capabilities are available in the tools our people already use, which means they don’t have to spend time learning new product suites. The information they’re looking for can be surfaced through tools like email or within Microsoft Teams, which means sellers can stay focused on their work without needing to switch between systems.

Processes that once took hours can now be completed in minutes.

“A lot of what we’re trying to do is get to a native Microsoft 365 Copilot experience where we meet people where they’re working every day. And then work is optimized to be able to reason over our day to pull the data in,” Toomey says.

What changed

The most visible shift we’ve observed is how sellers spend their time. Less effort goes into finding, assembling, and reconciling information, and more attention is directed toward understanding customer needs and making decisions that actively move the business forward.

After deploying this model across our sales organization, we saw meaningful improvements in both efficiency and effectiveness, including:

• 100,000 seller hours saved annually
• 30% reduction in data ingestion costs
• 10x faster insight generation
• 1,500 reports retired and consolidated

We credit our success to building a trusted data foundation; a streamlined, intuitive user experience; and embedding AI into the flow of work to help our sellers surface insights and information with a few clicks instead of spending hours sifting through irrelevant inputs.

We learned that executive sponsorship and active change management were essential to transforming the department successfully. Concentrating on consistency and departmental alignment around a set of trusted, verified, well-governed data and shepherding people through shifting how they worked with the data were just as important as adding AI into the process.

Looking ahead

At the heart of our trusted data foundation is the semantic layer, the place where our business definitions, metrics, and data quality are standardized across the organization. It’s now the engine that powers our agents. Because those definitions live in one governed place, our agents can reason over our data products with confidence, and we can trust what they surface.

That foundation is already bearing fruit. A new generation of personal and role-based agents at Microsoft is taking on the workflows that were consuming our sellers’ time, such as meeting preparation, pipeline reviews, and customer insight generation. These processes are now running on the governed data infrastructure we built. Everything draws from the same source of truth, so everything our agents work with is consistent, reliable, and ready to act on.

This return on our platform investment is bringing us closer to becoming a Frontier Firm where our people spend less time searching for the answers and more time acting on them.

The post From data sprawl to AI-driven seller insights at Microsoft appeared first on Inside Track Blog.

• Introduction
• Chapter 1: Understanding Work IQ
• Chapter 2: Establishing trust: How we govern Work IQ
• Chapter 3: How our employees experience Work IQ day-to-day
• Chapter 4: Work IQ beyond Microsoft 365
• Chapter 5: Working with Work IQ: The Customer Zero impact
• Conclusion: Where we’re heading: Work IQ, Fabric IQ, and Foundry IQ

Improving agentic workplace results with Work IQ

Adding deeper contextual intelligence to Microsoft 365 Copilot responses

Enterprise knowledge is perhaps a company’s most valuable asset, but for AI and agents, it can be difficult to take advantage of. Years of emails, documents, chats, meeting recordings, and workflows have created enormous volumes of rich data, scattered across systems and teams in a fragmented way. This data captures how work actually happens, but harnessing it broadly—especially in ways that support better decision making—has traditionally been almost impossible.

Enter the power of agentic AI tools.

In the modern agentic workplace, employees and teams here at Microsoft and elsewhere are finally able to take advantage of all that rich, unstructured knowledge. Microsoft 365 Copilot and AI agents can now access all this data and not simply retrieve information but also reason over it—learning how work gets done and then providing rich contextual responses and guidance.

“By giving AI the ability to reason across the vast repositories of unstructured data that our enterprise possesses, Work IQ fundamentally changes what’s possible for Copilot, agents, and employees alike.”

Brian Fielder, vice president, Microsoft Digital

We’ve given this new, dynamic way of leveraging your enterprise data to boost productivity a special name: Work IQ.

Work IQ represents a big step forward.

For us, it’s enabling the concept of “intelligence on tap” across our enterprise, making our organizational knowledge and work context accessible in real time, grounded in the signals employees generate every day. This transforms unstructured data from a challenge into a strategic resource—one that can support workflows at scale.

“Work IQ represents the next phase of the agentic workplace of the future—and it’s here,” says Brian Fielder, vice president of Microsoft Digital. “By giving AI the ability to reason across the vast repositories of unstructured data that our enterprise possesses, Work IQ fundamentally changes what’s possible for Copilot, agents, and employees alike.”

“It’s not really a brand-new capability, but more an evolution of what users already know, which is access to the grounding data in their Microsoft tenant. The difference is that Work IQ adds an additional layer to provide more context, allowing for richer and more relevant results.”

Aisha Hasan, principal product manager, Microsoft Digital

Internally here at Microsoft, Work IQ is having a tangible effect on how we work every day. A few simple scenarios that illustrate the power of Work IQ—described in greater detail in Chapter 3—include:

• Helping our employees understand which emails require their immediate attention, so they can focus on what matters
• Connecting meeting transcripts to the people involved in a meeting, accelerating actions through a deeper understanding of the participants and their work patterns
• Enabling our employees to create, organize, and publish Microsoft 365 content more quickly and with higher quality

This is just the beginning. As AI continues to permeate our business workflows, nearly every day-to-day task at Microsoft will be simplified, expedited, and improved by the intelligence of Work IQ. This includes the agents that are managing routine business and operational processes, giving them critical business context that helps their reasoning abilities.

This guide explores the ways that Work IQ is impacting how work gets done at Microsoft, and how Microsoft Digital—the company’s IT organization—has played a key role as Customer Zero, validating how Work IQ behaves under real enterprise conditions. It also examines the challenges and considerations that IT organizations will face as we enter an era where AI agents have access to unstructured data to complete workflows.

Chapter 1: Understanding Work IQ

Providing deeper insights through the power of context

Before we can fully explore the implications of Work IQ, it’s important to start with a clear understanding of what it is.

Work IQ is not a new application or service that users interact with directly. Rather, it’s a shared intelligence layer that continuously interprets work happening across the tenant. Understanding this distinction is critical, because it explains why Work IQ shows up everywhere Microsoft 365 Copilot works—and why it must be treated as foundational infrastructure, not as optional, add‑on functionality.

“It’s not really a brand-new capability, but more an evolution of what users already know, which is access to the grounding data in their Microsoft tenant,” says Aisha Hasan, a principal product manager in Microsoft Digital. “The difference is that Work IQ adds an additional layer to provide more context, allowing for richer and more relevant results.”

Work IQ is built on three layers:

• Data: It unifies signals from files, emails, meetings, chats, and business systems.
• Memory: It builds persistent understanding of how people and teams work.
• Inference: It combines models, skills, and tools to reason and act.

At a high level, Work IQ consists of the systems that collect and interpret signals from everyday work. These signals come from many familiar Microsoft 365 applications—Word, Outlook, PowerPoint, Teams, SharePoint, and more—as well as structured data sources (such as those contained in Power Apps and Dynamics 365 resources).

The fact that Work IQ unifies unstructured and structured data into a shared ontology is a key differentiator from traditional search tools. This combination, referred to as semantic unification, means that it can combine the authoritative data contained in structured sources with the intent, nuance, and narrative found in unstructured data.

Work IQ draws from a broad range of work data from your Microsoft tenant. The unstructured data includes:

• SharePoint sites, files, and other content
• OneDrive activity that reflects individual work and collaboration patterns
• Teams content, including chats, channels, and meeting data
• Outlook emails and attachments

In addition, calendar signals—such as meeting participation, recency, and frequency—add time-based context that helps Work IQ understand priority and relevance of different data. This is what it means to go beyond simple information retrieval.

When all these are combined, it provides rich context that allows Work IQ to reason across all our employees’ work in a way that would be impossible if each signal were evaluated independently.

In practice, this means that when an employee asks a question about a current work project in Copilot, the tool’s response is not simply informed by the model’s capabilities or general source material. Responses are shaped by Work IQ’s understanding of the employee’s role, recent work, collaboration patterns (who they work with), and the larger enterprise context and conversations surrounding the question.

How our employees interact with and understand Work IQ depends on their role in the organization.

Our personas and their relationship with Work IQ

End user

Gains richer, context-aware agent conversations

IT admin

Enables and governs Work IQ access

Agent builder

Manages data connectors quickly and easily

Security professional

Knows that sensitive data stays protected with Work IQ

AI agents using Work IQ behave similarly. They use the intelligence to ground their reasoning in real organizational data, ensuring that their actions and recommendations are aligned with how work is happening inside the tenant. Although there are differences in how they are configured, all agents in a Microsoft tenant can be set up to take advantage of the power of Work IQ.

The impact of Work IQ on our company has been dramatic—we’re seeing agentic responses and actions that go deeper than surface-level answers. Our ability to reason over both our structured and unstructured data is producing richer, more nuanced contextual results that are boosting our productivity.

As your organization assesses your level of AI readiness, think of Work IQ not as an abstract concept but as critical infrastructure. It’s the key to connecting enterprise knowledge, trust, and productivity in a single, shared foundation.

Work IQ versus Microsoft Graph

Work IQ does not replace what we call the Microsoft Graph, the general term for unified, API-enabling, secure, permission-aware access to Microsoft 365 data, insights, and services. While the Microsoft Graph provides our employees with access to all their work data, Work IQ turns those signals into meaningful context that AI can reason over. In other words, Graph answers the general question “what info exists,” while Work IQ interprets what that information means and weaves it into responses to make them better.

Key takeaways

As you prepare for Work IQ, these points can help frame how to think about its role in your organization:

• Work IQ is foundational infrastructure, not a user-facing feature. It operates as a shared intelligence layer across the tenant, continuously interpreting signals from everyday work.
• Work IQ draws its power from context, not isolated data. By combining signals from email, meetings, documents, calendars, and collaboration patterns, it enables Copilot and agents to reason about work in a way that goes beyond simple search or retrieval.
• Better agentic outcomes depend on Work IQ being in place. When agents and Copilot are grounded in Work IQ, their responses and actions align more closely with real enterprise work, delivering better relevance and measurable productivity gains.

Learn more

How we did it at Microsoft

• Discover how Work IQ is supercharging our AI usage at Microsoft. This post delves into the ways that Work IQ is boosting agentic response and changing the way we work at the company.  
• See how we’re embracing this agentic moment at Microsoft. This story covers how we use the power of Copilot and AI agents to boost employee productivity.
• Explore our IT playbook for the AI era and learn how we’re becoming a Frontier Firm. This article shares our journey as an IT organization toward the AI-powered future.

Further guidance for you

• Learn more about Work IQ with this post from the Microsoft 365 Copilot Blog.
• Check out this Microsoft Learn overview of the different Work IQ agent tools.
• Read all about the Microsoft Graph on the Microsoft Learn site.

Chapter 2: Establishing trust: How we govern Work IQ

Building on an existing foundation of solid governance and security

Like all Microsoft products, Work IQ was built with security foremost in mind. As the IT team at Microsoft, it is our responsibility to work in tandem with the product groups to ensure that all data that Work IQ has access to is well governed and secure.

The fact that Work IQ does not introduce new data into Microsoft 365 makes meeting this commitment easier. Embedded directly into the Microsoft 365 intelligence stack, Work IQ inherits the same compliance, security, and access controls that already govern the tenant.

“With great power comes great responsibility, and it’s up to your IT team to think about what it means to give your users full access to all this Work IQ data. It can greatly accelerate what people can build and what they can do.”

David Johnson, principal PM architect, Microsoft Digital

For Microsoft 365 Copilot–native agents, Work IQ is enabled to provide governed, context‑aware access to Microsoft 365 work data without requiring developers to build or manage individual data connectors.

As our governance experts note, this represents an inherent trade-off. Giving an agent access only to certain isolated data types reduces risk but also limits its value. Granting access through Work IQ means an agent can reason across everything the employee can access. This simplifies enablement but also requires stronger confidence in governance foundations.

Microsoft 365 intelligence stack

As our governance experts note, this represents an inherent trade-off. Giving an agent only access to certain isolated data types limits risk, but it also limits its value. Granting access through Work IQ means an agent can reason across everything the employee can access. This simplifies enablement but also requires stronger confidence in governance foundations.

“With great power comes great responsibility, and it’s up to your IT team to think about what it means to give your users full access to all this Work IQ data,” says David Johnson, a principal PM architect in Microsoft Digital. “It can greatly accelerate what people can build and what they can do. At the same time, organizations will want to think about the downstream implications of access.”

Exposing underlying governance issues

Our overall solution was to anchor Work IQ to our governance and security policies that already existed for our data. Sensitivity labels, data protection rules, and data-loss prevention policies remain the primary guardrails, as they do for all data across our enterprise. All these controls live at the data layer.

A critical aspect of this governance model is how sensitivity labels propagate through Work IQ experiences. In Microsoft 365, the label that is applied to a source document determines the label of any derived outputs, including summaries, insights, or AI-generated responses. This ensures that users have immediate context about the information’s sensitivity and how it should be handled. The label effectively travels with the data, reinforcing both user awareness and policy enforcement.

Labels also play a key role in controlling access beyond simple permissions. Even if a user has baseline access to a location, sensitivity labels can further restrict whether content can be extracted, shared, or surfaced through AI experiences. In some cases, organizations can configure policies so that content with specific labels is not returned at all in Work IQ or Copilot responses. This gives IT teams an additional layer of control to prevent exposure of particularly sensitive information.

These labeling principles extend across collaboration scenarios as well. For example, meeting labels determine the classification of all downstream artifacts—including recordings, transcripts, and notes. Sensitive discussions remain governed consistently, even as Work IQ helps make them more discoverable and actionable.

For example, even with Work IQ enabled, a document labeled Highly Confidential cannot be exposed through Copilot to someone without access, even if it is referenced in a Teams meeting transcript or included in an AI-generated summary. Copilot may understand that the document played a role in a particular decision, but it cannot extract or reveal its contents beyond what permissions allow.

This distinction—discoverable versus extractable—proved critical in our deployment of Work IQ. The intelligence layer makes data relationships visible, but it does not override protection. In one internal scenario, a sensitive document was found to be accessible through a Copilot query. The root cause was not Work IQ, but a missing sensitivity label—the AI tool simply honored what governance allowed. We treated the incident as a governance signal and corrected labeling at the source.

Remember that Work IQ can only access data that:

• Exists inside your Microsoft 365 tenant or is explicitly connected via approved connectors
• The current user already has permission to access
• Is allowed by tenant‑level admin policy, compliance, and sensitivity controls

The security and governance considerations also extend to how new agents are released across our enterprise. For example, an agent created for use within one internal team has lighter governance controls than one that is published to our internal Microsoft agent portal, which offers companywide access. The latter requires additional review, approval, and monitoring as part of our due diligence for governance and security.

