At Microsoft, we’re developing a growing agent ecosystem that offers diverse options for your workplace demands. This collection of tools acknowledges that there is no single “right” way to build an agent.

“The best agent architecture isn’t the most powerful one. It’s the simplest one that delivers value today while giving you the flexibility to grow tomorrow.”

Patrice Pelland, partner engineering manager, HR Employee Experience

Microsoft 365 Copilot, declarative agents, Copilot Studio, Microsoft Fabric data agents, and Microsoft Foundry agent service all represent valid patterns that are optimized for different kinds of work. Newer ideas like scout-style personal agents and Frontier Tuning (how we fine-tune our agent compliance) offer even more flexibility.

When choosing the best agentic technology for you, the rule of thumb is simple: Select the lightest architecture that fits your user experience, data needs, action model, and governance requirements.

“The best agent architecture isn’t the most powerful one,” says Patrice Pelland, a partner engineering manager in HR Employee Experience. “It’s the simplest one that delivers value today, while giving you the flexibility to grow tomorrow.”

Start with the surface, not the stack

The fastest way to get agent architecture wrong is to begin with technology instead of the workload. A better starting point is the user surface—where the work happens and how people expect to interact with it.

Our agent ecosystem is broad, but each of the core options map cleanly to a different kind of challenge:

• Microsoft 365 Copilot is the default entry point for AI in daily work, embedded across apps like Teams, Outlook, and Word. It excels at summarization, drafting, and reasoning over Microsoft Graph data without requiring users to leave their most familiar tools.
• Microsoft Copilot Cowork takes Microsoft 365 Copilot beyond just answering questions to actually executing on work assignments. Designed for complex, multi-step tasks, it can plan, coordinate, and carry out work across Microsoft 365 applications and connected systems while keeping users in control through approval checkpoints and oversight.
• Declarative agents extend Microsoft 365 Copilot with scoped instructions, knowledge, and lightweight actions. They’re the simplest way to create focused, role-based assistants while staying entirely within the Copilot experience.
• Copilot Studio agents provide a low-code environment for building workflow-driven agents with connectors, triggers, approvals, and multi-channel delivery. They’re best suited for business processes that need structure, governance, and integration across systems.
• Microsoft Foundry Agent Service is a code-first platform for building and operating custom agents with full control over models, orchestration, and runtime. It fits engineering-heavy scenarios that require scalability and tailored architecture.
• Microsoft Fabric data agents specialize in governed analytics over business data, using semantic models, ontologies, and curated datasets. They are designed to answer questions about KPIs, entities, and operations with trusted, auditable context rather than general document retrieval.
• Microsoft Scout and Autopilots introduce always-on personal agents that can coordinate work in the background across Microsoft 365. They focus on continuity—tracking commitments, preparing context, and proactively surfacing what matters next.
• Frontier Tuning enables organizations to teach agents how work actually gets done using company-specific data, workflows, and evaluation signals. It’s a later-stage capability that refines behavior inside compliance boundaries, rather than serving as a starting point.

Taken together, these are layers in a system. The practical decision is to pick the one closest to the user.

Picking the right agentic tool

Use Microsoft 365 Copilot as the default front door for broad productivity and Microsoft 365-grounded experiences. Extend it with declarative agents, connectors, plugins, or APIs when a scenario needs more domain-specific knowledge or actions. Do not treat plain Copilot as the primary architecture when the real need is a departmental specialist, governed workflow, analytics specialist, custom channel, or custom runtime. Those needs fit Copilot Studio, Fabric, or Foundry patterns better.

Use Microsoft Cowork when you want to complete a meaningful piece of work across multiple steps, files, applications, and systems. Cowork is best suited for long-running tasks that require planning, orchestration, and execution over time, while remaining grounded in Microsoft 365 context. Do not use Cowork as a replacement for workflow platforms, analytics specialists, or custom agent architectures. When a task requires governed business processes, specialized analytics, or deep runtime customization, Cowork should coordinate with other agentic tools rather than replace them.

Use Copilot Studio when governed workflow, connectors, approvals, and channel reach matter as much as the chat experience. Do not use it as the default for a small Microsoft 365 Copilot extension or for engineering-first scenarios that require custom model choice, runtime hosting, private networking, or deep orchestration; those fit declarative agents or Foundry better.

Use Microsoft Foundry when low-code or Microsoft 365-native layers hit real limits around orchestration, runtime control, model selection, deployment architecture, or engineering operations. Do not start here for a simple business workflow, no/low-code service agent, or focused Microsoft 365 assistant unless custom runtime control is truly the differentiator.

Use Fabric data agents when you’re looking for analytics expertise in a broader architecture. Fabric IQ, semantic models, and ontology can help translate raw data into business meaning. Do not use them as a general enterprise assistant or workflow engine: they are read-only, analytics-focused, and not intended for broad action-taking, unstructured document help, or custom model/runtime control.

Use Microsoft Scout when you need a personal front-door and coordination layer. It should not serve as a replacement for every enterprise agent architecture. When work crosses into governed process, business analytics, custom orchestration, or domain systems, Scout should delegate to Copilot Studio, Fabric data agents, MCP tools, or Foundry specialists.

Use Frontier Tuning only after the basics are in place: clear workflows, governed data, meaningful evaluation signals, and operational maturity. It is a later-stage optimization for institutional know-how, not the first step for a lightweight assistant or a substitute for sound architecture, data governance, or human-in-the-loop controls.

AI-ready data: The foundation of effective agents

The success of an AI agent depends less on the model it uses and more on the quality of the data that grounds it. Agents can only reason effectively when they have access to information that is trusted, discoverable, secure, and connected to the broader decision-making context. Organizations that invest in AI-ready data create the foundation for more reliable and useful AI experiences.

“The competitive advantage in the age of AI won’t come from access to models alone. Organizations also need to prioritize AI-ready data, which allows agents to reason accurately, securely, and at enterprise scale.”

Ravi Goripathy, principal software engineering manager, HR Employee Experience

AI-ready data goes beyond standard measures of data quality, such as accuracy, completeness, and consistency. It also includes the semantics, governance, and contextual information that help AI systems understand what the data means, how it should be used, and the way it relates to other information across the business. High-quality data is essential, but AI systems need data that is also meaningful, connected, and trustworthy.

As organizations deploy more copilots and agents, data readiness increasingly becomes the limiting factor for success, rather than model availability. A strong AI-ready data foundation helps agents produce better results, apply business context consistently, and operate within appropriate security and governance boundaries. The most effective AI strategies treat data readiness as a core architectural requirement.

“The competitive advantage in the age of AI won’t come from access to models alone,” says Ravi Goripathy, a principal software engineering manager in HR Employee Experience. “Organizations also need to prioritize AI-ready data, which allows agents to reason accurately, securely, and at enterprise scale.”

Building reliable agent systems

As soon as an agent moves from answering questions to taking actions, architecture matters more.

A useful pattern is to separate responsibilities into layers:

MCP tools play a key role here by exposing actions as controlled, reusable capabilities. Instead of giving an agent broad access to systems, you expose bounded functions with defined inputs, permissions, and telemetry. This improves reliability, governance, and auditability.

Similarly, not every interaction belongs in free-form chat. Tasks that require review, approval, or comparison—like approving a budget or evaluating candidates—benefit from structured UI components. MCP apps let you surface those experiences inside conversational flows without rebuilding entire applications. Rather than sending users to a separate system to complete a task, MCP apps can render forms, grids, dashboards, and other transactional experiences directly within the conversation, enabling users to review information, approve requests, update records, and take action in context.

As solutions mature, new patterns emerge. Always-on personal agents—like Scout-style Autopilots—can monitor commitments, prepare context, and proactively surface issues. In parallel, Frontier Tuning introduces a way to teach agents how work actually gets done in a specific organization, using real workflows, evaluation signals, and internal knowledge.

These are powerful capabilities, but they’re not starting points. They’re extensions of an architecture that already works.