Ultimately, Work IQ adheres to all of the security and governance policies and procedures in our tenant, preserving the trust that our security-first approach creates and maintains.

Key takeaways

The following are important considerations for data governance and security when you consider adopting Work IQ for your organization:

• With Work IQ, governance and security are top-line priorities. We made sure that Work IQ would always inherit the same compliance, access controls, and data protection policies that already govern Microsoft 365 data.
• Work IQ doesn’t introduce new data access—it changes how existing access functions. By packaging tenant data into a single intelligence layer, it facilitates easier agent builder access to the data you already have in your Microsoft 365 tenant.
• The distinction between discoverable and extractable data is central to safe AI deployment. Copilot and other agents can understand how work information is connected and referenced without exposing protected content beyond existing permissions.
• IT admins and leaders should consider the ramifications to their tenant. Work IQ makes agents more powerful and context-aware by opening up access to vast quantities of Microsoft 365 data, but IT professionals should always think through downstream effects on data security and governance.
• Work IQ surfaces governance gaps instead of masking them. When issues arise—such as misapplied sensitivity labels—the solution is not to restrict intelligence, but to strengthen data governance at the source.

Learn more

How we did it at Microsoft

• See how we’re governing AI agents at scale internally. This readiness guide explains how we approach security and compliance for AI agents at Microsoft.
• Read about our experience tackling Microsoft 365 Copilot governance. This guide details our internal governance efforts for Microsoft 365 Copilot as we rolled the tool out across our organization.
• Explore how we’re powering data governance at Microsoft with Purview Unified Catalog. This story explains how Purview is helping us redefine our internal approach to data governance.

Further guidance for you

• Learn more about governance and security for AI agents and find out how to keep your AI initiatives secure.

Chapter 3: How our employees experience Work IQ day to day

Transforming the way work happens at Microsoft

To understand how Work IQ shows up and impacts the workflows of people across our organization, we spoke to several Microsoft employees. They explained how Work IQ makes a difference in the results they’re getting from Copilot and other agentic AI tools and how the intelligence is supercharging their work.

Work IQ in Outlook    

Outlook email and calendars are the space where many of our employees feel the heaviest cognitive load of their day‑to‑day work. It’s also where Work IQ is surfacing some of the most innovative ways to help employees accomplish more.

“You open your Outlook in the morning and Copilot—by drawing on Work IQ context and through features like priority scoring and summarization—can help you see which messages need your attention first.”

Matthew Marzynski, principal product manager, core experiences, Microsoft Digital

Rather than treating messages and meetings as isolated items, Work IQ allows Copilot in Outlook to reason across email signals, conversation history, meeting patterns, and calendar behavior to deliver responses that reflect how work actually unfolds.

This means Copilot goes beyond keywords or unread status indicators to determine importance. Through Work IQ, it understands the context of each conversation—which threads are more urgent and relevant to your work and which are less vital.

“You open your Outlook in the morning and Copilot—by drawing on Work IQ context and through features like priority scoring and summarization—can help you see which messages need your attention first,” says Matthew Marzynski, a principal product manager for core experiences in Microsoft Digital. “Copilot is now beginning to offer proactive nudges to help you stay on top of what matters, surfacing what’s changed and what you need to focus on.”

The deeper context also aids Outlook in generating rich summaries of lengthy threads, which can highlight owners, decisions made, and next steps. This allows employees who are added to the thread or who have been away to quickly catch up on complex conversations without manually digging through seemingly endless past messages or related documents.

Marzynski frames Work IQ as an invisible intelligence layer that quietly reshapes how Outlook behaves over time. His core thesis is simple: Users never have to think about Work IQ; they just observe that Outlook is more helpful than before, and that their work gets easier.

“There are no complex commands to learn or rules to create. The intelligence works behind the scenes as you use Outlook,” he says. “Your inbox just gradually feels more relevant. Outlook adapts to how you work, rather than the reverse, and becomes more like an assistant instead of a filing cabinet of communications.”

Work IQ in Teams + Researcher Agent

Another immediate and tangible way our employees experience Work IQ is in Microsoft Teams meetings. The value begins the moment a meeting is recorded. Transcripts, speaker contributions, shared content, and AI‑generated summaries are automatically captured and folded into the attendees’ ongoing work context—without requiring manual note‑taking or follow‑up documentation.

Ray Peer is a senior product manager in Microsoft Digital who observed the power of Work IQ in a recent project he completed with our internal legal team. According to Peer, the team was struggling to find specific content in their data lake, which contains tens of thousands of documents, articles, and other content items.

“Based just on what people shared in that meeting, and what it knows about their work and about SIPOC diagrams, Researcher was able to generate a fully formed, detailed solution for me. That’s the intelligence layer at work.”

Ray Peer, senior product manager, Microsoft Digital

So, he facilitated a Teams meeting for a free-form process‑mapping discussion with a few members of Microsoft Legal. Days later, he put the meeting transcript into the Copilot Researcher agent and asked it to generate a structured SIPOC (Suppliers, Inputs, Process, Outputs, Customers) diagram and accompanying documentation.

He was amazed by the results.

“Based just on what people shared in that meeting, and what it knows about their work and about SIPOC diagrams, Researcher was able to generate a fully formed, detailed solution for me,” Peer says. “That’s the intelligence layer at work. It reasoned over what we said—there were no visuals shared or anything—and it came up with something that I could cut and paste into the final format. I used to have to do that manually, and it took hours.”

Work IQ connected the meeting transcript to the people involved, the SharePoint sites they used, and similar work done elsewhere in the organization. Copilot was able reason across different tools and unstructured data, rather than just treating the meeting transcript as a static artifact.

Note that this works differently from third‑party meeting tools, because the data never leaves the tenant. Work IQ treats Teams meetings as part of a continuous Microsoft 365 workstream—honoring permissions and sensitivity labels throughout—so conversations can become durable inputs for future work without adding risk or effort for employees.

Work IQ in SharePoint

In SharePoint, Work IQ is helping employees create, organize, and publish content by drawing on the rich context of their Microsoft 365 data. Rather than starting from a blank page or text block, content development is sped up as Copilot draws on their relationships, collaboration history, and metadata to help produce sites and documents.

“Copilot will recommend text changes, but also layout suggestions, image and graphic options, and other helpful assistance. It makes it easy to create more compelling content, more rapidly.”

Sam Crewdson, principal product manager, Microsoft Digital

For example, when you ask Copilot to create a new section in a SharePoint site—such as a project overview, status update, or other material—Work IQ enables the tool to look deeper than the prompt itself. When generating the content, it can draw on documents you’ve recently edited, your emails and Teams conversations, and related work happening across the organization. The output you get from Copilot is highly relevant and grounded in real work.

Sam Crewdson is a principal product manager at Microsoft Digital who has been a part of the SharePoint team for more than two decades. He’s excited about what Work IQ is enabling users to accomplish in the product using Copilot, as well as other agentic tools like Knowledge Agent (a domain-specific agent that can drill down on SharePoint sites and libraries).

“Copilot in SharePoint is now able to not only help you produce better written content, it’ll also offer more contextual and visual help,” Crewdson says. “Copilot will recommend text changes, but also layout suggestions, image and graphic options, and other helpful assistance. It makes it easy to create more compelling content, more rapidly.”

Another emerging scenario Crewdson described is conversational agentic authoring in SharePoint. In these workflows, employees refine their SharePoint pages by interacting directly with an agent—asking it to add sections, adjust tone, or suggest visuals. Over time, these agents will reduce repetitive setup steps and help teams move from draft to publish faster.

Across these experiences, Work IQ is helping shift SharePoint from a manual content creation tool to an application where agents automate everyday content tasks based on your overall work context and related Microsoft 365 data.

Key takeaways

Here are some things to remember when thinking about how Work IQ can impact your employee workflows:

• Work IQ reduces cognitive load in Outlook by understanding work context. By recognizing decision‑driven threads, collaboration patterns, and urgency over time, Copilot helps employees focus on what truly needs their attention without relying on manual rules or keyword searching.
• Email and calendar intelligence improves prioritization, summaries, and follow‑through. Work IQ allows Copilot to highlight owners, decisions, and next steps in long threads and nudge users toward timely action, based on how they typically work with colleagues.
• Teams meetings become durable inputs for future work when powered by Work IQ. Copilot and the Researcher agent can reason across meeting content, people, and related SharePoint work—creating structured outputs while honoring tenant security and permissions.
• Work IQ helps Copilot speed up and enrich content creation in SharePoint. By drawing on Microsoft 365 data, Copilot can generate more relevant content for your SharePoint sites and offer helpful layout and graphics suggestions that accelerate the site development process.

Learn more

How we did it at Microsoft

• See how to pick the right Copilot for the job. This post explores different Copilot agents and modules and discusses how to choose the correct one for your task.
• Discover how we deployed Copilot internally at Microsoft. This step-by-step guide breaks down the deployment and adoption process we followed to roll out this groundbreaking technology across our entire organization.
• Find out how we’re using AI to redefine work in Outlook. This blog post explores the latest AI features that our employees are using to work smarter in Microsoft Outlook.
• Learn five ways we’re getting more out of Teams in the era of Copilot and AI. This article details different Teams features that take advantage of the latest in AI technology.
• Read about the lessons learned using SharePoint agents at Microsoft. This post covers some of the insights gleaned through our deployment of different SharePoint agents.

Further guidance for you

• Check out the Microsoft Copilot Success Kit and learn how to accelerate your Copilot implementation with templates, onboarding guides, and technical readiness resources.
• Explore the company’s essential guide to Copilot adoption for practical steps to onboard, engage, and measure impact in your organization.

Chapter 4: Work IQ beyond Microsoft 365

Integrating Work IQ across the enterprise

As organizations adopt Copilot and other AI agents at scale, the question arises: How does Work IQ show up in different contexts? Is it something that only impacts your work in Microsoft 365 applications, or does it also play a role in external applications and other areas of your IT enterprise?

Based on our experience here at Microsoft, the answer is that Work IQ shows up differently depending on where it’s consumed, and those differences matter for admins, agent developers, and other IT professionals.

For most of our employees, Work IQ operates entirely behind the scenes inside Microsoft 365. It is not something users configure, enable, or interact with directly. By reasoning over your entire Microsoft 365 data graph, Work IQ improves the results that Copilot generates in apps like Outlook, Teams, Word, SharePoint, Copilot Chat, and GitHub Copilot.

In this mode, Work IQ is:

Built directly into the Microsoft 365 intelligence stack

Governed by existing permissions, sensitivity labels, and compliance policies

Invisible to users, with no new controls or UI to manage

Continuously learning from signals across emails, meetings, and files

You don’t “implement” Work IQ—it’s already present in first-party Microsoft products by default. If you have enabled Copilot, you are getting the benefits of Work IQ across all of these applications. 

Similarly, any agents you build for Microsoft 365 apps (such as using Agent Builder in Microsoft 365 Copilot) are scoped for use specifically in these apps, rather than outside of them. These agents do not require separate connectors, such as APIs or Model Context Protocol (MCP) servers, to access Work IQ. In fact, Work IQ MCP is a great tool to make your context ubiquitous to whichever agentic experience can be imagined.

Extending Work IQ beyond Microsoft 365: explicit by design

Implementation works somewhat differently outside of native Microsoft 365 experiences. When it comes to custom agents, line‑of‑business applications, or Azure‑hosted solutions, Work IQ does not show up automatically. In these contexts, it is intentionally enabled by our builders and governed by our administrators.

In these scenarios:

• Developers access Work IQ through APIs or MCP servers
• Admins explicitly control which capabilities are enabled or disabled
• Work IQ provides rich enterprise context without duplicating data
• Permissions and governance remain enforced at the tenant level

For us, this design is deliberate and has advantages. Rather than asking our developers to configure dozens of individual connectors for mail, calendars, files, and meetings, Work IQ offers them a single-entry point for enterprise context. Builder tools like Microsoft Foundry and Copilot Studio allow our teams to take the same Work IQ intelligence that Copilot uses and apply it to workflows that live outside Microsoft 365. Examples include automating newsletters, generating insights for account teams, or powering custom agents to handle specific scenarios.

The key distinction is accountability. Inside Microsoft 365, Work IQ is ambient. Outside it, Work IQ is a conscious architectural choice, one that requires actions upfront to enable.

Work IQ does not “open up new data” when used externally. It ports intelligence, not raw access, applying the same rules no matter where it’s consumed. At the same time, it gives organizations flexibility to decide when and how far that intelligence should travel.

This continuum—from implicit use inside Microsoft 365 to explicit use beyond it—also clarifies our roles:

• Our end users benefit without needing to learn anything new
• Our IT teams retain centralized control at the tenant level
• Our builders gain a faster path to context‑aware solutions

Work IQ works best when treated as a shared intelligence foundation, not a feature toggle. It is present by default where trust is already established, and it can be incorporated deliberately where your organizational requirements or innovation needs demand more reach.

Model Context Protocol servers and Work IQ

For organizations that move beyond native Microsoft 365 experiences and begin building custom agents, Model Context Protocol (MCP) servers are the primary mechanism for connecting those agents to Work IQ. While Work IQ is always available inside Copilot, MCP servers are what make much of that same intelligence accessible to agent builders.

At a high level, MCP servers are an open-standard technology (not proprietary to Microsoft) that act as governed tool interfaces to enterprise context. Each Work IQ MCP server represents a scoped slice of Microsoft 365 signals—such as email, calendar, Teams activity, or SharePoint content—and exposes them in a form that agents can reason over. Rather than wiring individual connectors or APIs for each workload, builders can rely on MCP servers to assemble relevant context automatically, while still honoring permissions, sensitivity labels, and tenant policies.

When we’re building agents, Work IQ becomes explicit, and MCP servers are how our builders declare their intent. This includes determining which types of enterprise context the agent needs, how broadly it should reason across work signals, and where governance boundaries apply.

From an IT perspective, MCP servers also provide a critical control point. Our administrators decide which Work IQ MCP servers are enabled in the tenant and which of our builders are allowed to use them. This ensures that extending intelligence beyond Microsoft 365 remains a deliberate choice rather than an accidental one.

Using these servers to connect with your enterprise data also represents real—but manageable—risk. They make existing permissions more actionable, which can amplify the impact of overshared content or weak data hygiene. The best practice is to treat these servers as governed infrastructure: enable them selectively at the tenant level, start with the minimum set required for defined agent scenarios, restrict usage to approved builders, and pair expansion with regular permission reviews and labeling discipline.