A layered approach beats a ‘super agent’

It’s tempting to imagine a single agent that handles context, analytics, workflows, and actions. In practice, a more durable approach is a set of loosely connected specialists that each focus on a specific responsibility.

A Microsoft 365–native assistant can serve as the front door for user interaction, delegating analytics to Fabric data agents, actions to Copilot Studio workflows or MCP tools, and more complex orchestration to Foundry-based agents when needed. This separation makes systems easier to govern, test, and evolve over time.

Before moving to production, architecture should include clear ownership, permission design, governance controls, human approval for sensitive actions, evaluation processes, and monitoring. These are all essential aspects of building an agent system that can be trusted.

Deciding where to start

When faced with multiple valid options, a simple decision path helps:

• If a packaged Microsoft experience already meets most of the need, use it.
• If the experience should live in Microsoft 365, start with Copilot and a declarative agent.
• If the core problem is workflow—approvals, routing, integration—lean toward Copilot Studio.
• If the value depends on governed business data, bring in Fabric data agents early.
• If you need deep control over orchestration, models, or runtime behavior, step up to Foundry.

And across all of these: add structure only where the workload demands it. Use MCP tools for actions, structured UI where chat breaks down, and human review where risk increases.

Most importantly, resist the urge to start at the most complex layer. Many successful solutions begin as simple Microsoft 365 extensions and evolve incrementally into multi-layered systems.

Looking ahead: From tools to systems of work

The trajectory of agent architecture is clear. We’re moving from isolated assistants toward interconnected systems that combine personal productivity, business data, workflows, and continuous coordination.

“Agentic AI is shifting from isolated capabilities to connected systems of work. The organizations that lead will be those that design for evolution—starting simple, learning continuously, and scaling with intention.”

Ravi Goripathy, principal software engineering manager, HR Employee Experience

In the near term, that means more layering: personal agents coordinating work, workflow agents handling processes, analytics agents grounding decisions, and platform agents orchestrating across systems. Over time, capabilities like Frontier Tuning will make these systems more adaptive, learning from how organizations actually operate while staying within governance boundaries.

But the core principle is unlikely to change. The most successful teams won’t be the ones who adopt the most advanced architecture first. They’ll be the ones who start with the smallest viable solution, validate it in real workflows, and scale intentionally—adding complexity only when it delivers clear value.

“Agentic AI is shifting from isolated capabilities to connected systems of work,” Goripathy says. “The organizations that lead will be those that design for evolution—starting simple, learning continuously, and scaling with intention.”

The post Start light, scale intentionally: Choosing the right Microsoft agent architecture appeared first on Inside Track.

As the company’s IT organization, we in Microsoft Digital manage millions of network-connected assets. An asset could mean an employee device, network infrastructure, pieces of equipment that sit inside the data center or buildings, or anything else that gets connected to our network.

Information about these assets previously lived within disparate systems, processes, and teams scattered across the company. This fragmentation created a barrier to the establishment and maintenance of a trusted IT asset inventory.

For example, if a security team needed information such as device ownership, location, and the lifecycle status of a device involved in a potential security incident (which remains rare at Microsoft), the dispersed nature of the previous system meant a lot of manual outreach across the organization. This was time-consuming and frustrating for our IT team, and it also delayed security incident response times.

This limited visibility also made it more difficult to manage the company’s hardware investments in an efficient way, something that we knew needed fixing. And because we realized this is a challenge our customers are also having, we—the company’s Customer Zero—agreed to share what we learned with you along the way.

To get started, we launched a multi-year effort to inventory our enterprise IT assets. This inventory brought together previously disconnected sources, improved data quality, and created the foundation for future AI-powered asset management experiences across the enterprise.

Why asset inventory matters

At Microsoft, we prioritize security over all other business outcomes. Our security teams depend on accurate, up-to-date information about network devices to protect the company’s virtual environment, detect any issues promptly, and respond to security incidents efficiently. Any time spent tracking down essential details like device ownership and network identity can lead to delays in incident response.

“One of the early challenges we faced was that we weren’t operating as a single organization—we were a collection of businesses. Each business leader had developed their own governance frameworks, operating practices, and approaches to managing assets, making consistency and alignment difficult to achieve.”

Debashis Sahoo, principal group manager, Microsoft Digital

We on the IT team don’t control the acquisition process of assets across every business unit of the company, which meant asset inventory was constantly changing and there was no direct oversight for when new devices entered the network.

“One of the early challenges we faced was that we weren’t operating as a single organization—we were a collection of businesses,” says Debashis Sahoo, a principal group manager in Microsoft Digital. “Each business leader had developed their own governance frameworks, operating practices, and approaches to managing assets, making consistency and alignment difficult to achieve.”

Another important element of IT asset inventory is the financial aspect.

We inventory all high-value devices in our network to determine our financial footprint in the IT asset arena, which includes both devices that are being actively used—including laptops, printers, and deployed IoT devices—and devices that aren’t currently being deployed on our network.

“For employee devices in Microsoft, 60% of our employees own more than one device,” says Aniruddha Das, a principal product manager in Microsoft Digital. “For developers, typically they have a primary device. They might also have a backup workstation, but a lot of times what we have seen is these devices sit in inventory and they don’t get deployed, which means we have our capital tied in but not being utilized. We want to know what’s tied in so that we have good information about what to purchase next year.”

These undeployed devices were a prime focus of our inventory efforts. We also prioritized finding which devices had reached their end of service life limits and needed to be replaced. Older devices don’t get patched as often, which can expand the attack surface of IT infrastructure.

The scale of these different challenges meant we couldn’t work on everything at once. Our team worked with security stakeholders to identify the devices and attributes that were most critical to the company’s security on a daily basis. We determined that network devices, lab devices, and Internet of Things (IoT) devices should take priority.

Driving alignment through iterative leadership check-ins

Our answer to this challenge was to build a process that connected to any system that might have information about a security-related asset inside the company.

To create this new system, which we call the Enterprise Asset Data Platform (EADP), we brought together stakeholders from across Microsoft in a Kaizen continuous improvement initiative.

When we waded in, we found the task of standing up a new IT asset inventory proved more wide-ranging in scope than we initially anticipated. Asset data was peppered across myriad systems, each of which served a different business need. The networking, security, real estate, lab, and enterprise asset management teams all had stakes in the outcome of this initiative. We ran a two-day program to identify focus areas and divide them into different segments, such as the IoT and lab segments.

The Kaizen process helped us establish common goals across departments, define what success would look like, and create a roadmap. We used this exercise to create a charter defining what issues we wanted to address and get universal alignment around the shared goal of improving inventory quality and completeness.

We aimed to cut down the time it takes to address and remediate security incidents and stand up weekly, biweekly, and monthly operating reviews around governance for the 12 months following the Kaizen to monitor the progress of the initiative. This was to remove any blocks that might arise and make sure teams were aligned throughout the process.

Modernizing our inventory solution

Expanding our IT asset inventory capabilities meant we needed to modernize the tech that underpinned them. Our device data was initially collected from over 70 sources aggregated into a central inventory, which meant we encountered a wealth of issues with duplication, data quality, and clashing governance.

In ingesting all this data, we took over data management from the teams who distribute devices here at Microsoft, which cleaned up our data because it more accurately reflected what was really being used by our employees and contingent staff. This allowed us to reduce our per-person spend by  22%, a significant savings.

And we’re making more improvements. If one of our business units is planning to buy a new set of laptops or lab devices, we can now “see that,” and can mine our data at that point, instead of hoping to learn about the acquisition from a secondhand source.

The complexity of standing up and maintaining this process demanded that we create our data platform, which we built on Microsoft Fabric. With this system, data is ingested, enriched, reconciled, and surfaced through curated datasets designed for operational use, removing the need to manually review each device and compare it against what’s already on record.

Accelerating our progress using AI

Building a trusted inventory delivered immediate security and operational benefits to our organization. But we quickly realized that the same foundation could support a broader goal: Using AI to help our employees and contingent staff find, understand, and act on asset information. With consistent data now available across the enterprise, we could begin building intelligent experiences on top of the inventory, rather than asking users to navigate dozens of disconnected systems.

As a platform provider, Microsoft Fabric holds all the device data across the company; the majority of our AI workload is on this platform. If a Microsoft team needs to create an AI experience for their business unit and wants to use those capabilities, they can do it by connecting to our Microsoft Digital suite of AI tools.

“AI has improved our engineering efficiency drastically and reduced our time to value significantly. We’re able to deliver value and identify gaps much sooner than before.”

Ashwin Kaul, senior product manager, Microsoft Digital

We’re constantly looking for new ways to create valuable experiences for different domains and business units at Microsoft. We’re currently in the process of creating agent-based experiences that streamline tasks like lab operations, device tracking, and asset updates. Our operators will eventually be able to interact with our AI-powered assistants using natural language, streamlining the process of finding and using the information they need.

Because inventory records are now standardized and trusted, teams no longer needed to spend time reconciling data across systems. That consistency also created an opportunity: using AI to help employees interact with asset information more naturally.

When it comes to selecting which devices a user needs, AI can understand the user’s persona, their role, and make recommendations for which devices would best suit that user’s experience. From there, the user can choose from the AI’s device recommendations. This device selection process, which used to take anywhere from 15–20 days, can now be completed in minutes.

“AI has improved our engineering efficiency drastically and reduced our time to value significantly,” says Ashwin Kaul, a senior product manager in Microsoft Digital. “We’re able to deliver value and identify gaps much sooner than before.”

AI also helps us track and trace and then remediate security issues within our extensive network of IoT devices, which contain thousands of lights sensors, temperature controls, and other device types across the campuses of our offices globally. We partnered with our real estate team to use Microsoft Copilot studio to stand up agentic AI capabilities for data quality checks to make sure that all these devices are being accurately documented.

At our Kaizen event, we chose a goal of a 90% improvement for inventory completeness and accuracy. We’ve made a 74% improvement compared to our baseline as of summer 2026, and we’re on track to end the year with an 85% improvement.

Our AI-powered foundation for the future

For our team in Microsoft Digital, the journey began with a simple objective: Gain a trusted view of the company’s technology assets. The result is a stronger security posture, improved operational efficiency, and a data foundation that can support increasingly intelligent experiences.

One great example of these kinds of new experiences: We’re currently in the process of developing a Labs Asset Management Agent, or LAMA. LAMA will be a human-led, multi-agent Frontier Firm experience for our employees that will hugely simplify Microsoft Labs operations.

Our labs have a huge amount of operational processes to manage and run. Once we simplify these processes and reduce the amount of manual intervention needed to manage lab devices, we anticipate reducing vendor and hardware costs using this agentic AI. Our goal is to speed up the pace at which we can do lab deployments by 50%.

As organizations look for new ways to apply AI, our experience demonstrates an important principle: the quality of AI outcomes depends on the soundness of the underlying data. Building a trusted asset inventory may not be the most visible part of an AI strategy, but it is often one of the most important.

The post Building a trusted IT asset inventory with Fabric and AI at Microsoft appeared first on Inside Track.

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.

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.

As AI capabilities advanced, we saw an opportunity to move beyond individual productivity gains and toward something more transformative: Empowering our developers to build intelligent agents that can automate workflows, streamline operations, and create new business value.

Realizing this vision required more than new tools. We needed to rethink how we foster development, govern innovation, and operate at scale.

“We’ve made a lot of progress enabling our developers to build agents that make us more productive. We’re Customer Zero at Microsoft, which means we’re the first to deploy and use the technology and services that we later sell to our customers. Those learnings give us a unique perspective and story to share about the journey our developers have been on with AI and agents.”

Brian Fielder, vice president, Microsoft Digital

Today, we’re sharing the foundation we built that supports this shift.

We’re driving employees across Microsoft to create and use AI agents—from simple, task-focused solutions to enterprise-grade applications available across the company. It’s all supported by a secure, governed, and extensible platform.

“We’ve made a lot of progress enabling our developers to build agents that make us more productive,” says Brian Fielder, vice president of Microsoft Digital, the company’s IT organization. “We’re Customer Zero at Microsoft, which means we’re the first to deploy and use the technology and services that we later sell to our customers. Those learnings give us a unique perspective and story to share about the journey our developers have been on with AI and agents.”

Within the context of Microsoft Build 2026, we’re sharing what it really takes to move from experimentation to impact. Through this collection of stories and resources, we highlight how we’re empowering our developers to build with agentic AI—from establishing governance and platform capabilities to driving adoption and delivering real-world outcomes. Our goal is to provide practical insights you can use to accelerate your own AI journey.

“We hope you find the journey we’ve been on practical and useful,” Fielder says. “When it comes to agents, we’re accelerating fast and scaling at an enterprise level. As our story continues to evolve, we look forward to sharing it with you.”

Guidance for developers: How we manage agentic AI at Microsoft

These articles outline our vision for agentic AI, showing how we’re building a secure, governed, and extensible foundation for AI agents—from Work IQ and Copilot Studio to Agent 365, Azure DevOps, and Model Context Protocol—so developers can create scalable, high-value solutions across the enterprise.

April 16, 2026

Alex Fleck

Becoming a Frontier Firm: A guide for deploying AI agents based on our experience at Microsoft

May 21, 2026

Alex Fleck

Governing AI agents at scale: Lessons from our journey at Microsoft

• How Work IQ is supercharging our AI usage at Microsoft
• Shaping AI management at Microsoft with Agent 365 and Copilot controls
• Deploying Microsoft Agent 365: How we’re extending our infrastructure to manage agents at Microsoft
• Unlocking enterprise AI extensibility at Microsoft with Microsoft Copilot Studio
• Powering AI value with Microsoft 365 Copilot extensibility at Microsoft
• How our employees are extending enterprise AI with custom retrieval agents
• Transforming our approach to sensitivity labels at Microsoft with Microsoft Entra
• Reclaiming engineering time with AI in Azure DevOps at Microsoft
• Protecting AI conversations at Microsoft with Model Context Protocol security and governance
• Intelligence on tap: How Work IQ enables AI and agents at Microsoft
• Taming our Python dependencies at Microsoft with AI
• AI for Knowledge Management: Keeping support content up-to-date at Microsoft

UPCOMING POSTS

• Our guide for deploying Agent 365 based on our experience at Microsoft

Our IT guide to becoming a Frontier Firm

These stories share our IT playbook for becoming a Frontier Firm, highlighting a practical path to enterprise AI maturity through agentic transformation, operational scale, responsible innovation, and partnership—showing how IT leaders can balance governance, modernization, and employee engagement while building an AI-first organization.

December 4, 2025

Keith Boyd

Becoming a Frontier Firm: Our IT playbook for the AI era

• Enterprise AI maturity in five steps: Our guide for IT leaders
• The agentic future: How we’re becoming an AI-first Frontier Firm at Microsoft
• AI at scale: How we’re transforming our enterprise IT operations at Microsoft
• Transforming into an AI-first Frontier Firm in partnership with our works councils
• Fast Train to the AI Frontier: Balancing risk and innovation in the era of AI at Microsoft

Working as developer in IT at Microsoft in the era of AI

These stories explore what it means to work in Microsoft Digital during the AI era, showing how developers and knowledge workers are reshaping engineering, the employee experience, and their own career growth through AI-powered tools, new ways of working, and personal journeys that reflect the evolving culture of IT at Microsoft.

May 21, 2026

Matthew Cooke

IT on the cutting edge: Working in Microsoft Digital in the era of AI

• Powering the new age of AI-led engineering in IT at Microsoft
• The Frontier Firm: How knowledge workers are forging their own AI tools at Microsoft
• The future of work is here: Transforming our employee experience with AI
• Olutunde Makinde: From Lagos to Redmond, a Microsoft IT engineer’s journey
• Building from the inside: Anahit Hovhannisyan’s impact on IT at Microsoft

Key takeaways

From our journey enabling agentic AI across Microsoft Digital, several key principles have emerged to help organizations move from experimentation to scalable, enterprise-wide impact.