Your readiness plan should be to ensure that governance is in place, then selectively enable MCP servers where agents require deeper context. The servers are the bridge that lets agent builders tap into Work IQ safely, allowing you to bring enterprise intelligence into custom solutions without breaking the trust model that makes Copilot effective at scale.

Key takeaways

Here are some things to remember when thinking about how Work IQ shows up across your organization—especially if you plan to extend this intelligence into custom agents and applications:

• Work IQ is foundational inside Microsoft 365 and intentional outside it. Within Copilot experiences, Work IQ operates implicitly, while custom agents introduce a conscious decision to consume that intelligence through MCP servers.
• Governance principles don’t change when extending Work IQ, but they become more visible. MCP servers enforce existing permissions, labels, and tenant policies, making it critical that governance foundations are solid before agents rely on deeper context.
• Agent builders declare intent through MCP server selection. Choosing which Work IQ MCP servers to use defines what enterprise signals an agent can reason over and how broadly it reflects real work patterns.
• Preparing to extend Work IQ beyond Microsoft 365 is about readiness. Organizations that are already ready for Copilot can selectively enable MCP servers to unlock richer agent scenarios without introducing new security or compliance risk.

Learn more

How we did it at Microsoft

• Learn how we’re shaping AI management at Microsoft with Agent 365. This post discusses how we use the Agent 365 control plane to govern Microsoft 365 Copilot and agents internally.
• Read our complete guide to deploying AI agents internally at Microsoft. This Inside Track readiness guide covers all aspects of how we’ve implemented agentic AI across our organization.
• Discover how we’re unlocking enterprise AI extensibility with Copilot Studio. This story explores how we govern more advanced agents built with Copilot Studio, including connectors, orchestration, and scaled governance.
• Check out how we’re protecting AI conversations at Microsoft with Model Context Protocol security and governance. This article delves into MCP governance through secure-by-default architecture, automation, and inventory for faster, safer agent development.

Further guidance for you

• Read this Work IQ MCP overview on the Microsoft Learn site to find out more about how agents connect using Model Context Protocol servers.
• See the Work IQ Copilot reference on the Agent 365 Microsoft Learn site for further technical detail.

Chapter 5: Working with Work IQ: The Customer Zero impact

Change management lessons from our experience with an ambient intelligence layer

Work IQ wasn’t rolled out across our organization as an abstract platform decision or deployment milestone. Its development has been one aspect of our overall transformation into an AI-first Frontier Firm.

Along the way, Work IQ has been shaped by our long‑standing Customer Zero mission at Microsoft Digital: Using our own products at enterprise scale first, learning directly from how employees experienced it, and allowing those lessons to shape how the technology is refined and extended to customers.

In our tenant, Work IQ benefits emerged gradually through incremental improvements to relevance, context, and intelligence across Microsoft 365. These gains were driven by advances in AI that made it possible to interpret everyday work signals more effectively.

There was no formal product implementation or adoption campaign when we launched Work IQ at Microsoft. As ambient infrastructure, Work IQ is an unseen part of all employee workstreams—nearly every experience benefits from it. At the same time, the power of Work IQ depends on everyone in our organization being effective stewards of their own unstructured data, preserving security, governance, and relevance.

Enablement and adoption

To fully realize the value of Work IQ, we have found that organizations must invest in the foundational behaviors that make their organizational knowledge accessible. One of the key steps in this effort is enabling and encouraging the use of meeting transcripts. Work IQ depends on the artifacts of daily work to build context, and without transcripts, a significant portion of meeting insights and decisions remain inaccessible to the intelligence layer.

Making transcription a standard part of our employees’ everyday collaboration proved essential. Transcripts create a durable, searchable record that Work IQ can connect to documents and actions, helping employees quickly understand what happened, even if they weren’t present. When paired with existing governance controls like sensitivity and meeting labels, organizations can capture this data securely while unlocking great value from this collective knowledge.

This is actually a cultural shift.

We gave our teams clear guidance and encouraged meeting transcription as part of their normal workflow. When paired with the enhancements to meeting recaps in Microsoft Teams, this becomes a powerful tool for preserving and leveraging organizational knowledge.

Of course, Copilot adoption and training efforts were also a vital part of our getting the most from Work IQ. Our employees needed demonstrations of all the things that Copilot could help them accomplish, along with encouragement to jump in and try it out for themselves. Our data shows that internal AI usage has grown significantly over time—from a few thousand users to hundreds of thousands across the company—in large part due to:

• Employee-driven champions programs
• Scenario‑based learning efforts
• Timely and consistent internal communications

Usage also grew internally as our product teams continually refined our AI tools, aided by our collection of user feedback on agentic answers to identify low-quality output and irrelevant detail.

Another major insight we captured was the importance of persistent memory to the Copilot and Work IQ experience. Through our work as Customer Zero, we collected a large volume of feedback from employees indicating that this was a priority—users should not have to repeatedly explain who they are or what they are working on.

The experience was subsequently improved, and Work IQ now helps enable Copilot to remember user history and tailor responses accordingly—delivering summaries for communicators and deeper technical detail for engineers, for example.

Our Customer Zero efforts also validated a critical governance principle for us. As intelligence improved, some teams were surprised by how much context Copilot could surface. In every case, investigation showed that the underlying data access already existed. Work IQ did not change permissions or expose new data—it made existing relationships more visible. This reinforced the importance of strong data hygiene, sensitivity labeling, and permission management as prerequisites for trusted intelligence.

Ultimately, our work as the company’s Customer Zero validated that Work IQ is best understood as shared infrastructure. Its value compounds when organizations focus on readiness—governance, learning, and trust—and allow intelligence to scale naturally across work, rather than treating it as a feature to deploy.

When these conditions are in place, Work IQ quietly raises the quality of Copilot and agent experiences without adding complexity for users or additional burden for IT.

Key takeaways

As you consider how Work IQ might take shape in your own organization, consider these observations from Microsoft Digital’s Customer Zero experiences with this new intelligence layer:

• Meeting transcription is the key. Making sure all meetings are transcribed is essential for Work IQ, so it can build context on how work happens in your organization. This is a technical and cultural change that you need to facilitate and encourage.
• Awareness and learning are keys to usage and feedback. Our internal Copilot adoption grew when employees were shown practical scenarios and encouraged to experiment, supported by champions programs and ongoing internal communication.
• Change management drives results. Use employee champions, role-based immersive learning, and timely internal communications to help your employees understand what Work IQ is and how it can help your enterprise maximize the value of AI.
• Treating Work IQ as shared infrastructure unlocks compound value. When governance, learning, and trust were in place, intelligence could reason across all our rich unstructured data —improving Copilot and agent experiences without adding additional work for users or IT.

Learn more

How we did it at Microsoft

• Read about how we serve as Customer Zero in an AI-powered world. This story describes how Microsoft Digital’s role as Customer Zero has grown and evolved as we move into the Frontier Firm era.
• How to pick the right Copilot for the job. This post explores different Copilot agents and modules and discusses how to choose the correct one for your task.
• Recapping our meetings with Microsoft Teams Premium. This Inside Track story explains how Intelligent Recaps have changed the game when it comes to getting the most out of meetings at Microsoft.

Further guidance for you

• Check out the Microsoft Copilot Success Kit and learn how to accelerate your Copilot implementation with templates, onboarding guides, and technical readiness resources.

Where we’re heading: Work IQ, Fabric IQ, and Foundry IQ

Combining different layers of intelligence to transform the workplace

While impactful on its own, Work IQ is just part of larger story of how we’re using the power of rich data and agentic AI to transform how we work at Microsoft.

“While Work IQ can access your Microsoft 365 data, Fabric IQ will connect to your organizational data, such as analytics. Foundry IQ can leverage both, plus other domain data, to help developers build powerful agentic solutions.”

Naveen Jangir, principal architect, Microsoft Digital

Work IQ is one layer. It allows our AI tools to reason over unstructured data so this powerful resource can be a part of our larger enterprise intelligence system. But it also includes two other aspects of this three-layer system—Fabric IQ and Foundry IQ. Combined, these three capabilities enable organizations to take full advantage of your knowledge estate to forge the AI-powered workplace of the future.

“While Work IQ can access your Microsoft 365 data, Fabric IQ will connect to your organizational data, such as analytics,” says Naveen Jangir, a principal architect in Microsoft Digital. “Foundry IQ can leverage both, plus other domain data, to help developers build powerful agentic solutions.”

Here’s how these capabilities work together in complementary roles to impact how work gets done at Microsoft:

• Work IQ handles unstructured data—like documents, emails, PDFs, and web content—by extracting meaning and context from human language.
• Fabric IQ operates over structured data—like tables, databases, metrics, events, and transactions—to bring consistency and analytic rigor to our work.
• Foundry IQ provides the knowledge-grounding layer, where entities, relationships, and ontologies allow reasoning to stay aligned with enterprise truth.

While each component is powerful on its own, the deeper value is what becomes possible when they are used together.

The intent is to enable agents that can reason across all enterprise knowledge, regardless of where it originated or how it was stored. An agent should be able to read a policy, connect it to operational data, understand who and what is involved, explain its conclusions, and take an action (if desired) through a shared ontology based on organizational context.

That kind of capability can’t emerge just from information retrieval. It requires shared meaning across systems, content, and data types.

This is where the role of Work IQ becomes especially important. We have found that unstructured data contains some of the most critical institutional knowledge an organization has, but it rarely arrives in a form that is ready to be reasoned over. Documents reference people, systems, processes, and timelines in ways that make sense to humans, but not to machines. They can also fall out of date or represent a draft state that was never meant to be presented as verified information.

Work IQ bridges this gap by transforming the raw text into structured understanding, without stripping away nuance.

“Work IQ is already helping us change the way that work gets done. Instead of hunting for information or stitching context together manually, our employees can focus on decisions, creativity, and outcomes—because the intelligence is already there, working with them every day. It’s an integral part of preparing our organization for our agentic AI future.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

The crucial mechanism for that transformation is entity extraction, paired with a shared ontology. When a document mentions an employee, a system, a regulation, or a product, Work IQ identifies that reference as something concrete and reusable. Over time, those entities become the connective tissue between unstructured content, structured records in Fabric IQ, and the semantic backbone that Foundry IQ relies on to ground reasoning in the agents we create.

We can already see signs of this promised future at Microsoft today. Take a tool like our Employee Self-Service Agent, which we launched late last year. What before was a collection of static HR documents becomes a living knowledge system: policies are parsed, roles and eligibility criteria are extracted, and guidance is grounded in an understanding of employee role and location. The agent can answer a question and explain why the answer applies, because it understands both the document and the organizational context behind it.

This is why Work IQ is such a strategic capability. Improving document quality, normalizing metadata, resolving entities, and establishing governance are not one-off hygiene tasks. They expand what future agents will be able to do safely and reliably. The more coherent your unstructured data becomes, the less guesswork agents must do and the more context they can absorb.

“Work IQ is already helping us change the way that work gets done,” says Vijaya Alaparthi, a principal group product manager in Microsoft Digital. “Instead of hunting for information or stitching context together manually, our employees can focus on decisions, creativity, and outcomes—because the intelligence is already there, working with them every day. It’s an integral part of preparing our organization for our agentic AI future.”

For us, the direction forward is clear. The better your data foundation, the more capable—and trustworthy—your agents become. As unstructured and structured knowledge converges, intelligence stops being a set of isolated features and becomes a system.

Organizations that invest in technology like Work IQ to harness their unstructured data as enterprise knowledge are the ones that will deploy the most capable agents going forward and will be best positioned to take advantage of the agentic future.

Key takeaways

If you want your organization to be able to use Work IQ to propel your own agentic transformation, consider what we’ve learned on our journey:

• Work IQ transforms unstructured enterprise data into actionable intelligence. By reasoning over emails, documents, meetings, and chats, it unlocks institutional knowledge that was previously fragmented and underused.
• The intelligence operates as foundational infrastructure, not a user-facing feature. Work IQ runs continuously behind the scenes across Microsoft 365, improving Copilot and agent responses wherever they appear without configuration.
• Context is what makes Copilot feel truly intelligent. By combining signals from collaboration patterns, conversations, documents, and more, Work IQ enables agents to respond based on how work actually happens, not just what information can be retrieved.
• Security and governance remain intact because Work IQ inherits existing controls. It doesn’t create new access to data; it reveals relationships while fully honoring permissions, sensitivity labels, and compliance policies.
• Employees experience Work IQ as reduced cognitive load, not added complexity. Inbox relevance, richer summaries, and clearer follow-through improve naturally over time.
• Using Work IQ beyond Microsoft 365 is a deliberate, governed choice. MCP servers allow builders to bring enterprise context into custom agents while giving IT teams clear control over scope, access, and risk.
• Work IQ is the foundation for the next generation of agentic intelligence, especially when combined with Fabric IQ and Foundry IQ. The more coherent and well-governed your unstructured data is today, the more capable, explainable, and trustworthy your future agents will become.

Learn more

• Explore how Work IQ is helping supercharge AI usage at Microsoft.
• Discover our chapter-by-chapter internal deployment guide for Microsoft 365 Copilot.
• Check out how we’re embracing this agentic moment at Microsoft.
• Dive into a Microsoft Learn overview of different Work IQ agent tools.
• Read an IT playbook for the AI era and learn how we’re becoming a Frontier Firm at Microsoft.
• See how we’re using Model Context Protocol security and governance to protect AI conversations at Microsoft.
• Learn about our experience governing AI agents at scale in this readiness guide.
• Find out how we’re unlocking enterprise AI extensibility with Copilot Studio.

Try it out

Get a closer look at Work IQ.

The post Intelligence on tap: How Work IQ enables AI and agents at Microsoft appeared first on Inside Track Blog.

Our Global Collection team in the Microsoft Treasury division makes sure payments are seamlessly executed in our fast-moving global enterprise environment. However, our case managers were often losing valuable time figuring out things like who the right contact was for a given customer, which issues were likely to be challenged by a customer, and where an exception should be routed next. This information was spread across systems or buried in handoffs.

To solve these challenges, our team built a human-led, AI agent-assisted support system to reduce preparation time and streamline their processes.

“Building the AI assistance wasn’t the hard part,” says Kathy Brustad, a director in the Global Treasury and Financial Services division at Microsoft. “The hard part was reimagining the collection experience with AI front and center, and bringing the underlying infrastructure up to speed to get it there.”

In this post, we explain how we did it so you can learn from our experience.

“We have over 1,000 collectors around the world who perform collections for Microsoft. They had multiple systems they had to go to in order to find out things like the totality of the customer’s invoice and what conversations a different team had with the customer. The information was fragmented.”