• Treat your organization as Customer Zero. Use your own AI capabilities first to generate real-world insights, validate scenarios, and build credibility before scaling to customers.
• Build a foundation for scale. Establish a secure, governed, and extensible platform that enables developers to create AI agents—from simple solutions to enterprise-grade applications.
• Empower developers to drive transformation. Move beyond productivity gains by enabling developers to build intelligent agents that automate workflows and unlock new business value.
• Align governance with innovation. Rethink how you enable development, govern AI, and operate at scale to balance flexibility with responsible use.
• Connect tools, platforms, and workflows. Integrate AI capabilities across your ecosystem—linking platforms, governance models, and development tools to support consistent, scalable adoption.
• Translate experimentation into impact. Focus on turning early AI exploration into coordinated, enterprise-wide efforts that deliver measurable outcomes.

Try it out

Learn more about Microsoft Build.

Related links

• Read our guide for deploying AI agents based on our experience at Microsoft.
• Learn about how we’re governing AI agents at scale internally.
• Find out how Work IQ is supercharging our AI usage at Microsoft.
• Check out an IT playbook for the AI era and see how we’re becoming a Frontier Firm.

The post Microsoft Build 2026: Empowering our developers to adopt agentic AI at Microsoft appeared first on Inside Track.

These innovations have come in many different forms across every group and function at the company. It’s impossible to capture them all in a single concept or story, but one of the ways that we’ve activated the power of AI for our workforce is Work IQ.

Work IQ isn’t a product.

It’s a shared intelligence layer that enables Microsoft 365 Copilot and AI agents to reason over and understand your organization’s work data, then use that context to generate more relevant responses and actions. This means that the entire Microsoft Graph—including rich unstructured data from your Teams chats and meetings, Outlook emails, Word documents, PowerPoint presentations, and more—is now part of your AI-powered work experience.

“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

Work IQ enables Copilot to not only tailor answers to your role and responsibilities, but also to understand who your most frequent collaborators are, comprehend details about your latest projects, surface deliverables and deadlines, and intuit next steps. Additionally, Work IQ makes it easy for any AI agent to take advantage of the same rich enterprise data to return and act on more contextual results.

“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.”

At Microsoft Digital, the company’s IT organization, we’ve seen firsthand how this intelligence layer is accelerating employee adoption of Copilot and agentic AI as outputs become more perceptive and valuable. Work IQ is a foundational step toward a future where AI has moved beyond isolated assistance and become a trusted professional helper—sometimes described as a digital colleague—that carries out tasks and anticipates needs in every aspect of daily work.

How Work IQ impacts everyday work

One of the most instructive aspects of Work IQ’s impact across our organization is that it happened without a traditional deployment. There was no enablement event for employees or operational playbook distributed to administrators. It didn’t require any changes to the application interfaces. Yet over time, our employee Copilot interactions improved in measurable ways.

“There was a period where we weren’t adding new content to Copilot, and yet I noticed our metrics for quality and user satisfaction kept going up. Why was that? It was because of all these incremental improvements that we refer to as Work IQ.”

Dodd Willingham, principal product manager, Microsoft Digital

This was a direct consequence of introducing a shared intelligence layer into a Microsoft environment that was already rich in work signals. Those work signals are extremely valuable data that was difficult to extract meaning from before the advent of AI. As the technology advanced, we could take full advantage of this data to inform and improve agentic responses.

As Customer Zero for the company, Microsoft Digital was at the forefront of measuring the impact of Work IQ. Our employees saw significant gains in relevance, grounding, and answer coherence in Copilot that were visible in the metrics, even during times when the underlying content remained relatively static. That’s the Work IQ difference.

“There was a period where we weren’t adding new content to Copilot, and yet I noticed our metrics for quality and user satisfaction kept going up,” says Dodd Willingham, a principal product manager in Microsoft Digital. “Why was that? It was because of all these incremental improvements that we refer to as Work IQ.”

At a systems level, Work IQ reasons across a broad cross-section of Microsoft 365 data, including:

• Outlook email content, thread structure, and interaction patterns
• Teams chats, channels, and meeting transcripts
• Calendar events and scheduling metadata
• Documents and files across Word, PowerPoint, Excel, OneDrive, and SharePoint
• Signals that show who collaborates with whom, how often, and in what context

Work IQ can also access structured data in tools like Dynamics 365, Power BI, Power Apps, and other business applications. The ability to extract context and interpret structured and unstructured data in a unified intelligence layer is the reason why Work IQ is making such a difference for our employees.

Making Outlook better

Outlook provides a useful lens on how Work IQ functions because it’s both heavily used by our employees and a highly contextual tool. Although the application hasn’t outwardly changed, the way Copilot interacts with inbox and calendar data has evolved, in part due to richer context provided by Work IQ.

“The intelligence works behind the scenes as you use Outlook. Your inbox just gradually feels more relevant. Outlook adapts to your work patterns, making your inbox feel more like an assistant, instead of a filing cabinet of communications.”

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

Now when you turn to Copilot in Outlook to summarize email threads, it can surface decision points, action owners, and unresolved issues. Instead of treating email as a collection of messages and providing rote summaries, Copilot perceives it as a record of decisions and commitments over time.

Calendar-related experiences are on a similar trajectory. Meeting preparation and follow‑up suggestions are now drawing on prior interactions with the same participants, relevant documents that were previously shared, and historical patterns around similar meetings.

Work IQ isn’t rule-based automation layered on top of Outlook. Users aren’t configuring new filters or workflows. Instead, the system is adapting based on observed patterns, meaning user behavior can remain the same while output quality improves

“The intelligence works behind the scenes as you use Outlook,” says Matthew Marzynski, a principal product manager for core experiences in Microsoft Digital. “Your inbox just gradually feels more relevant. Outlook adapts to your work patterns, making your inbox feel more like an assistant, instead of a filing cabinet of communications.”

Applying persistent memory

Another important aspect of Work IQ is the ability to retain persistent memory of each employee’s role, responsibilities, and work context. Copilot and other agents no longer need to be continually prompted with details about who the user is and what they’re working on. It learns that information and remembers it going forward.

This feature, also called persistent understanding, builds trust and increases efficiency each time an employee turns to AI for help with their work. AI systems that depend on manual context-setting don’t scale well across large organizations, which we at Microsoft Digital learned as we tested and deployed Copilot across the company.

“The user no longer has to tell the agent, ‘I work in this area, so please tailor your response to that’ every time,” says Anishkumar Ramakrishnan, a principal PM manager in Microsoft Digital. “With Work IQ, Copilot and agents recall it going forward. It remembers things that the user doesn’t even remember themselves about their past work and actions. This is the promise of intelligent context.”

From answers to action: Work IQ and AI agents

As organizations move toward integrating AI agents into all aspects of their day-to-day work, the value of Work IQ increases. Any agent—not just a general-purpose agent like Copilot—that can interpret vast amounts of your unstructured work data is going to produce results that are far more relevant than one that simply draws on general knowledge about a topic or process.

“Before, a builder had to go connector by connector and be very prescriptive—calendar read, email read, meeting access—just to build an agent. Now they can simply point the agent to Work IQ, and it gains contextual access across mail, calendar, meetings, and files through a single connector (API or MCP server).”

Naveen Jangir, principal architect, Microsoft Digital

Early agent implementations relied on narrower task-specific access to data. For each agent, a developer would have to build connections to a particular document library, mailbox, or set of calendar data. Each connection required separate consent and management, which generally resulted in a more limited scope.

But with Work IQ, builders can create agents using Microsoft Copilot Studio or other development platforms (such as Microsoft Foundry) that use APIs or Model Context Protocol (MCP) servers to connect to Microsoft Graph data. This enables them to bring the full power of enterprise data to any agentic creation, not just Microsoft 365 agents.

“Before, a builder had to go connector by connector and be very prescriptive—calendar read, email read, meeting access—just to build an agent,” says Naveen Jangir, a principal architect in Microsoft Digital. “Now they can simply point the agent to Work IQ, and it gains contextual access across mail, calendar, meetings, and files through a single connector (API or MCP server).”

This shift doesn’t just simplify agent development—it fundamentally expands what agents are capable of. Instead of operating within narrow, predefined tasks, agents can now reason across a broader work context to deliver better outcomes. For example, an agent supporting a project manager can surface relevant email threads, identify key stakeholders from meeting activity, reference the latest project documents, and highlight upcoming deadlines—all within a single interaction.

Intelligence without bypassing governance

From a governance perspective, Work IQ doesn’t introduce a new security model. Instead, it operates entirely within the existing Microsoft 365 data protection boundaries that our company and our customers already rely on.

The intelligence layer can access this enterprise data, but it does so while honoring permissions, sensitivity labels, access policies, and compliance controls defined at the source. Work IQ can only surface or act on information that the user—or an agent identity acting on the user’s behalf—is already authorized to access.

This inheritance model is intentional. Governance remains rooted in the data layer, not in the AI layer. Work IQ respects established controls such as identity‑based access and tenant policies, which means agents are generally given less access than human users.

“An agent user only gets access to what is explicitly shared with it,” Jangir says. “Human users typically have broader default access. By design in Work IQ, agents can usually see less than people, not more.”

For IT and security teams, this places the emphasis squarely on data discipline and identity controls, which are complementary security layers. Work IQ amplifies the value of well‑governed data and exposes weaknesses where governance is inconsistent. Admins remain in control of access and can turn off APIs and MCP server connections if they want to limit an agent’s data access.

Work IQ, Fabric IQ, and Foundry IQ

As we’ve scaled up Copilot and agentic AI internally, one lesson has become clear: Intelligence works best when it’s part of a layered infrastructure rather than working on its own.

That’s why Work IQ is just one context layer we’re using at Microsoft. We’ve also developed Fabric IQ and Foundry IQ, which are complementary layers in our overall data strategy. Each of these addresses a different aspect of enterprise intelligence.

The three layers serve distinct but connected purposes:

• Work IQ focuses on unstructured productivity data, helping AI understand how people work across email, meetings, documents, and collaboration signals.
• Fabric IQ applies similar reasoning to analytical and structured data, adding context and explanation to metrics, trends, KPIs, and other business signals.
• Foundry IQ provides the foundation for builders to create agents that draw from both worlds, connecting intelligence across Microsoft 365, analytics platforms, and line‑of‑business systems.

Taken together, these layers also contribute to something deeper: the emergence of a shared business ontology. By extracting and aligning business entities—such as people, projects, and processes—from both structured data in Fabric IQ and the unstructured signals captured by Work IQ, the system perceives meaningful connections that previously were hidden. This unified understanding allows agents to reason across domains with greater precision, linking metrics to the real work and making insights more actionable in context.

This architecture matters because it removes artificial seams. Agents shouldn’t need to shift between separate contexts for work content, enterprise data, or application logic. The IQ layers make it possible to deliver a single agentic experience that reasons consistently, applies governance uniformly, and moves with users across environments. Just as importantly, the same controls—identity, permissions, labeling, and policy—flow through each layer, keeping trust intact as capability expands.

At Microsoft, Work IQ and the other context layers are helping Copilot and agents to accelerate beyond AI experimentation. They are now vital operational tools that make everyone more productive across the global enterprise. Context and intelligence in agentic tools are a key part of the future of work, at Microsoft and for our customers as well.

Key takeaways

Here are some things to keep in mind as you prepare your own organization to take full advantage of Work IQ:

• Treat the technology as infrastructure, not a feature. We didn’t formally roll out Work IQ. Its value emerged gradually as it improved Copilot responses and as our agent builders could more easily tap into unstructured enterprise data.
• Expect improvements in AI quality without changes to your data. We saw measurable gains in relevance and user satisfaction even when underlying content remained the same, driven by better contextual reasoning across existing work signals.
• Focus on how employees work, not just what content exists. Work IQ improves AI outcomes by connecting people, relationships, and activity patterns, resulting in more actionable and grounded responses.
• Use Work IQ to move from assistance to action with agents. By giving agents access to contextual enterprise data through a unified layer, we enabled more automated workflows without requiring developers to manage dozens of connectors manually.
• Invest in data governance early to maximize AI value. Because Work IQ inherits permissions and policies from the data layer, its effectiveness—and safety—relies on clear labeling, intentional access design, and disciplined data management.
• Enable self-service collaboration data so it’s available for Work IQ. WorkIQ can only ground on data that is both available and not purposefully hidden. We make sure that our meetings are AI-enabled (and often recorded) and allow self-service in Teams and SharePoint, so the data is not hidden from Work IQ.
• Build toward a unified intelligence model across work and data. Combining Work IQ with Fabric IQ and Foundry IQ means agents can operate seamlessly across different kinds of data and incorporate more intelligence into their output and actions.

Try it out

Get a closer look at Work IQ.

Related links

• Check out how we’re embracing this agentic moment at Microsoft.
• Explore a Microsoft Learn article for more technical details about how Work IQ works.
• Learn how to get started building agents with Microsoft tools.
• Discover our detailed guide on how we deploy AI agents internally at Microsoft.
• 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.

The post How Work IQ is supercharging our AI usage at Microsoft appeared first on Inside Track.

Humans can usually spot the signs of outdated information and exclude it when answering a question or addressing a work topic. But what happens when there’s no human in the loop?

At Microsoft, we’ve embraced the power and speed of agentic solutions across the enterprise. This means we’re at the forefront of developing and implementing innovative tools like the Employee Self-Service Agent, a chat-based solution that uses AI to address thousands of IT support issues and human resources (HR) queries every month—queries that used to be handled by humans. Early results from the tool show great promise for increased efficiency and time savings.

In developing tools like this agent, we were confronted with a challenge: How do we make sure all the unstructured data the tool was trained on is relevant and reliable?

Many organizations are facing this daunting task in the age of AI. Unlike structured data, which is well organized and more easily ingested by AI tools, the sprawling and unverified nature of unstructured data poses some tricky problems for agentic tool development. Tackling this challenge is often referred to as data conditioning.

Read on to see how we at Microsoft Digital—the company’s IT organization—are handling data conditioning across the company, and how you can follow our lead in your own organization.

Read our guide

Learn how to deploy an Employee Self-Service Agent based on our experience here at Microsoft.

How AI has changed the game

We already fundamentally understand that the power of AI and large language models has changed the game for many work tasks. The way employee support functions is no exception to this sweeping change.

“A tool like the Employee Self-Service Agent doesn’t know if something is true or false—it only sees information it can use and present. That’s why stale or outdated information is such a risk, unless you manage it up front.”

David Finney, director of IT Service Management, Microsoft Digital

Instead of relying on human agents to answer employee questions or resolve issues, we now have AI agents trained on vast corpora of data that can find the answer to a complex question in seconds.

But in our drive to give these tools access to everything they might need, they sometimes end up consuming information that isn’t helpful.

“A tool like the Employee Self-Service Agent doesn’t know if something is true or false—it only sees information it can use and present,” says David Finney, director of IT Service Management. “That’s why stale or outdated information is such a risk, unless you manage it up front.”

Before AI, support teams didn’t need to worry as much about the buried issues with unstructured content because a human could generally spot it or filter it out manually. After we turned these tools loose, they began reading everything, including:

• Older or hidden SharePoint content that humans would never find—but AI can
• Large knowledge base articles with buried incorrect information
• Region-specific content that’s not properly labeled

“For example, humans never saw the old, decommissioned SharePoint sites because they were automatically redirected,” says Kevin Verdeck, a senior IT service operations engineer. “But AI definitely could find them, and it surfaced ancient information that we didn’t even know was still out there.”

Data governance is the key

A major part of the solution to this problem is better governance. We had to get a handle on our data.

“We needed to determine the owners of the sites and then establish processes for reviewing content, updating it, and defining how it should be structured. I would highly encourage that our customers think about governance first when they are launching their own AI tools, because everything flows from it.”