Kathy Brustad, director, Global Treasury and Financial Services

Stitching together information across systems

Our AI agent is focused on helping our case managers prioritize high-value work by:

• Predicting late payments and possible customer disputes
• Summarizing customer case interactions for use by case managers
• Routing customer emails to the right collections manager faster and with greater precision Automatically matching payments to invoices
• Automatically responding to customer inquiries

“We have over 1,000 collectors around the world who perform collections for Microsoft,” Brustad says. “They had multiple systems they had to go to in order to find out things like the totality of the customer’s invoice and what conversations a different team had with the customer. All of this information was fragmented. We didn’t have a single view of how much a customer owed us.”

We started by consolidating these dispersed tools and systems into an SAP and Microsoft Dynamics 365 environment, creating a single source of truth for all relevant customer, invoice, and payment data.

On that foundation, we layered on Microsoft’s IQ intelligence platform to infuse semantic understanding and business context. That standardized our workflows by simplifying templates and worklists to reduce complexity and put consistent global practices into place. Routine communications became fully automated.

We then applied AI to improve payment matching accuracy from 40% to 90%, generate customer response drafts, and intelligently route cases to reduce time-consuming back‑and‑forth.

Copilot assistance was embedded directly into the daily workflow of our case managers to reduce administrative load by providing inline knowledge suggestions, summarizing calls, and automatically drafting replies. With these standardized automated workflows, we could apply 98% of payments within 48 hours.

“In a nutshell, this is the collection story: We have various agents and models deployed to assist our human agents with all the activities they have to do, saving hundreds of thousands of hours that we spent on manually tracking things before.”

Kathy Brustad, director, Global Treasury and Financial Services

Moving faster on ‘act ready’ work

Deploying the agent was only the starting point. The harder work was helping our collection team change established ways of working. Brustad described the shift as learning to “run it in a different way,” moving from manual, fragmented preparation toward workflows where prioritization, context gathering, and routing were increasingly supported within the system.

To make that shift possible, the team introduced a change management work stream program and role-based training focused on real, day-to-day scenarios alongside the rollout. By anchoring the work in clear business pain points and showing tangible improvements, our team saw how the new approach made their work easier. Each morning, the agent prioritized each case manager’s workload according to urgency and past client behavior so case managers could immediately focus on the accounts that were the most pressing.

We reduced repetitive communications using automatically drafted responses and automated statements.

“In a nutshell, this is the collection story: We have various agents and models deployed to assist our human agents with all the activities they have to do, saving hundreds of thousands of hours that we spent on manually tracking things before,” Brustad says.

After deploying this system to our case managers, we saw measurable improvements in both productivity and speed, including:

• Hundreds of thousands of hours unlocked annually in order to do more human-led high-value work rather than routine administrative tasks
• 40% reduction in call preparation time
• 2X growth in automatic cash applications
• 2.5X acceleration of customer inquiry resolution time

Operationally, the team also saw up to 60% reduction in inquiry handling time through inline suggestions, summarized calls, and automatically drafted replies. To ensure these improvements were real and repeatable, we emphasized observability in our evaluation approach. Our team tracked dollars collected through collections and hours worked to create productivity metrics.

Data, trust, and good governance

When introducing AI systems or agents into finance workflows, leaders often ask two questions:

1. Can we trust the outputs?
2. Can we govern the process?

“The biggest takeaway is to know your own process very, very well. You need to understand where all the bottlenecks and pain points are. Start from there to design the new agent-enabled process instead of saying, ‘I’m going to just inject the agent into my existing process.’”

Kathy Brustad, director, Global Treasury and Financial Services

For us, trust came from getting the basics right in the form of right-sizing our enterprise data, standardizing our workflows, and establishing clear ownership for each part of the work. When we tested early and included frontline users throughout the process, outcomes improved.

“The biggest takeaway is to know your own process very, very well,” Brustad says. “You need to understand where all the bottlenecks and pain points are. Start from there to design the new agent-enabled process instead of saying, ‘I’m going to just inject the agent into my existing process.’”

Embed custom agent assistance directly into the moments where time disappears, such as prioritization, preparation, routing, and drafting so adoption feels natural and can be measured. You can prove impact with a small set of metrics like cycle time, throughput, dollars collected, and hours saved, and iterate from there.

Key takeaways

Modernizing collections is about fixing the fundamentals first, before you add AI into the mix. As you begin to streamline your own finance workflows, keep these lessons in mind:

• Fix fragmented workflows before adding intelligence: AI delivers the most value when it’s layered on top of standardized processes and a unified data foundation rather than disconnected systems and ad hoc handoffs.
• Embed assistance where time is actually lost: Copilot-style support works best when it shows up directly in prioritization, preparation, routing, and drafting to reduce friction without changing how people work.
• Focus AI on high‑ROI decisions, not just automation: Predicting late payments, flagging likely invoice disputes, and surfacing context can help teams spend time where it matters.
• Design around the practitioner’s day: When work arrives prioritized and prepped, case managers spend less time chasing context and more time resolving exceptions.
• Measure what matters to prove impact: Cycle time, dollars collected, throughput, and hours saved provide a clear, repeatable way to track productivity gains and cashflow velocity.
• Pair generative AI with strong governance: Trust comes from clear ownership, standardized workflows, quality data, and ongoing human oversight.

Editor’s notes:

• SAP is an enterprise finance system that many organizations use to manage invoices, payments, and financial records in a single, centralized platform.
• All metrics cited are based on Microsoft internal data gathered during the writing of this article. They’re best read as directional signals from that period, and they may change as systems, processes, and behaviors evolve. Microsoft makes no warranties, express, implied, or statutory.

Try it out

• Build and test an AI‑assisted workflow with Microsoft Copilot Studio to see how agents can help prioritize work, summarize context, and route cases more efficiently.
• Start a Dynamics 365 free trial.

Related links

• Discover the five lessons we learned on how to make AI stick for sellers during our Copilot rollout.
• See how we revamped and modernized our Microsoft Treasury infrastructure using Azure.
• Explore how we’re supercharging our support experience with Microsoft Dynamics 365 and AI.
• Learn how we’re transforming the marketing function at Microsoft with AI.
• Read about our role as Customer Zero in an AI-powered world.
• Check out how we’re measuring the impact of Microsoft 365 Copilot and AI internally.

The post Streamlining finance cash collection at Microsoft with AI appeared first on Inside Track Blog.

• Introduction
• Chapter 1: Building your agent governance strategy
• Chapter 2: Establishing a solid data foundation for agent governance
• Chapter 3: A matrixed approach to agent governance
• Chapter 4: Tracking, impact, and value
• Conclusion: Governing the frontier

Empowering employees and protecting your organization through agent governance

Welcome to the agentic frontier

Agents are expanding the frontier of enterprise AI. By creating tools that surface knowledge, take actions, and even reinvent workflows, organizations can apply the power of AI to business processes in new and innovative ways.

But this shift raises questions for business and IT leaders: How do you get the benefits of agents without putting your organization and employees at risk? How do you encourage citizen developers to create agents freely while maintaining control, security, privacy, and compliance?

At Microsoft Digital, the company’s IT organization, we’re putting practical governance structures in place to ensure our internal agents are useful, safe, and properly scoped. Through a deliberate strategy of empowerment with established guardrails, we’re unlocking the potential of agentic transformation while maintaining the trust that defines our work.

Read and prepare

Check out our complete guide to deploying and adopting agents across the enterprise, based on our own experience here at Microsoft.

The AI maturity model and frontier transformation

Agentic AI has made a new operational model possible, one that blends machine intelligence with human judgment, creating AI-operated, human-led teams.

We call organizations that enact this model Frontier Firms.

As organizations move toward this new operational state, they progress from foundational AI assistance through escalating levels of agentic maturity and complexity. First, humans operate with help from an AI assistant like Microsoft 365 Copilot. Then, human-agent teams work together. But the future lies with humans leading teams of agent users: AI agents that perform core labor with relative autonomy.

Capturing the benefits of this model relies on many factors, but in our experience as Microsoft Digital, two main tenets are instrumental to a successful transformation:

1. Empowering employees and teams to create and experiment with their own agents
2. Properly governing those agents to protect the enterprise

It’s a balance. If you set agent builders free without the proper guardrails, you risk data overexposure, agent sprawl, and security vulnerabilities. However, being too restrictive about governance stifles individual imagination, workflow reinvention, and innovation that can come from agentic AI.

“At Microsoft, we’ve moved beyond envisioning the agentic future into operating within it every day. Our experience as Customer Zero gives us a unique perspective on what it takes to govern AI agents at scale, turning early lessons into proven practices that help organizations innovate with confidence.”

Brian Fielder, vice president, Microsoft Digital

We’re here to help you find the right balance for your organization.

This guide shares what we’ve learned along the way. As you read, you’ll follow our journey as Customer Zero at Microsoft, and you’ll gain access to tips and resources that we’ve assembled to help you apply our expertise to your own agent governance practice.

Every organization is different, and your experience will differ from ours in terms of risk tolerance, technical capability, resourcing, and more. This guide highlights some principles and best practices you can apply to your own business context, needs, and objectives.

“At Microsoft, we’ve moved beyond envisioning the agentic future into operating within it every day,” says Brian Fielder, vice president of Microsoft Digital. “Our experience as Customer Zero gives us a unique perspective on what it takes to govern AI agents at scale, turning early lessons into proven practices that help organizations innovate with confidence.”

Now is the time to seize this opportunity. Follow along to start your own journey toward frontier transformation and capture the benefits of trusted, connected agentic intelligence.

Learn from our experience governing agents

Within Microsoft Digital, we’ve been acting as Customer Zero for frontier transformation by creating the tools, infrastructure, and processes that power agents at Microsoft.

Our goal is to make it easy for employees to engage with agentic tools freely and adaptably while maintaining safety and responsibility. The path to this objective relies on a three-pronged approach to governance:

• Embedded governance functionality: Agent creation and publishing tools should incorporate good guidance, governance, and guardrails out of the box, making agents people create essentially self-governing.
• IT oversight: This is a new space and a new way of working, so it isn’t feasible for all agents to self-govern at this point. As an IT organization, we fill gaps in governance through reviews and oversight. We establish risk-based policies around types of agents, exposure and sharing, and other pivots.
• User education: It’s almost impossible to predict every governance gap and need, so educating our users helps them avoid accidentally increasing risk. Our Agents at Microsoft team and individual change managers are the guides for these efforts. Employees can also refer to resources like Microsoft Learn courses and the Agent Builders SharePoint hub.

Throughout this journey, we’ve empowered our employees to create all kinds of agents, ranging from simple personal tools built by people working in every function, with every level of technical skill, all the way to AI-powered enterprise tools designed by professional developers for use across lines of business and even the entire company.

As part of the process, we’ve incorporated guardrails to ensure less technical employees are limited to tools that simply retrieve enterprise knowledge, such as SharePoint Agent Builder or Copilot Studio, while software engineers get the full power of any tool they need that can take action or automate workflows, including Microsoft Foundry and Microsoft 365 Agent Toolkit.

Over the course of this journey, we’ve learned valuable lessons about effective agent governance, including:

• How to build an impactful but flexible governance strategy
• Strategies for creating an AI-ready data ecosystem
• Ways to apply appropriate policies and controls for highly diverse agents
• Approaches for tracking the impact and value of agents

Chapter 1: Building your agent governance strategy

Thinking through your organizational needs and building a framework to govern agents

As we’ve incorporated agents into different aspects of our organization, we’ve also deepened their involvement in employees’ daily workflows and core business processes. Because of this, we’re diligent about the governance guardrails and policies that protect our organization.

We’ve accumulated a wealth of knowledge and insights in this area through our efforts governing Microsoft 365 Copilot. Based on this experience, some of the key priorities that we made sure to adhere to included:

• Effectively applying controls to ensure users and apps don’t get access to privileged information
• Preventing employees from creating agents that violate company policies
• Balancing the freedom for employees to share their creations with the need to prevent agent sprawl
• Delineating which agents are authoritative and applicable for enterprise functions and which ones are meant for employees’ own personal use.
• Inventorying agents to provide lifecycle management
• Securing and protecting confidential data while respecting our responsible AI principles: Fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness
• Unlocking telemetry that enables us to govern agents effectively

By focusing on each of these dimensions, our governance team has centered its efforts on the value these agents provide to the company while also ensuring organizational safety and trust. To realize this value, we emphasize three key principles that help protect both our employees and the organization:

Security

We’ve established standards for data classification, policies for handling confidential information, and other security measures to protect data from unauthorized access, misuse, and disclosures. Microsoft Purview powers these capabilities through data labeling, rights management, and data loss prevention.

Privacy

Privacy compliance measures keep personal data protected and ensure agents adhere to regulatory frameworks in the regions where we operate. We conduct regular privacy assessments for all applications, including high-impact agents.

Regulation

Regulatory compliance assessments ensure agents meet prevailing legal standards. Our legal and compliance teams carefully monitor AI guidelines, regulations, and laws as they evolve so we can understand and incorporate them into these assessments.

We incorporated elements of our tenant’s minimum bar for governance into how we secure agents. Those include Microsoft Purview Information Protection, a functional inventory, activity logging, lifecycle management, and the ability to properly isolate agents so that they don’t cross data boundaries.

Our overarching tenant governance strategy is to govern items like documents and data at the container level. However, within a SharePoint site, for example, the added functionality of agents demands that we introduce further controls like sharing limits, breadth of knowledge sources, agent metadata, and information about an agent’s behaviors.

Turning priorities into principles

To operationalize governance, we developed six principles that guide our approach to agents. They form the governance foundation for a wide matrix of agent creation and usage opportunities.

1. We ensure a strong data hygiene foundation so we can trust our data estate as employees build and use agents.
2. We empower employees to build personal agents that can access permitted services and data sources to help automate and accelerate their tasks.
3. We empower teams and lines of business to build agents with known lower-risk patterns to accelerate impact.
4. We provide a smooth release path for engineering teams to develop agents designed for enterprise functions so they can access all the services and sources they need. This includes the same software development lifecycle (SDLC) reviews and certifications as other enterprise software, which we outline in Chapter 3.
5. We accelerate innovation through agent and automation templates while maintaining an AI Center of Excellence (CoE) to help teams think through their opportunities.
6. We reimagine employee experiences and task execution to simplify and optimize productivity.

Read and prepare

Powering the technical veracity of AI at Microsoft with a Center of Excellence.

Securing control through agent lifecycles

As we strategized to operationalize good governance, agent lifecycles became one of our most crucial tools. We superimposed the enterprise lifecycle on top of these policies, with both user-based and attestation-based lifecycles.