Olivier Cherel, senior business process manager, Microsoft Digital

The first step was a massive cleanup effort, including removing decommissioned SharePoint sites and deleting references to retired programs and policies. The next step was making sure all content had ownership assigned to establish who would be maintaining it. This was followed by setting up schedules for regular content updates (lifecycle management).

Governance was the first priority for IT content, according to Olivier Cherel, a senior business process manager in Microsoft Digital.

“We had no governance in place for all the SharePoint sites, which were managed by the various IT teams,” Cherel says. “We needed to determine the owners of the sites and then establish processes for reviewing content, updating it, and defining how it should be structured. I would highly encourage that our customers think about governance first when they are launching their own AI tools, because everything flows from it.”

Content governance was also a huge challenge for other support areas, such as human resources. A coordinated approach was needed.

“HR content is vast, distributed across multiple SharePoint sites, and not everything has a clear owner,” says Shipra Gupta, an engineering PM lead in Human Resources who worked on the Employee Self-Service Agent project. “So, we collaborated with our content and People Operations teams to create a true content strategy: one source of truth, no duplication, with clear ownership and lifecycle management.”

Cherel observes that this process forces teams to think about their support content in a totally different way.

“People realize they need a new function on their team: content management,” he says. “You can’t simply rely on the knowledge found in the technicians’ heads anymore.”

Adding structure to the unstructured data

The simple truth is that part of what makes unstructured data so difficult for agentic AI tools to deal with is that it’s disorganized.

“Our HR Web content already had tagging for many policy documents, which helped us get started. But it wasn’t consistent across all content, so improved tagging became a big part of our governance effort.”

Shipra Gupta, engineering PM lead, Human Resources

AI works best with content that has as many of the following characteristics as possible:

• Document structure, including:
  • Clear headers and sections
  • Page-level summaries
  • Ordered steps and lists
  • Explicit labels for processes
  • HTML tags (which AI can see, but humans can’t)
• Structured metadata, including:
  • Region codes (e.g., US-only policies)
  • Device-specific tags
  • Secure device classification
  • Country-based hardware procurement policies and HR rules

This kind of formatting and metadata allows the AI tool to more clearly parse and sort the information, meaning its answers are going to have a much higher accuracy level (even if it might be a little slower to return them).

“A good example here is tagging,” Gupta says. “Our HR Web content already had tagging for many policy documents, which helped us get started. But it wasn’t consistent across all content, so improved tagging became a big part of our governance effort.”

Be sure that as part of your content review, you’re setting aside the time and resources to add this kind of structure to your unstructured data. The investment will pay off in the long run.

Using AI to help condition data for use

As AI tools grow more sophisticated, we’re using them to directly work on AI-related challenges. This includes using AI on the challenge of unstructured data itself.

“Right now, these efforts are primarily human-led, but we are applying AI to, for example, help write knowledge base articles,” Cherel says. “Also, we’re starting to use AI to determine where we have content gaps, and to analyze the feedback we’re getting on the tool itself. If we just rely on humans, it’s not going to scale. We need to leverage AI to stay on top of things and keep improving the tools.”

Essentially, the future of such technology is all about using AI to improve itself.

“We’re looking at building an agent to help validate content,” Finney says. “We can use it to check for outdated references, old processes, or abandoned terms that are no longer used. Essentially, we’ll have AI do a readiness check on the content that it is consuming.”

Ultimately, the better the data is conditioned, the more accurate and relevant the agent’s responses will be. And that will make the end user—the truly important human in the loop—much happier with the final outcome.

Key takeaways

We’ve highlighted some insights to keep in mind as you consider how to condition your own organization’s data for ingestion by AI tools:

• Unstructured data becomes a business risk when AI is in the loop. AI agents consume everything they can access, including outdated, hidden, or conflicting content, making data conditioning a critical prerequisite for agentic solutions.
• AI highlights content issues that were previously invisible. Decommissioned SharePoint sites, outdated policies, and region-specific content without proper labels all became visible after AI agents began scanning across systems.
• Governance is a vital part of the conditioning process. Assigning clear content ownership and establishing lifecycle management are essential steps in ensuring the content being fed to AI tools is of high quality and is well managed.
• Adding structure to data dramatically improves AI accuracy. Clear document formatting, consistent tagging, and rich metadata help AI agents return more relevant, reliable answers.
• AI will increasingly be used to condition and validate the data it consumes. Microsoft is already exploring using AI to identify content gaps, analyze feedback, and flag outdated information, creating a continuous improvement loop that can scale faster than human review alone.

Try it out

Learn how your organization can take advantage of the power of the Employee Self-Service Agent.

Related links

• Read how we’re accelerating employee services at Microsoft with the Employee Self-Service Agent.
• Learn how we keep our content fresh and well-governed with AI-powered SharePoint.
• Check out how we’re using good governance to ride the wave of agents washing over Microsoft.
• Discover how we’re becoming an AI-first Frontier Firm at Microsoft.
• See how we’re tackling Microsoft 365 Copilot governance internally at Microsoft.

The post Conditioning our unstructured data for AI at Microsoft appeared first on Inside Track.

In light of this priority, we have established a data council to help accelerate our companywide AI-powered transformation.

Our data council is a cross-functional team with representation from multiple domains within Microsoft, including Microsoft Digital, the company’s IT organization; Corporate, External, and Legal Affairs (CELA); and Finance.

“By championing robust data governance, literacy, and responsible data practices, our data council is a crucial part of our AI-powered transformation. It turns enterprise data into a strategic capability that fuels predictive insights and intelligent outcomes across the organization.”

Naval Tripathi, principal engineering manager, Microsoft Digital

Our data council’s mission is to drive transformative business impact by establishing a cohesive data strategy across Microsoft Digital, empowering interconnected analytics and AI at scale. Our vision is to guide our organization toward Frontier Firm maturity through a clear blueprint for high-quality, reliable, AI-ready data delivered on trusted, scalable platforms.

“By championing robust data governance, literacy, and responsible data practices, our data council is a crucial part of our AI-powered transformation,” says Naval Tripathi, principal engineering manager in Microsoft Digital. “It turns enterprise data into a strategic capability that fuels predictive insights and intelligent outcomes across the organization.”

Our evolving data strategy

Over the past two decades, we at Microsoft—along with other large enterprises—have continuously evolved our data strategies in search of the right balance between control and agility. Early approaches were highly decentralized, with different teams owning and managing their own data assets. While this enabled local optimization, it also resulted in inconsistent quality and limited enterprise-wide insight.

Our subsequent shift toward centralized data platforms brought much-needed standardization, security, and scalability. However, as data platforms grew more sophisticated, ownership often drifted away from the business domains closest to the data, slowing responsiveness and diluting accountability.

Today, we and other leading companies are embracing a more balanced, federated approach, often described as a data mesh. Rather than forcing all our data into a single centralized system or allowing unchecked decentralization, the data mesh formalizes domain ownership while embedding governance, quality, and interoperability directly into shared platforms.

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. The result is a system that supports innovation at the edges, strong governance at the core, and seamless collaboration across domains, enabling the transformation of data from a technical asset to a strategic, enterprise-wide capability.

Quality, accessibility, and governance

To scale enterprise data and AI, organizations must first ensure their data is trusted, discoverable, and responsibly governed. At Microsoft Digital, our data strategy is designed to create data foundations that power intelligent applications and effective decision making across the company.

“High-quality, well-governed data is essential to accelerate implementation and adoption of AI tools. Data quality, accessibility, and governance are imperatives for AI systems to function effectively, and recognizing that is propelling our data strategy.”

Miguel Uribe, principal PM manager, Microsoft Digital

By implementing a data mesh strategy at scale, we aim to unlock valuable data insights and analytics, enabling advanced AI scenarios. Our data council focuses on three core dimensions that make AI-ready data possible:

• Quality: Making sure enterprise data is reliable and complete
• Accessibility: Enabling secure and discoverable access to data
• Governance: Protecting and managing our data responsibly

Together, these dimensions form the foundation for scalable innovation and AI-powered data use. They connect data silos and ensure consistent, high‑quality access across the enterprise—enabling both humans and AI systems to work from the same trusted data foundation. As AI use cases mature, this foundation allows AI agents to retrieve and reason over data through enterprise endpoints, while supporting advanced analytics, data science, and broader technology.