This means we treat agents owned by individual employees like any other user app and delete them when they leave the organization. Meanwhile, we ensure that agents owned by teams have a lifecycle that’s defined by the tenant and tied to attestation, our internal enterprise SDLC, and accountability confirmations.

This approach helps us combat sprawl by eliminating agents that no longer serve a purpose. It provides a solid foundation for more fine-tuned, matrixed policies and practices.

Governing amid real-time technology acceleration

One recent development illustrates how the rapid advancement of AI technology requires us to stay ahead of policy for new features.

Model Context Protocol (MCP) adds new capabilities, but also new risks and challenges. It’s a simple standard that lets AI systems communicate with the right tools and data without custom integration work. Instead of building a new connection or API every time, teams plug into a common pattern.

That standardization delivers speed and flexibility, but it also changes the security equation. We’ve extended our security and governance practices to account for MCP servers.

Our practices and policies help us govern agents effectively in this new environment. First, we assess security across four layers: Applications and agents, the AI platform, data, and infrastructure. We establish a secure-by-default strategy by positioning every remote MCP server behind our API gateway and establishing practices for vetting, identity management, automation that slows agents at the right moments, context trimming, and server isolation.

As you define policies for governing your own agentic ecosystem, you can take inspiration from our process. Start by asking questions about what you want to accomplish and what you want to protect, then move on to establishing your most important priorities. From there, you can cement those priorities into policies.

Learning from our approach to agent governance strategy

Match policies to progress on your AI journey

The complexity of agent governance depends on the maturity of your organization and where you are in your adoption journey. Start slowly to let that maturity grow over time.

A strong policy framework is the foundation

Lean on existing app governance policies, then layer agent-specific structures on top.

Take your cues from established standards

Global regulations around privacy, security, and responsible AI provide a good baseline for establishing governance policies. Assign teams to work through these regulations and incorporate their insights into your agent governance strategy.

Decide on your comfort level with risk

Bring cross-disciplinary experts together from across your organization to determine what level of risk is acceptable for different agents and their use cases. Put guardrails in place for low-risk scenarios and establish processes for supporting more complex or sensitive use cases. Evaluate what data sources agents can extract information from. Establish whether users have shared sensitive data sources.

Change is constant

Plan to reassess and revise your governance structure regularly. Agents are evolving rapidly, as is the tooling surrounding them, so maintaining good governance policies will be an ongoing practice.

Governance is a value driver for employees

Governance isn’t just about protecting your organization. It also provides the right patterns to make sure your employees are getting value from agents. Establish strong measures of business value and a robust methodology for management and assessment of agents through ongoing tracking. This kind of observation and telemetry is foundational and should be a key part of your governance efforts.

Key takeaways

Use these tips based on what we learned here at Microsoft to build your strategy for agent governance at your company:

• Establish a cross-disciplinary agent Center of Excellence. Bring together stakeholders across the organization to define priorities, goals, and shared practices for agent adoption.
• Right-size oversight based on risk. Determine your organization’s risk tolerance and define which agents require more or less involvement from IT, security, and compliance teams.
• Operationalize agent oversight and management. Establish an oversight model and implement tools that help manage agents at scale.
• Establish change management and adoption. Determine and implement a strategy for driving adoption to educate and empower employees.
• Create a centralized governance and information hub. Provide employees and agent builders with a single place to find guidance, standards, and governance information.

Learn more

How we did it at Microsoft

• Find out how we’re protecting AI conversations at Microsoft with MCP security and governance. This post outlines how we’re streamlining MCP governance through secure-by-default architecture, automation, and inventory.
• Explore our IT playbook for the AI era and learn how we’re becoming a Frontier Firm. This article shares our journey as an IT organization in support of this new operating model.
• Discover how Microsoft is becoming an AI-first Frontier Firm. This story shares how we’re approaching the idea of an agentic future.
• Read our five-step guide for IT leaders who want to drive greater AI maturity. This resource can help you chart a course through AI maturity to reimagine what’s possible for the enterprise.
• Learn from our experience tackling Microsoft 365 Copilot governance. This guide details our governance efforts for Microsoft 365 Copilot and can serve as a starting point for agentic governance.

Further guidance for you

• Secure your agents at scale with Microsoft Agent 365 guidance for unified identity, compliance, and control across platforms.
• Learn about the responsible AI policies and practices that guide our use of AI at Microsoft.

Chapter 2: Establishing a solid data foundation for agent governance

Setting agents up for success using a secure, robust data foundation

Operating according to an escalating maturity model means we’ve done the foundational work to secure and govern our data estate for Microsoft 365 Copilot. Many of the same principles apply to agents, with the added complexity of incorporating additional data sources.

To lead these efforts, we established a cross-functional team of data professionals within our AI CoE. This team is mostly comprised of Microsoft Digital employees who support corporate functions like Corporate, External, and Legal Affairs (CELA) and Global Workplace Services. Together with our AI CoE, this team helped us define what it means to have AI-ready data.

In essence, AI-ready data just means information we’ve certified for AI workloads. We certify those data sources using Microsoft Purview to identify defects in our core data products, and we’ve also built AI-powered assessments to certify which data lakes are AI-ready.

In most ways, governance is tool-agnostic and rooted in basic principles. With robust data labeling, data hygiene, and permissions in place alongside our AI tools, which respect labels by default, we can confidently give every employee the ability to build basic agents and trust in our governance guardrails. For decades, the challenge of data analysts and engineers was maintaining a consistently reliable source of truth despite inconsistent data quality, insufficient governance, and years of collecting data in silos. Microsoft Fabric and Microsoft Purview can help resolve these issues.

We’re embracing a more balanced, federated approach to data management today. We call this approach a data mesh. Rather than allowing unchecked decentralization or forcing all our data into a single centralized system, the data mesh formalizes domain ownership while embedding governance, quality, and interoperability directly into shared platforms.

The data mesh connects and distributes, data products across domains, enabling shared data access and compute while scaling beyond centralized architectures.

Platform services are standardized blueprints that embed security, interoperability, policies, standards, and core capabilities — providing guardrails that enable speed without fragmentation.

Data management zones provide centralized governance capabilities for policy enforcement, lineage, observability, compliance, and enterprise-width trust.

With this approach, our domain teams publish data as well-defined, discoverable products, while common standards for security, metadata, and compliance are enforced through automation rather than manual processes. This model preserves enterprise trust and consistency without sacrificing speed or autonomy. By adopting a data mesh mindset, we can scale analytics and AI more effectively across the organization while still keeping ownership closely connected to the business focus.

Confidentiality labels, the practical framework for data protection

To operate according to Zero Trust principles, we needed a coherent system that lets us see, label, and protect data. Otherwise, the burden of data loss prevention would fall solely on employees, who would have to exercise individual discretion whenever they decided how to house and share potentially sensitive content.

With labeling, it’s important to strike a balance between the depth necessary for supporting an array of data governance controls and the simplicity to ensure labeling isn’t burdensome for users.

We decided on four overarching labels for container and file classification, each with its own sub-labels. The highest-level schema looks like this:

1. Highly confidential: We only share our most critical data with named recipients.
2. Confidential: Any items crucial to achieving our goals feature limited distribution.
3. General: Employees can share daily work–like personal settings and postal codes–internally throughout Microsoft.
4. Public: We share unrestricted data meant for public consumption freely. That includes information like publicly released source code and openly announced financials.

For our risk tolerance and organizational needs, we made the decision to protect data designated confidential or higher. As a result, we contain data flows to their tenants and only trust suitable storage destinations for content. That suitability depends on a storage location’s ability to gate which connectors can work with particular source data and sensitivity labels.

The administrators responsible for workspaces like SharePoint sites set default labels. These labels serve as a foundation for appropriate access and circulation for objects within those containers. It takes the burden of labeling off of employees. The sensitivity labels that administrators apply map to several different categories of policies that can anticipate and help to mitigate data loss and risk.

They communicate four key areas:

1. Breadth of availability: Labels determine whether the workspace is broadly available internally or is a private site.
2. External permissions: We administer guest allowance via the group’s classification, allowing specified partners to access teams when appropriate.
3. Sharing guidelines: We tie important governance policies to the container’s label. For example, can an employee share this workspace outside of Microsoft? Is this group limited to a specific division or team? Is it restricted to specific people? The label establishes these rules.
4. Conditional access: While we haven’t implemented this policy at Microsoft, tying identity and device verification to container labels can introduce additional governance controls.

Within Microsoft Digital, we’ve put a lot of thought into how each of our labels aligns with relevant policies. You can see more of the logic behind our sensitivity labels and their policies in this graphic:

If a container owner needs different policies for a set of files to provide greater external access, they can self-service new groups without accidentally violating our governance practices.

At Microsoft, we use Microsoft Purview, which is our suite of data estate management tools, but you can use your tool of choice to apply labels in your environment. Microsoft tools will respect them. Microsoft Purview helps us accomplish three important tasks: mapping our labeling structure onto the relevant policies, verifying them against our standards, and backstopping self-service data loss prevention practices through automation.

Automation is particularly useful. We’ve configured Microsoft Purview Information Protection to scan automatically for wayward credentials, malicious user behaviors, and other sensitive information in items without the proper protections. When Purview detects a violation, our governance team receives alerts that prompt them to contain the risk by upgrading an item’s sensitivity label or requiring employees to remedy the issue.

The result is a system that allows flexibility for employees to self-manage their digital workspaces while providing guardrails that help our governance experts take appropriate actions without overtaxing their time and resources.

Our approach within Microsoft Digital is just one way to create an AI-ready data estate, but aspects of our story will hold true for almost any organization. Consider establishing a body to take over responsibility for AI-ready data, developing your primary goals for AI-ready data, unifying your data estate, and implementing a system of confidentiality labels.

Learning from our approach to agent governance strategy

Define the responsibility for AI-ready data

Identify and assign enterprise data owners to implement and oversee the processes that guarantee data quality.

Create intuitive labels

Your employees will be the ones applying labels, so make those labels intuitive. For example, “highly confidential” is easy to understand, while “business-critical” could be interpreted in many ways from a sensitivity standpoint.

Don’t overwhelm your users

Make labeling simple and intuitive to ensure it isn’t overwhelming. Employees should have a limited set of choices to keep things comprehensible.

Use existing defaults

Identify the security needs and regulatory compliance that are specific to your organization and use built-in governance controls available through Microsoft tools.

Key takeaways

You can use these tips based on what we learned here at Microsoft to tackle agent governance at your company:

• Establish a cross-functional data council. Form a data council to help promote a culture of AI-ready data with professionals from all relevant disciplines, including human resources, legal, security, IT, and anyone else who can share relevant expertise.
• Certify datasets for AI workloads. Limit agents to datasets that have been certified as “AI-ready” to minimize hallucinations and reasoning errors.
• Define your labeling parameters. Keep the number of labels to five main labels with five sub-labels each. The fewer you use, the better.
• Align your sensitivity labels with policies. Consider how your labels line up with breadth of availability, external permissions, sharing guidelines, and conditional access.

Learn more

How we did it at Microsoft

• Follow our journey as we transform our data culture with AI-ready data. This story highlights how we’re using Microsoft Fabric and Microsoft Purview to revolutionize our approach to data governance and AI readiness here at Microsoft.
• Discover how a data council is powering our unified data strategy at Microsoft. This post tells the story of how the Microsoft Digital Data Council is leading our efforts to adopt an AI-ready data strategy.
• See how we’re conditioning our unstructured data for AI at Microsoft. This article explains our internal strategies for handling unstructured data, which can be surfaced by AI agents.

Further guidance for you

• Learn about governance and security for agents and how to keep your AI initiatives secure across your organization.
• Establish responsible AI policies for AI agents to support ethical, transparent, and accountable AI agent deployment across your organization.

Chapter 3: A matrixed approach to agent governance

Governing different types of agents for different contexts, built with different toolsets

Our customers have expressed a strong desire to start building agents, but they’re concerned about where to begin and how to manage those agents once they’re built. They worry about persistent problems such as hallucinations and agent sprawl. These concerns are especially pronounced on IT teams.

During our Customer Zero journey, we’ve learned that the diversity of agent types and creation methods means there’s no one-size-fits-all approach to governance. Generalized approaches will only get you so far.

We’ve found it helpful to think about different kinds of agents along an escalating spectrum of development complexity:

There’s an entire matrix of different parameters that apply to an agent at any level of this spectrum, and they all require different policies. Those parameters include:

• Level of reach: Personal agents, limited sharing (like development environments or team boundaries), or enterprise-wide distribution
• Agent-building tool: SharePoint agent builder, Agent Builder in Microsoft 365 Copilot, Microsoft Copilot Studio, or tools geared to more professional developers (such as Microsoft Foundry or Microsoft 365 Agent Toolkit)
• Knowledge sources and content accuracy: Public sites, SharePoint and OneDrive, directly uploaded files, enterprise apps and systems, or third-party knowledge bases

Each of these parameters creates a pivot that we need to govern, and we’ve carefully assembled a set of policies and controls to account for them. As our understanding and use of agents advances, we’re continually updating how we match their characteristics and capabilities with relevant policies and any applicable reviews.

Within Microsoft Digital, we’ve adopted a risk-based approach that helps us establish a matrixed model for agent governance. The foundational idea is that we identify potential harms for each kind of agent, then assign policies for the level of review and oversight they require.

For example, simple agents that can only read and present data tend to be low risk. Because their access is tied to their creators’ identities and access, our data governance structures and guardrails can prevent overexposure. But for agents that have capabilities like writing data, taking action, or creating items, more reviews are necessary.

A matrix of agent governance policies, pivoted by parameter

The following matrix enumerates the factors that determine how we govern different kinds of agents created using different tools. This matrix helps our employees understand the agent creation process and helps us maintain safety and control.

In addition to mapping out our policies for governing agents, the matrix illustrates how we see their relative utility across the organization. It demonstrates an escalation from personally useful to organizationally useful agents. Their governance policies and controls escalate accordingly.

Regionality is an additional concern. Regulatory compliance might vary, but it’s important to keep in mind that certain kinds of data access and actions might be perfectly permissible in one region, but not in another.

One example is our Employee Self-Service Agent, a central resource employees can turn to for help with IT support, HR questions, and facilities requests. Because it can access potentially sensitive personal information, this agent required additional review from European works councils to ensure it met all relevant workplace standards.

As you facilitate the experimentation and innovation with agents across your workforce from citizen developers to pro developers, consider adopting a similar matrixed approach to agent governance. It starts with understanding your organization’s needs, your risk tolerance, and the different employee populations you want to equip with agent-building capabilities.