“High-quality, well-governed data is essential to accelerate implementation and adoption of AI tools,” says Miguel Uribe, a principal PM manager in Microsoft Digital. “Data quality, accessibility, and governance are imperatives for AI systems to function effectively, and recognizing that is propelling our data strategy.”

Quality

AI-ready data is available, complete, accurate, and high-quality. By adopting this standard, our data scientists, engineers, and even our AI agents are better able to locate, process, and govern the information needed to drive our organization and maximize AI efficiencies.

By utilizing Microsoft Purview, our data council can oversee the monitoring of data attributes to ensure fidelity. It also monitors parameters to enforce standards for accuracy and completeness.

Accessibility

Ensuring that our employees get access to the information they need while prioritizing security is a foundational element of our enterprise data strategy. Microsoft Fabric allows us to unify our organization’s siloed data in a single “mesh” that enables advanced analytics, data science, data visualization and other connected scenarios.

Microsoft Purview then gives us the ability to democratize that data responsibly. By implementing a data mesh architecture, our employees can work confidently, unencumbered by siloed or inaccessible data, and with the assurance that the data they’re working with is secure.

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-wide trust.  

Governance

As organizations scale AI capabilities, strong governance becomes essential to ensure security, compliance, and ethical data use. Data governance—which includes establishing data policies, ensuring data privacy and security, and promoting ethical AI usage—is critical, as is compliance with General Data Protection Regulation (GDPR) and Consumer Data Protection Act (CDPA) regulations, among others.

However, governance is not only a technical capability; it’s also a cultural commitment.

Responsible data use must be embedded into the way teams manage data and build AI solutions. Through Microsoft Purview, we implemented an end-to-end governance framework that automates the discovery, classification, and protection of sensitive data across the enterprise data landscape.

This unified approach allows teams to innovate confidently, knowing that the data powering their insights and AI systems is trusted and protected, as well as responsibly managed.

“AI systems are only as reliable as the data that powers them,” Uribe says. “By investing in trusted and well-managed data, we accelerate not only the adoption of AI tools but our ability to generate meaningful insights and intelligent outcomes.”

The data catalog as the discovery layer

By serving as a common discovery layer for humans and AI, the data catalog ensures that governance translates directly into speed, accuracy, and trust at scale.

A unified data strategy only succeeds if both people and AI systems can consistently find the right data. At Microsoft, this is enabled by our enterprise data catalog, which operationalizes the standards set by our data council. 

For business users, the catalog provides intuitive search, ownership transparency, and trust signals—enabling confident self‑service analytics. For AI agents, the same catalog exposes machine‑readable metadata, allowing agents to programmatically discover canonical datasets, validate schema and freshness, and respect governance constraints.

Our role as Customer Zero

In Microsoft Digital, we operate as Customer Zero for the company’s enterprise solutions, so that our customers don’t have to.

That means we do more than adopt new products early. We deploy them at enterprise-scale, operate them under real‑world constraints, and hold them to the same standards our customers expect. The result is more resilient, ready‑to‑use solutions and a higher quality bar for every enterprise customer we serve.

“When we engage product teams with real telemetry from how data is created, governed, and consumed at scale, we move the conversation from theory to execution. That’s how enterprise readiness becomes real.”

Diego Baccino, principal software engineering manager, Microsoft Digital

Our data council embodies this Customer Zero mindset through its Enterprise Readiness initiative. By engaging product engineering as a unified enterprise voice, the council drives strategic conversations that surface operational blockers, influence roadmap prioritization, and ensure new and existing data solutions are truly ready for enterprise use.

These learnings are then shared broadly across Microsoft Digital to accelerate adoption, reduce duplication, and scale proven patterns across teams.

“When we engage product teams with real telemetry from how data is created, governed, and consumed at scale, we move the conversation from theory to execution,” says Diego Baccino, a principal software engineering manager in Microsoft Digital and a member of the council. “That’s how enterprise readiness becomes real.”

This work is deeply integrated with our AI Center of Excellence (CoE), where Customer Zero principles are applied to accelerate AI outcomes responsibly. Together, the AI CoE and the data council focus on improving data documentation and quality—foundational capabilities that are required to make AI feasible, trustworthy, and scalable across the enterprise.

By grounding AI innovation in measurable data quality and governance standards, Microsoft Digital ensures that experimentation can safely mature into production‑ready solutions. The partnership between our data council, our AI CoE, and our Responsible AI (RAI) Council is essential to our broader data and AI strategy.

“AI readiness isn’t aspirational—it’s operational,” Baccino says. “By measuring the health of our data, setting clear quality baselines, and using those signals to guide product and platform decisions, we turn data into a strategic asset and AI into a repeatable capability.”

Together, these teams exemplify what it means to be Customer Zero: Transforming enterprise experience into action, governance into acceleration, and data into durable competitive advantage.

Learn more

Read about our AI Center of Excellence.

Advancing our data culture

Our data council plays a pivotal role in advancing the organization transition from data literacy to enterprise data and AI capability. In conjunction with our AI CoE, it creates curricula and sponsors learning pathways, operational practices, and community programs to equip our employees with the skills and mindset required to thrive in a data- and AI-centric world.

While early efforts focused on improving data literacy, our data council ’s mission has evolved to enable data and AI capability at scale together with our AI CoE—where employees not only understand data but can effectively apply it to build, operate, and govern intelligent solutions.

“Our focus is not just teaching our teams about data. It is enabling employees to apply data to create AI-driven outcomes. When teams understand how data powers AI systems, they can make better decisions, design better products, and build more responsible AI experiences.”

Miguel Uribe, principal product manager, Microsoft Digital

Our curriculum includes high-level courses on data concepts, applications, and extensibility of AI tools like Microsoft 365 Copilot, as well as data products like Microsoft Purview and Microsoft Fabric.

By facilitating AI and data training, offering internally focused data and AI certifications, and internal community engagement, our council ensures that employees develop the capabilities required to responsibly build and operate AI-powered solutions. Achieving data and AI certifications not only promotes career development through improved data literacy, it also enhances the broader data-driven culture within our organization.

“We recognize that AI capability is built when data skills are applied directly to real AI scenarios and business outcomes—not when learning exists in isolation,” Uribe says. “Our focus is not just teaching our teams about data; it is enabling employees to apply data to create AI‑driven outcomes. When teams understand how data powers AI systems, they can make better decisions, design better products, and build more responsible AI experiences.”

Find out more

Learn how a Copilot Expo helped us build interest in Microsoft 365 Copilot.

Lessons learned

Our data council was created to develop and execute a cohesive data strategy across Microsoft Digital and to foster a strong data culture within our organization. Over time, several critical lessons have emerged.

Executive sponsorship enables transformation

Executive sponsorship is a key element to ensure implementation and adoption of a data strategy. Our leaders are committed to delivering and sustaining a robust data strategy and culture and have been effective champions of the council’s work.

“Leadership provides support and reinforcement of the council’s mission, as well as guidance and clarity related to diverse organizational priorities,” Baccino says.

Cross-functional collaboration accelerates impact

Our council’s work has also benefited from the diverse representation offered by different disciplines across our organization. Embracing diverse perspectives and understanding various organizational priorities is critical to implementing a successful data strategy and culture in a large and complex organization like Microsoft Digital.

Modern platforms allow for scalable AI productivity

Technology and architecture also play a critical role in enabling enterprise data and AI capability. Platforms like Microsoft Purview and Microsoft Fabric provide the governance, discovery, and analytics infrastructure required to create trusted, AI-ready data ecosystems.

Combined with strong leadership support and community engagement, these platforms allow our organization to move beyond isolated data projects toward connected, enterprise-wide intelligence.

As our organization continues to evolve, our data council’s strategic work and valuable insights will be crucial in shaping the future of data-driven decision making and AI transformation at Microsoft.