Learning from our matrixed approach to agent governance

Figure out your building environment strategy

Decide which scenarios match up with specific environments and make those environments available to the relevant employees.

Design governance structures that scale from low-code to more advanced agentic tools

With the proliferation of AI agents, platform-level approvals similar to the Power Platform model at Microsoft can ensure rapid innovation while requiring review for individual high-impact scenarios.

Build trust through transparency and structure

A clear, well-documented approval process helps internal regulatory advisors understand new AI technologies and establishes the trust needed for productive, long-term collaboration.

Treat regional partners as strategic allies in the agentic future

Early feedback on digital agents from regional partners like works councils helps improve product design, accelerate approvals, and reduce fear or misconceptions about AI in the workplace.

Don’t forget that Copilot Studio is part of Power Platform

You can use what you’ve learned empowering citizen developers in Power Platform to guide your work with agents.

Key takeaways

Use these tips based on what we learned here at Microsoft to tackle agent governance at your company:

• Establish your tolerance for risk. Determine where the most prevalent risks emerge across different populations and kinds of agents. Remember, you control the guardrails in your environment.
• Determine what agent-building tools you want to roll out and who can use them. Different populations benefit from different agent-building capabilities. Put thought into what individuals and teams can create and the degree of partnership each level will need from IT.
• Define your governance parameters for different kinds of agents. Determine the best ways to hedge against risk at every level. For example, you might choose to trust in tenant governance for simple agents and establish reviews for more complex tools.

Learn more

How we did it at Microsoft

• See how we’re empowering employees with the Power Platform. This article explains how our Power Platform governance model influenced our matrixed approach to agent governance.
• Learn how our employees are building their own AI tools. This article describes how knowledge workers at Microsoft are forging their own agents to help improve efficiency and get more done.
• Check out our lessons learned using SharePoint agents at Microsoft. This post shows how we approached governance for low-risk, knowledge-only agents through our deployment of SharePoint agents.
• Read how we’re unlocking enterprise AI extensibility with Copilot Studio. This story explores how we govern more advanced agents built with Copilot Studio, including connectors, orchestration, and scaled governance.

Further guidance for you

• Explore governance and security for AI agents across the organization with Microsoft Learn content.
• Check out this industry-standard framework that validates matrixed, risk-based governance approaches for AI systems.

Chapter 4: Tracking, impact, and value

Managing agents and assessing their business impact for the organization

It’s clear that agents bring astonishing capabilities to the enterprise. For many organizations, what remains unclear is exactly how to measure their impact. Without that information, businesses are at a loss for ways to articulate value and drive improvement.

Tracking agents is also a crucial component of preventing sprawl: We need to understand what agents we have, how employees are using them, what critical processes they’re supporting, and if they’re contributing value or need to be retired.

We’re at the beginning of our impact-tracking journey, but our work can provide a starting point for your own efforts to measure the value of AI initiatives at your organization.

Managing our agent catalog through comprehensive tracking

Microsoft Digital partners with other internal organizations to ensure we’re prioritizing the right agents and avoiding agent sprawl. Ideally, these engagements take place before teams start building their agents so we can avoid wasted effort or duplicated work.

Still, ongoing management efforts are crucial to keeping our agent ecosystem healthy. Telemetry is the key to assessing usage and ensuring compliance. We’ve developed our own internal tooling to ensure that:

• Metadata is complete and available
• The tooling tells us the right information about our agents
• The tools connect properly with other compliance tooling, like Microsoft Purview

This telemetry also reveals agent behaviors, shows how agents do their work, and tracks events, actions, and policy baselines.

These capabilities help us gain visibility into policy adherence and violations, and then to conduct enforcement actions. We also track the speed of reaction and mitigation. AI-ready data and robust guardrails mean we head off most violations before they occur.

A robust inventory, an agile policy framework, and an automated workflow for enforcement are cornerstones for successfully governing agents at scale.

The release of Microsoft Agent 365, now in early access, represents the next step in agent observability and management, two key aspects of agent governance and sprawl mitigation. This control pane for agents incorporates many of our learnings as we’ve bridged governance gaps through IT intervention.

Some of the key aspects of the control pane:

The registry

Provides a complete view of agents, and the enterprise agent store makes it easy to find the right agents for each role and business process within familiar workflows in Microsoft 365 Copilot and Teams.

Visualization

Delivers the observability layer, including role-specific oversight, compliance and audit features, and performance measurements that can help organizations track their agents’ impact and see where they contribute value.

Interoperability

Ensures Agent 365 is open to any Microsoft-built or partner ecosystem, while delivering work intelligence through access to data and Microsoft 365 apps.

Security features

Provide crucial confidence through visibility into security posture, detection and response capabilities, and intelligent runtime defense.

As Customer Zero for Agent 365, we’re excited to have a platform for observability and telemetry that encompasses everything from agentic creation through usage.

Tracking governance from agent inception

Professionally developed agents add a new dimension of tracking and governance, because we need standards in place for ensuring compliant agent-building and to remediate any issues.

We use our Azure DevOps instance to catalog apps on our tenant, and we’ve applied this practice to agents created professionally for lines of business and enterprise agents. This tool contains our service tree with product and app log registration, which is tied to our KPI dashboard and scoring system that validates agent data against our policies.

Our expectation is that all new apps and agents start from a place of compliance. Any new agent is registered through this platform, and we expect adherence within the first 14 days. In our experience, the introduction of new metrics, policies, or timeframes as our governance policies evolve is where agents tend to drop out of compliance. The priority is restoring compliant status.

We’ve established a series of metrics to help track and manage these expectations:

• Enablement velocity
• Renewal velocity
• Agents in compliance
• Time to remediation of noncompliance

Through a DevOps process built on our preexisting software development lifecycle practices, we’ve applied governance not only to agents themselves, but to the process of building them professionally.

Measuring progress and unlocking value

Properly measuring value depends on concrete definitions of success and metrics that support it. Articulating AI’s impact came with several challenges. First, we had to land on a consistent taxonomy for different measurement areas. Then we needed to make the relevant data accessible, ensure its quality, and confirm it made sense.

The Microsoft Digital AI Value Framework is our flexible, modular tool for measuring the impact of our AI initiatives. With tools for measurement firmly in place, we can effectively demonstrate value and guide further decision-making.

We plan to use the following capabilities to improve the overall ecosystem:

• Filtering our agent inventory on specific criteria like the type of agent or how it was built
• Enhancing governance-specific actions we can take with agents in areas like ownership and quarantining
• Gaining visibility into trends like agent usage
• Ingesting agent blueprints and defining policy templates

We’re still in the midst of our agentic measurement journey at Microsoft, but the blueprint for tracking already exists. Your organization might be in the early stages of agent readiness and deployment. If that’s the case, it could be helpful for you to internalize the lessons we’ve learned as Customer Zero and apply them as early as possible in your own journey toward AI maturity.

Learning from our agent adoption experience

Think proactively, not retroactively

If you put effort into tracking agentic impact early in your AI maturity journey, you’ll be poised to start capturing insights immediately instead of applying your methodology retroactively.

Involve a wide array of stakeholders

This workstream needs oversight from different kinds of stakeholders, including your leadership team, IT, Microsoft 365 administrators, agent developers and builders, and employee champions. That will provide the sponsorship, expertise, and perspective you need for success.

Different measurements will be appropriate for different phases of your initiatives

These measurements include monthly, weekly, or daily active usage; consider which metrics make sense at each phase of an AI initiative.

Establish a continuum of value

Agents need to tie into real business goals, so it’s important to establish metrics that actually speak to those objectives. Cascade business goals to concrete KPIs with well-defined timelines and track those diligently.

Embrace the red

Try to think of underperformance not as failure, but as data. Performance data over time helps you course correct or pivot, making sure you invest where it matters.

Key takeaways

Here are some important steps to keep in mind as you embark on your own tracking and measurement efforts for agents:

• Establish priorities and parameters for tracking agents. Consider measurements that relate to sprawl, usage, and coverage, and build them into your telemetry tooling.
• Pull your stakeholders together to establish measurement parameters. Cascade business priorities into measurable value.
• Conduct ongoing tracking. Establish a cadence for tracking and reviewing progress with your team.

Learn more

How we did it at Microsoft

• Learn how we’re measuring the impact of Microsoft 365 Copilot and AI at Microsoft. Discover our framework for tracking the impact of the investments in AI that we’re making internally.
• Find out how we’re deploying Microsoft Agent 365 internally. This story shares our intentions for using our unified control pane for agents.
• See how we’re building an AI-powered continuous improvement culture at Microsoft. This article outlines our approach to continuous improvement.
• Check out how we’re reshaping Microsoft with continuous improvement and AI. This post discusses the importance of continuous improvement for accelerating AI at Microsoft, including three initiatives already underway.

Further guidance for you

• This Microsoft Learn guidance helps organizations align AI investments with strategic goals and measurable outcomes over time.
• Explore a cloud adoption framework that provides practical guidance for defining success metrics, tracking ROI, and tying AI initiatives to business outcomes.

Governing the frontier to scale innovation

AI agents are rapidly becoming core contributors to how work gets done. As our experience within Microsoft Digital demonstrates, realizing their full potential demands more than powerful tools or enthusiastic builders. It requires thoughtful governance that evolves alongside your AI maturity, protects what matters, and gives employees the confidence to innovate responsibly.

As you consider your own strategy for managing agents, it can be helpful to keep one truth in mind: Governance is a catalyst for progress, not a barrier. By embedding guardrails into tools, grounding agent creation in AI‑ready data, applying risk‑based and matrixed policies, and reinforcing all of it through adoption and education, we’ve been able to expand agentic capability without sacrificing security, privacy, or trust.

From our experience, we’ve learned that governance works best when it’s:

• Proportional, scaling with risk and agent complexity
• Embedded, not bolted on after the fact
• Human‑led, recognizing that accountability and judgment remain essential
• Iterative, adapting as technology, regulations, and business needs evolve

When you design governance this way, it allows experimentation, learning, and impact at scale. Employees feel empowered to build agents that solve real problems, while IT and compliance teams gain visibility and control without becoming bottlenecks. Crucially, leaders can measure value, manage risk, and make informed decisions about where to invest next.

“At Microsoft, we believe the future of agentic AI depends on governance that empowers people first. The structures should be invisible when they’re working, intentional when they’re needed, and trusted by everyone they serve.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

This is the foundation of the Frontier Firm: Organizations where humans lead and agents operate, guided by clear principles and trusted systems.

As you continue your AI maturity journey, remember that there is no single, correct governance model. Your approach will reflect your risk tolerance, regulatory environment, data maturity, and organizational culture. The practices outlined here provide a proven starting point informed by real-world deployment at enterprise scale.

“At Microsoft, we believe the future of agentic AI depends on governance that empowers people first,” says Vijaya Alaparthi, principal group product manager in Microsoft Digital. “The structures should be invisible when they’re working, intentional when they’re needed, and trusted by everyone they serve.”

Now is the moment to act. Start with strong foundations. Empower your builders. Measure what matters. And treat governance not as a constraint, but as a strategic advantage that allows your organization to move faster, innovate safely, and lead confidently on the agentic frontier.

Key takeaways

Here are the high-level learnings and insights that you need to consider as you embark on your own agent governance journey, based on what we’ve learned here at Microsoft:

• Treat governance as an enabler of innovation, not a brake. Effective agent governance is what makes large‑scale innovation possible. When you embed guardrails into platforms, data, and processes, employees can build and experiment confidently without exposing the organization to unnecessary risk or slowing progress.
• Match governance rigor to agent risk and maturity. Not all agents need the same level of oversight. A risk‑based, matrixed approach lets organizations trust lightweight, personal agents while applying deeper reviews to agents that write data, take actions, or operate across business‑critical systems.
• Start with AI‑ready data and zero‑trust foundations. Strong agent governance rests on secure, well‑labeled, high‑quality data. Clear ownership, intuitive sensitivity labels, default protections, and automation reduce reliance on user judgment and allow agents to operate safely at scale.
• Embed governance where agents are built and used. The most effective governance is built into tools and workflows, not enforced through manual reviews alone. Defaults, limits, identity‑based access, lifecycle controls, and telemetry should apply automatically so agents are governed by design.
• Plan for the full agent lifecycle to prevent sprawl. Agent inventories, ownership models, attestation, and retirement processes are essential. Governance needs to account for how you create, share, evolve, audit, and ultimately decommission agents, whether individuals or enterprise teams are responsible for building them.
• Reinforce governance through adoption and education. Guardrails work best when employees understand them. Targeted adoption programs, clear guidance, prerequisites for advanced tools, and visible leadership sponsorship can help employees build responsibly and recognize their role in protecting the organization.
• Measure what matters to prove value and drive improvement. Visibility drives trust. Telemetry, observability, and clear metrics that span productivity, quality, risk reduction, and experience allow organizations to track impact, course‑correct early, and continuously improve their agent ecosystem.

Learn more

• Discover our IT playbook for becoming an AI-driven Frontier Firm.
• Learn how to secure your agents at scale with Microsoft Agent 365 guidance.
• Read a guide to governance and security for AI agents at your organization.
• Check out this detailed guide to our governance process for the internal rollout of Microsoft 365 Copilot.  
• Explore ways to establish responsible AI policies for agents across the organization.  
• Find out how a data council is powering our unified data strategy at Microsoft.

Try it out

Get started building and managing agents at your company with Microsoft Agent 365.

The post Governing AI agents at scale: Lessons from our journey at Microsoft appeared first on Inside Track Blog.

But in practice, the technical side of transformation is only part of the story. The missing piece is us humans.

When we encounter these kinds of challenges internally at Microsoft, we think of ourselves as “Customer Zero.” We roll out our technology across our own organization first, learning what works and what doesn’t in real time and at scale so we can pass our lessons on to you.

“After an early wave of enthusiasm for Copilot, adoption declined. People questioned whether AI was relevant to their role, worried about what it might mean for their work, and disengaged when the change they experienced didn’t match the change they imagined.”

Daniel Bertrand, senior director, AI Transformation Office

We learned valuable lessons about AI adoption and sustainable change when we deployed Microsoft 365 Copilot across our Microsoft Commercial organization, one of the company’s largest sales and service organizations. What we observed led us to reset our strategy and build a more human-centered process for deploying and driving adoption of our AI technology.

Driving AI adoption with role relevance and daily habits

Here on the Customer Zero team in Microsoft Customer and Partner Solutions (MCAPS), our 60,000-employee strong sales organization, we saw that getting access to Copilot didn’t automatically result in widespread AI adoption.

“After an early wave of enthusiasm for Copilot, adoption declined,” says Daniel Bertrand, a senior director on the AI Transformation Office team in MCAPS. “People questioned whether AI was relevant to their role, worried about what it might mean for their work, and disengaged when the change they experienced didn’t match the change they imagined.”

Initially, people used Copilot like a search engine and expected it to make work go away. When that didn’t happen automatically, they didn’t know how to approach prompting the AI, or how to create value with it. The gap between access and know‑how is where adoption slowed.

“I knew from experience that people prefer to hear from—and learn alongside—those closest to their day-to-day work, to build trust and confidence.”

Susan Neece Robien, senior director of adoption and change, AI Transformation Office

We reframed the problem from “How do we scale the technology?” to, “What does this change feel like for people doing the work every day?”

By talking to people in our larger organization about why they were reluctant to work with Copilot, we discovered the adoption barrier was less about the technology being available and more about whether people trusted it, understood how it fit their role, and felt confident enough to build new habits around it.

The ‘Adoption-in-a-Box’ approach

After these conversations, we changed our strategy across the board.

“I knew from experience that people prefer to hear from—and learn alongside—those closest to their day‑to‑day work, to build trust and confidence,” says Susan Neece Robien, a senior director of adoption and change on the AI Transformation Office team. “That led me to conceptualize Adoption‑in‑a‑Box—a repeatable approach that combines behavior‑change guidance, peer influence, habit‑forming activities, and light gamification so people can experiment with AI in a non‑threatening way and build confidence over time.”

We rolled out the Adoption-in-a-Box concept across the team in the following ways:

• Emphasized visible leadership support: We circulated videos and “day in the life” PowerPoint 1-pagers of how our leaders were using Copilot.
• Formed a community of early adopters: They becamepeer champions for adoption, evangelizing best practices and leading workshops.
• Created a Role Hub: The hub contained practical, role-specific learning about how to use Copilot rather than doing high-level general trainings.
• Ran prompt campaigns: To get our team started with habitually using AI in their daily roles, we ran prompt campaigns to make prompt learning accessible and actionable.
• Created the Copilot Cup: We encouraged friendly competitions with leadership support. We also ran hackathons and prompt-based scavenger hunts to gamify learning about and using the AI for our team.
• Created ongoing measurement mechanisms: We stood up dashboards with monthly, weekly, and daily average usage reports. We also ran quarterly surveys to track sentiment around AI adoption on the team.

After our initial success with Adoption-in-a-Box, we scaled it to adoption leads, who brought the model to life within their teams.

When people feel safe in experimenting with AI and incorporating it into their day-to-day work, that’s when it provides real value for the organization and the individual. We’ve learned that sustainable, scalable AI transformation succeeds when we put people first.

Key takeaways

If you’re wondering how to encourage your own team to adopt new AI technology into their workflows, you can learn from our experience:

• Prioritize visible leadership participation. Leaders set the tone of any transformation, and AI adoption is no exception.
• Roll out for role relevance. Specificity is the key here: How does AI relate to each person’s individual role? If the tool provides value and saves time, people will incorporate it into their workflow.
• Establishing habits is crucial. Sustainable adoption means people use the tool on a daily basis in the natural flow of their work. Give them low-friction opportunities to learn the ropes.
• Encourage peer-to-peer experimentation. Early adopters can be a valuable resource for showing others the way. Lowering the stakes by having a peer guide employees in a workshop or one-on-one can take the pressure off as they experiment with the tech.

Try it out

Test drive an AI-assisted workflow of your own with Microsoft 365 Copilot to see how agentic AI can transform your team.

Related links

• Discover the five lessons we learned about how to make AI stick for sellers during our Copilot rollout.
• Check out our full guide describing how we deployed and drove adoption of Microsoft 365 Copilot across our global organization.
• Read about our internal change management strategy for Copilot and how it helps us drive the future of work.
• Learn how the creation of a Copilot Expo propelled AI usage at Microsoft.
• See our seven tips for driving Copilot adoption with a virtual skilling event.
• Discover how one Microsoft Digital employee powers her typical workday using Copilot.

The post Staying human: How we’re using AI to transform the sales experience at Microsoft appeared first on Inside Track Blog.

In 2026, we’re celebrating 25 years of SharePoint at Microsoft. Microsoft Digital, the company’s IT organization, is commemorating this anniversary by reflecting on the journey we’ve taken with the product over the last quarter-century.

In this article, we’ll share our journey as SharePoint’s Customer Zero and step through the lessons we’ve learned building and maintaining an IT stack in the age of agentic AI.

Why SharePoint?

In the early 2000s, we faced a technical challenge familiar to just about any organization: We had important documents and data scattered across siloed file shares, institutional knowledge hidden away in email attachments, and access challenges preventing different teams from collaborating across geographical borders and departmental boundaries.

SharePoint offered the solution to these challenges.

Its flexible, web-based platform gave us the ability to collaborate using shared sites, centralized document libraries, and widely accessible workspaces. The application also fundamentally reshaped our corporate communications and publishing capabilities, providing features that would power key internal portals like Microsoft Web (our longtime internal company homepage, often called MSW), HRWeb, and MS Library.

“At the time, because there were so few customers running SharePoint at scale, the product was in many ways directly built to meet our IT needs.”

Sam Crewdson, principal program manager, Microsoft Digital

The evolution of how we used SharePoint in Microsoft Digital can best be described in three phases:

1. Our on-premises expansion and optimization
2. Our migration to the cloud, self-service growth, and modernization
3. Our incorporation of agentic AI

On-premises expansion and growing pains

When we first adopted on-premises SharePoint at scale, it became indispensable almost immediately. Internal teams used SharePoint to replace their existing file shares, publish information internally, and create many custom workflows and applications tailored to their needs.

Our team at Microsoft Digital was responsible for deploying SharePoint on an enterprise scale. Because we were one of the first enterprise customers to fully use SharePoint’s capabilities, we worked closely with the SharePoint product team from the beginning of its existence as a company. This meant we played a sizable role in influencing what SharePoint ultimately became.

“At the time, because there were so few customers running SharePoint at scale, the product was in many ways directly built to meet our IT needs,” says Sam Crewdson, a principal program manager in Microsoft Digital. “A result of our being their first and best customer at the time was that the SharePoint team often built capabilities for us that no one else was asking for yet, such as specific portals features and supportability needs.”

Our initial adoption of SharePoint exposed some structural limitations and gaps. To meet the goals of our internal customers, we often relied on custom code, which made upgrades more difficult. And data governance and lifecycle management could be challenging, with our internal teams creating thousands of sites with little or no ownership tracking.

Using SharePoint in this way meant rapidly accumulating abandoned sites and outdated content. Trying to conduct even routine maintenance became difficult because there was no reliable way to contact site owners.

“Because of the initial difficulties, SharePoint was frustrating at first, especially for admins. But then I realized how important it was for our users—the product saved them so much time, and they were so happy that it was available. It was a complete 180-degree shift in my mindset towards SharePoint.”

Thomas Snyder, principal service engineer, Microsoft Digital

These challenges meant tensions often ran high for the IT team during the initial adoption phase. Tempers sometimes flared as we navigated this period in SharePoint’s evolution at Microsoft.

However, the time and effort we put into overcoming these growing pains—time and effort our customers didn’t have to invest themselves—made the frustrations well worth it.

“Because of the initial difficulties, SharePoint was frustrating at first, especially for admins,” says Thomas Snyder, a principal service engineer in Microsoft Digital. “But then I realized how important it was for our users—the product saved them so much time, and they were so happy that it was available. It was a complete 180-degree shift in my mindset towards SharePoint.”

Scalable self-service, effective governance, and the cloud

SharePoint’s role at Microsoft quickly expanded from a collaboration platform into a more powerful application where our teams could build workflows, forms, dashboards, and other solutions.

Thanks to a decision to enable SharePoint’s self-service site creation capabilities, our internal customers were able to use it to build the sites they needed without having to wait for us in IT. By removing the friction of having to work with IT, they innovated faster and built new capabilities on their own using SharePoint’s out-of-the-box technology.

However, this self-service power we gave to our users also drove some sprawl that we were not initially ready to manage. By the late 2000s, the information explosion that SharePoint sparked at the company was increasing our operational and governance burden. The rapid growth in sites delayed upgrades and introduced security and compliance issues stemming from a lack of clear ownership when site owners changed jobs or left the company.

As a result of this growth, we made the decision to invest heavily in building up our governance and lifecycle management for SharePoint. We prioritized defining clear ownership for all SharePoint sites, establishing best practices around data cleanup, and building the guardrails necessary to make widespread adoption and use more manageable.

Moving SharePoint to the cloud

Our cloud migration started in late 2010 and quickly became the driving force for us in IT. Rather than see the migration as a simple lift-and-shift activity, we took the opportunity to strategically reconfigure the architecture and customization level of our SharePoint instance.

This was a huge undertaking.

We had to think globally across all our sites in different regions and countries. The tooling suite for migration was immature at the time, meaning some of our portals and sites would require refactoring. We also had to contend with the constraints of varied and sometimes conflicting regional data residency requirements.

“It’s effectively filtering, so you don’t migrate everything. You’re cleaning your house before you move. You don’t move everything in your garage—you clean it out first. The easiest move is the one you don’t have to do.”

David Johnson, principal product manager architect, Microsoft Digital

Our approach to moving SharePoint to the cloud took several phases

First, early adopters who expressed active interest in migrating were provisioned the first sites in the cloud. By harnessing their enthusiasm for cloud services, we allowed them to self-migrate their own site content

Second, we did extensive analysis of all sites to establish actively used sites. Sites where we had no recent usage were backed up, stored offline, and deleted. If nobody screamed, we didn’t move them to the cloud.

Third, we moved the zero- and low-customization sites. These were sites using out-of-box features that had the highest likelihood of a successful migration

Finally, all we had left were the highly customized sites, which often used customization approaches which were not supported in the cloud. These we chose to manually rebuild and often to refactor as part of our migration approach.

While we were making these first-in-the-world migrations, we spent a lot of time with our SharePoint product team partners to learn how best to move sites and to document the approaches for the millions of sites that would follow. Sites which had high levels of customization or features that the cloud couldn’t support were instead rebuilt in the cloud environment from the ground up.

We treated our SharePoint cloud migration as an opportunity to take stock of what we had and decide what we didn’t want to bring with us into the new age of SharePoint at Microsoft. We cleaned our data and retired unused sites based on which content and functions employees told us they regularly used and relied on.

“It’s effectively filtering, so you don’t migrate everything. You’re cleaning your house before you move,” says David Johnson, a principal product manager architect in Microsoft Digital. “You don’t move everything in your garage—you clean it out first. The easiest move is the one you don’t have to do.”

Cloud migration also presented fresh governance challenges for our team. Governance practices had to be established for this new environment that would allow for effective self-service across multiple sites.

Building governance around lifecycle management, attestation, ownership policies, and guarding against oversharing required a significant amount of effort from the team, but it was necessary to ensure a smooth transition from an on-premises tool to the cloud.

Site modernization: Reducing the need for customization

Around 2016, SharePoint rolled out what came to be known as SharePoint Modern. This new version was a game changer for our major portals, as it reduced the need for heavy, developer-driven customization and replaced it with powerful out-of-the-box page creation capabilities, responsive design, and improved accessibility. The product also eventually added seamless built-in integration with solutions like Microsoft Teams and OneDrive.

Less custom code meant we could upgrade faster and dramatically lower our development, support, and maintenance costs. But the best part was the improved user experience and better navigability of the new version. Before this, our IT team fielded numerous questions about SharePoint on a weekly basis. The more intuitive, user-friendly experience of modern SharePoint reduced the volume of inquiries and service requests drastically. Our internal users were happier, and so were we.

SharePoint in the age of agentic AI

We see SharePoint as a key “knowledge platform” for AI. It’s a critical enterprise-scale repository for our documents and data and other information that we use to power our global enterprise.

“Security through obscurity is dead. It’s the double-edged sword of semantic search.”

Thomas Snyder, principal service engineer, Microsoft Digital

As such, it’s one of our key “knowledge platforms,” locations where we store the information that is the lifeblood of our enterprise. And as our enterprise-scale repository for documents, data, and other information used to run our global multinational, it has become the launching point for many of our AI-powered experiences.

AI is only as effective as the quality of the data it can access, which is why we’ve prioritized governance best practices as we make this transition. With these new tools, we’ve had to overcome new challenges.  For example, in the early days of AI, the discovery of previously well-buried personal data is becoming a common occurrence.

“Security through obscurity is dead,” Snyder says. “It’s the double-edged sword of semantic search.”

Prioritizing good governance helps ensure agentic AI only has access to the data it’s permitted to use, avoiding accidental oversharing and related hallucinations.

As an AI-driven Frontier Firm, we’re empowering our non-technical users and engineering and development teams alike to begin building custom AI agents to drive innovation at Microsoft. Our teams can now use agents in SharePoint for tasks like creating applications, knowledge depositories, and sites, saving huge amounts of time and effort.

Many of these agents will eventually be available in Azure DevOps and GitHub, so we’re focused on helping SharePoint site owners put the appropriate data ownership and permissions in place to effectively manage and govern the data for use by agentic AI.

After 25 years, SharePoint remains a core part of IT operations across Microsoft. We look forward to growing alongside it as it continues to evolve and improve.

Key takeaways

These insights can help you mature and transform how you use SharePoint at your company:

• Self-service and good governance go together. Without solid guardrails for your SharePoint instance, your organization could contend with information sprawl and internal friction between departments.
• Cloud migration is a golden opportunity. Before you migrate from on-premises IT to the cloud, take the time to clean your data to avoid carrying technical debt and outdated information into the future.
• Out-of-the-box capabilities are your friend. Customization is useful, but too much of it can be unwieldy and expensive to maintain.
• Make data hygiene a priority. Poorly governed data can undermine users’ trust in AI, expose sensitive information, and delay widespread adoption.

Try it out

Explore how Microsoft SharePoint can help your team collaborate more effectively and be ready for the agentic AI future.

Related links

• Read how we keep our content fresh, findable, and governed with AI-powered SharePoint at Microsoft.
• Learn about how we’re handling Microsoft 365 Copilot governance issues internally in this guide.
• Discover ways to use SharePoint agents to surface knowledge and improve how employees find answers.
• Explore how we’re supercharging SharePoint sites with Microsoft 365 Copilot.

The post 25 Years of SharePoint at Microsoft: Our lessons learned as Customer Zero appeared first on Inside Track Blog.

But curiosity alone doesn’t change how work gets done.

Very quickly, we saw the difference between interest and impact. Turning early excitement into meaningful, sustained behavior change required more than access to new technology—it required trust, relevance, and new habits embedded into daily work.

As our employees moved beyond experimentation, a consistent set of questions emerged:

• Is this relevant to my role?
• Can I trust the output?
• How does this fit into the way I already work?

That shift reframed how we approached adoption. Instead of treating Copilot as a deployment milestone, we began treating it as a change experience, one grounded as much in people and behavior as in technology.

Five lessons from our journey stood out.

1. Leadership makes change visible

Adoption accelerated when leaders didn’t just endorse Copilot—they used it.

Early on, we saw hesitation in teams where leadership signals were unclear. Employees were cautious about changing how they worked without explicit, visible support.

What made the difference was modeling.

When our leaders shared how they were using Copilot in their own workflows—and what they were learning along the way—it reduced uncertainty and made the change tangible.

“In the era of AI, ‘do as I say, not as I do’ won’t cut it. Leaders need to be visible and accountable for modeling the way forward in their organizations.”

Pam Maynard, chief AI transformation officer, Microsoft Customer and Partner Solutions

2. Peer networks scale trust faster than top-down messaging

Enterprise-wide communications created awareness but didn’t create confidence.

Employees needed to see how Copilot applied to the reality of their own work—ideally from someone who understood it firsthand.

That’s where our champion network became essential. Early adopters ran workshops, shared practical examples, and offered real-time support grounded in everyday scenarios. Their proximity to the work made their guidance credible. Adoption became more social, and trust built faster.

3. Relevance matters more than generic training

We quickly learned that generic training wasn’t enough.

While easy to scale, broad guidance often failed to connect with employees who couldn’t immediately see how AI applied to their responsibilities.

What worked instead was role-based immersion:

• Prompts grounded in real workflows
• Examples aligned to specific responsibilities
• Scenarios that reflected day-to-day tasks

Whether drafting customer account plans, summarizing meetings, or synthesizing research, the most effective experiences mirrored the work employees already owned.

As relevance increased, so did confidence. Copilot shifted from an abstract capability to a practical tool.

4. Habits—not enthusiasm—drive lasting change

Initial experimentation was widespread. Sustained usage was not.

Like any new tool, Copilot didn’t become part of daily work by default. The real challenge was helping employees return to it often enough to form new habits.

What moved the needle were small, repeatable actions:

• Simple prompts embedded into existing workflows
• Shared examples that lowered the barrier to entry
• Low-friction ways to experiment without risk

Over time, these patterns changed behavior. Copilot became less of a novelty and more of a natural extension of how work gets done.

Some examples of practical prompts that helped to change habits include:

• “Summarize recent news, earnings highlights, and strategic priorities for (company name) and suggest three conversation starters relevant to their digital transformation goals.”
• “Based on my meeting notes, draft a follow-up email summarizing what we discussed, the next steps we agreed on, and any open questions—keep the tone warm and professional.”
• “Review my sent emails and meeting notes from the past week and list any customer commitments or action items I may still need to follow up on.”

5. Measurement only works when paired with listening

Usage data provided valuable signals—but it didn’t tell the whole story.

To understand what was really happening, we paired quantitative data with qualitative feedback such as:

• Employee surveys
• Live discussions
• Direct, in-the-moment input

This combination gave us a clearer picture of what was resonating, where friction remained, and how to adjust. Measurement shifted from just reporting outcomes to also enabling continuous learning.

Adoption without employee feedback can easily turn into guesswork. Leaders don’t have time for that when the stakes of frontier transformation are so dramatic. Organizations that win in the era of AI are ones that can measure and see the impact on their day-to-day operations.

The bottom line

Scaling AI isn’t just about access—it’s about absorption.

Our experience reinforced a simple truth: Value is created when people integrate AI into the way they already work. That requires more than tools. It requires trust, relevance, habits, and continuous feedback.

“Even with intuitive technology like Microsoft 365 Copilot, you can’t underestimate the criticality of getting human-centered change right,” says Pam Maynard, chief AI transformation officer for Microsoft Customer and Partner Solutions. “Our experience makes it clear that modeling the right behaviors, engaging with champions, helping employees to build the habit, focusing on role-immersive training, and measuring what matters while listening to our employee signals are the keys to driving successful AI-transformation at scale.”

When these elements come together, adoption becomes durable, and based on our experience at Microsoft, transformation becomes sustainable.

Key takeaways

How can you replicate our success in your own organization? Focus on these key lessons:

• Leadership visibility is critical. Leaders need to model expectations to set the right tone from the top.
• Peer networks scale credibility faster than top-down messaging. Peer influence can scale further and faster than policy alone because examples are closer to real work.
• Role based immersion beats generic training. Generic training doesn’t always connect. Role specific prompts and resources tied to real seller workflows made the value concrete and raised confidence.
• Habit formation is the real adoption engine. Repeatable micro actions like practical prompts, shared examples, and low friction experiments are what move the needle, turning AI from a novelty to a productivity partner.
• Measurement without listening creates blind spots and risk. Usage data is just part of the story; pairing telemetry with employee signals prevents “guesswork” and turns measurement into learning, which is important for catching where people get stuck.

Try it out

Ready to embrace Microsoft 365 Copilot in your organization? Discover all the ways that Copilot can accelerate your AI-powered journey, especially with deliberate change management to drive adoption.  

Related links

• Read our guide to how Microsoft Digital deploys, governs, supports, and drives usage of Microsoft 365 Copilot at global scale.
• Discover how a Copilot Expo supercharged AI usage at Microsoft by promoting role-based immersion.
• Learn about our internal change management strategy for Microsoft 365 Copilot and how it helps us drive the future of work.
• Meet Opeoluwa Burnett and see how she uses Copilot to power a typical workday.
• Find out how you can pick the right Copilot for the job, based on our own experience at Microsoft.
• Check out our seven tips for driving Copilot adoption with a virtual skilling event.

The post Making AI stick for sellers: Five adoption lessons from our Copilot rollout appeared first on Inside Track Blog.

Microsoft Digital stories

The rate of change in IT is accelerating at a blistering pace. AI-powered capabilities like Microsoft 365 Copilot have enabled a new era of employee productivity. Today, agentic capabilities are supercharging IT like never before. As IT leaders, we are living in extraordinary times.

But change can be destabilizing, even during normal times for the most confident and sure-footed IT teams. That’s why our commitment at Microsoft Digital—the company’s IT organization—to act as Customer Zero for our company and our customers has never been more important.

“We need to shepherd the company through this era of AI disruption. We’re working to transform all of Microsoft into an AI-first workplace, sharing our insights with customers so they can follow our lead.”

Matt Hempey, partner product manager, Microsoft Digital

If we can navigate this era of generational change, powered by AI with confidence, and provide a clear blueprint for our customers to follow, we will truly live up to Microsoft’s mission to “empower every person and every organization on the planet to achieve more.”

“We need to shepherd the company through this era of AI disruption,” says Matt Hempey, partner product manager in Microsoft Digital. “We’re working to transform all of Microsoft into an AI-first workplace, sharing our insights with customers so they can follow our lead.”

Like the advent of the personal computer in the 1970s and 1980s and the rise of the internet in the 1990s, AI is having a transformative effect on information technology—and the risks and opportunities are potentially even greater than those earlier breakthroughs. In Microsoft Digital, our role as Customer Zero has become more critical than ever. We serve as Microsoft’s first and best customer for each of our groundbreaking technologies, ensuring that they’re enterprise and world-ready.

An evolving approach

Being Customer Zero means a deep partnership between our IT organization and our company’s product engineering teams to envision the right experiences, co-develop innovative products, and both listen to and act on the insights we gather from our employees and customers. We work closely together to stay grounded in the way our employees use our products every day, so your employees can benefit from our experiences and takeaways.

“In Microsoft Digital, we’re well aware that the only constant is change. But the last 12 months have shown that our old models are no match for the wave of change we’re seeing at Microsoft. We have to adapt our approach.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

But just like changes in the tech industry caused Microsoft to embrace an agile approach to software development, the advent of AI compels us to reimagine our role as Customer Zero. Since the first version of this article was published three years ago, our approach in Microsoft Digital has changed. Today, our focus is on helping our employees to harness the transformative power of AI to reimagine the world of work.

Customer Zero evolution

“In Microsoft Digital, we’re well aware that the only constant is change,” says Vijaya Alaparthi, principal group product manager in Microsoft Digital. “But the last 12 months have shown that our old models are no match for the wave of change we’re seeing at Microsoft. We have to adapt our approach.”

Our philosophy has shifted from envisioning new experiences to a wholesale reimagining of the workplace. We’re moving beyond deploying, governing, and building solutions to a world where we empower our employees to define and deploy agentic capabilities that dramatically increase their productivity.

While we still deploy, govern, and operate our productivity tenants in secure and compliant ways, we also focus on defining guardrails that enable our employees to maximize their productivity and creativity. And we continue to be the voice of the organization’s own digital transformation, with an intentional focus on how AI is helping us transform our enterprise from the inside out.

Envisioning the future

When we began our Customer Zero journey, our charter was to envision transformative employee experiences. With advances in AI and the advent of Microsoft 365 Copilot, our ambition has moved beyond experiences to consider entire workflows, as well as the agentic workplace of the future. Here are some of our plans:

• We engage with our employees and customers around the world to better understand their needs, then work with product engineering to co-develop AI-powered solutions.
• We anticipate and address product requirements that our large enterprise customers will have as they pursue agentic workflows, based on our experience reimagining business, operational, and technological processes at Microsoft.
• We leverage our insights gained from managing a vast array of IT services at Microsoft—including network, infrastructure, devices, and end-user services—to design and build new AI-powered capabilities, then ensure they meet the needs of our employees.
• We evaluate and improve our processes using continuous improvement techniques to ensure they’re ready for an agentic future.

{Learn more about Microsoft Digital’s IT journey and how it’s enabled AI transformation at Microsoft.}

Empowering our employees

While Microsoft Digital still builds, deploys, and drives adoption of new employee capabilities, our posture has shifted from employee enablement to employee empowerment. Some examples of how we’re empowering our employees include:

• Within our productivity tenants, we create whatever digital assets and containers they need to be productive. That could be a new SharePoint site, Teams group, Power BI workspace, agent, or even an Azure subscription. The key is that the new tool or asset is in our tenant, which means its lifecycle can be securely managed, enabling productivity while also helping to cut down on shadow IT.
• Our employees can create agents using Agent Builder in SharePoint or Copilot Studio. These AI-powered agents are helping our teams achieve new levels of productivity, and future capabilities are only going to accelerate that trend.
• We’ve nurtured and empowered a Copilot champions community at Microsoft that stands at nearly 10,000 employees. These enthusiastic champions are helping to shape the future of AI at the company, as they build new AI-powered experiences while sharing their knowledge and excitement with their peers. Their expertise and passion augment our ability to drive change in the enterprise, with role-based champions helping to supercharge AI-powered transformation across Microsoft.

{Learn how we’re driving adoption of Microsoft 365 Copilot with our Champs community.}

Defining the guardrails

In an era of agentic transformation, simply deploying, operating, and managing our services is no longer enough. To support employee empowerment, we need to define appropriate guardrails to maintain a secure and compliant environment while also enabling innovation. We do that by:

• Applying controls to ensure that users and apps don’t gain access to privileged information.
• Keeping employees from creating agents that violate company policies.
• Balancing between the freedom for employees to share their creations and the need to prevent agent sprawl.
• Delineating which agents are authoritative and safe for enterprise functions.
• Maintaining an inventory of agents to provide lifecycle management.

These guardrails keep our environment safe and secure while simultaneously allowing our employees to define the future of AI-powered productivity.

{Discover how we’re governing AI here at Microsoft.}

Creating the AI-powered IT blueprint

Transformation with AI is hard work, but thankfully our Microsoft Digital team is actively working to share insights from our own transformational experience. We do that in person, through our global network of Experience Centers. We do it virtually, through hundreds of virtual customer engagements each year. And we do it right here on Inside Track, where we share detailed guides, stories, and other artifacts designed to accelerate your own AI-powered digital transformation.

Our commitment is to not only share our IT blueprint, but also to listen to our customers so we can amplify your insights to improve Microsoft enterprise products and services.

“In our Customer Zero capacity, we partner with product teams across the company to bring AI-powered experiences to life. Everything we do as Customer Zero helps Microsoft serve as the showcase for AI-powered digital transformation.”

Nathalie D’Hers, corporate vice president, Microsoft Employee Experience

Customer Zero: A mentality and a promise

Microsoft Digital continues to evolve, and our approach as Customer Zero is key to that evolution. We obsess over applications of AI to ensure that our employees are the most productive in the world. As a customer, you can have even greater confidence in our AI-powered solutions since we’ve already deployed and tested them at a global enterprise scale.

“We’ve become an increasingly strategic contributor to Microsoft’s product offerings, especially in this era of AI,” says Nathalie D’Hers, corporate vice president of Microsoft Employee Experience. “In our Customer Zero capacity, we partner with product teams across the company to bring AI-powered experiences to life. Everything we do as Customer Zero helps Microsoft serve as the showcase for AI-powered digital transformation.”

The next chapter of our Customer Zero journey is the most exciting yet. As we continue to learn, we’ll share more stories from the front lines of AI-powered digital transformation here on Inside Track.

Key takeaways

Here are some things to keep in mind as you contemplate your own organization’s transformational journey with AI:

• The pace of change in IT is faster than it’s ever been. As Customer Zero, Microsoft Digital is focused on co-developing, deploying, governing, and driving adoption of new services, and sharing our IT blueprint so you can learn from our experience.
• Shepherding your company through AI-driven disruption is essential in a complex and rapidly evolving technology environment. Define your own vision for an agentic future and share it with your employees so they understand how they’ll need to learn and grow to support it.
• Empowering your employees while implementing the right guardrails is an effective strategy to maximize the benefits of AI-driven transformation, powered by employee innovation.

Try it out

Ready to enable your own AI-powered transformation? Sign up for a free trial of Copilot Studio and take your first steps toward an agentic future. 

Related links

• Trace the history of digital transformation at Microsoft.
• Learn how our employees are extending enterprise AI with custom retrieval agents.
• Find out how we’re approaching change management with Microsoft 365 Copilot.
• See how we’re embracing this new ‘agentic’ moment at Microsoft.

The post Inside Microsoft: Being Customer Zero in an AI-powered world appeared first on Inside Track Blog.