Key takeaways

Here are some things to keep in mind as you contemplate forming a data council to help you manage and scale AI impacts responsibly at your own organization:

• A data mesh strikes the balance enterprises have been chasing. By formalizing domain ownership while enforcing standards through shared platforms, you avoid both chaotic decentralization and slow, over-centralized control.
• Governance is an accelerator when it’s automated and embedded. Using platforms like Microsoft Purview and Microsoft Fabric, governance shifts from a manual gatekeeping function to a built‑in capability that enables faster, trusted analytics and AI.
• AI systems are only as strong as their discovery layer. A unified enterprise data catalog allows both people and AI agents to find, trust, and use data consistently—turning standards into operational speed.
• Customer Zero turns theory into enterprise‑ready execution. By operating its own data and AI platforms at scale, Microsoft Digital provides real telemetry and practical feedback that directly shapes product readiness.
• Building AI capability is a cultural effort, not just a technical one. Our data council’s focus on applied learning, certification, and real-world AI scenarios ensures data skills translate into durable business outcomes.
• AI scale exposes the cost of fragmented data ownership. A data council cuts through silos by aligning priorities, resolving tradeoffs, and concentrating investment on the data assets that matter most for AI impact.
• Shared metrics create shared ownership. Publishing data quality and AI‑readiness scores at the leadership level reinforces accountability and positions data as a core enterprise asset.

Try it out

New to Microsoft 365 Copilot? Check it out!

Related links

• Learn more about the councils that are steering AI projects at Microsoft.
• See how we’re tackling Microsoft 365 Copilot governance internally at Microsoft.
• Explore how we’re unlocking enterprise AI extensibility at Microsoft with Microsoft Copilot Studio.
• See AI-powered agents in action: How we’re embracing this new ‘agentic’ moment at Microsoft.

The post Harnessing AI: How a data council is powering our unified data strategy at Microsoft appeared first on Inside Track.

But as data sprawls across teams, systems, and borders, the task of ensuring that it remains secure, accurate, and well-governed is a daunting one. A sound approach to data governance is the backbone of responsible data use across the enterprise, creating clarity around data ownership and access.

In an organization the size of Microsoft, no single team can carry this responsibility on its own. Effective data governance must be a distributed effort across all departments and functions.

This story explains how our marketing organization uses the Microsoft Purview Unified Catalog to organize and standardize the data we rely on daily. By putting clear ownership, consistent definitions, and reliable governance in place, we’re turning fragmented, unreliable data into an advantage that supports faster decisions and more effective campaigns.

Data governance at scale

As companies grow, their data governance becomes increasingly complex, with different teams creating their own versions of key data concepts, often without realizing it. The complexity is most visible in the way users across an organization define foundational terms.

“We found adoption to be much easier when helping teams focus on building more value in their data instead of driving governance like a compliance effort.”

Nick Doughty, senior product manager, Microsoft Purview Unified Catalog

Examples in marketing include what counts as a customer (active vs. inactive, marketing- or sales-qualified), what constitutes sensitive data (personally identifiable information, behavioral data, partner data), and what a metric means (conversion, engagement, attribution windows).

When inconsistent practices take hold, ownership becomes murky. With the increasing demands that managing data quality and integrity put on our leaders and their teams, effective data governance becomes one more hurdle to productivity.

“We started off implementing data governance like an issue register,” says Nick Doughty, a senior product manager within Microsoft Purview Unified Catalog. “Then we progressed to more of an enforcement method, similar to how we were doing security at the time. We found that when we started to push really hard on teams, similar to how we drove other compliance efforts, it was difficult for them to justify or understand why they would want the added governance.”

The introduction of Microsoft Azure Purview in 2020 marked a turning point.

A united platform for data governance, security, and compliance, Purview helps organizations understand, protect, and manage data across environments. It also addresses fragmented data, lack of visibility into where sensitive data lives and how it moves, compliance complexity with regulations (including GDPR and HIPAA), and security risks.

“Our marketing teams used to spend hours hunting for the right customer list because multiple versions lived in different locations, each with unclear owners and inconsistent labels. Now our marketers can trust they are working from current information, while avoiding compliance risks associated with incorrect or unauthorized data.”

Sourabh Mathur, principal engineering lead, Global Marketing Engines and Experiences

The Purview Unified Catalog serves as the AI-powered backbone, automatically discovering, classifying, and organizing information so users can easily find and trust the data they need.

By launching the unified catalog, we gave our users a consistent way to understand and use their data, while reinforcing strong governance and compliance practices. The result is data that’s more discoverable, reliable, and actionable. (The product was renamed Microsoft Purview in 2022 and became part of Microsoft 365 compliance tools.)

“Our marketing teams used to spend hours hunting for the right customer list because multiple versions lived in different locations, each with unclear owners and inconsistent labels,” says Sourabh Mathur, a principal engineering lead in Global Marketing Engines and Experiences, who helped set up Purview for our marketing organization.

With the unified catalog in place, Purview surfaces the dataset, shows its lineage, and applies the correct sensitivity classifications.

“Now our marketers can trust they are working from current information, while avoiding compliance risks associated with incorrect or unauthorized customer data,” Mathur says.

Powering marketing at Microsoft with Purview

With more than 200 Microsoft Azure subscriptions, our marketing organization manages one of the largest data estates at the company. The team faces the constant challenge of scattered data, unclear data ownership, and inconsistent governance practices that slow down campaigns and increase compliance risk.

“Marketing can now scale governance across hundreds of data products, support self-service data collection with guardrails, automate access decisions, and enable AI workloads on trusted data.”

Deepak Kumar Biswal, principal software engineering lead, Global Marketing Engines and Experiences

By adopting Purview, our marketing team gained unified visibility, clearer classification standards, and smoother collaboration with other departments, like IT and legal. This reduces friction while strengthening data protection.

The result is an organization that moves faster with greater confidence in how it handles customer and campaign data.

Instead of relying on legacy knowledge, forcing users to dig through different servers and SharePoint sites, or constantly sending queries to the engineering teams, our marketing professionals can now explore the curated Purview Unified Catalog, making streamlined, efficient data discovery possible.

“Marketing can now scale governance across hundreds of data products, support self-service data collection with guardrails, automate access decisions, and enable AI workloads on trusted data,” says Deepak Kumar Biswal, a principal software engineering lead in Global Marketing Engines and Experiences. “Purview turns responsible data use into everyday practice, not extra work.”

Data governance and security: Two sides of the same coin

For our marketing organization, data governance and security are inseparable concepts. As soon as you have customer information, you need to make sure it’s secure—sensitive data must be carefully defined, consistently managed, and protected from misuse or breach.

Purview supports this goal by combining governance capabilities with security and compliance controls that provide added layers of protection.

Within marketing, the governance and security teams work closely together. Good governance measures ensure our data is properly defined and standardized, while strong security policies ensure it’s handled with proper safeguards. By pairing governance with strong security practices, our marketing team can remain compliant with data privacy laws, prevent misuse of sensitive information, and foster trust across their organization.

When our marketing team began its Purview journey five years ago, it adopted a centralized governance model. Much like the structure of a government—where federal, state, and local entities each play a role—our approach allows both centralized standards and local autonomy. This creates consistency across the organization without stifling agility.

Our Data Governance team took on the role of steward, defining standards, onboarding systems, and collaborating with its IT partners to connect data environments. Existing assets like data dictionaries and process flows were used to seed the catalog, ensuring the team started from known ground rather than reinventing definitions from scratch.

This deliberate, incremental approach allowed our marketing team to thoughtfully build out healthy governance practices. By moving slowly, the team learned from each step on its journey, refining processes and establishing consistent practices as it moved along.

For example, working closely with our team in Microsoft Digital allowed them to experiment with different ways of discovering and cataloging their data. This involved taking learn and refine how Purview tuned their data before they rolled anything out broadly.

Our goal is to transition to a completely federated model in which responsibility shifts outward. Rather than the marketing governance team doing all the stewardship, individual groups will take ownership of their data within Purview. This shift distributes accountability, embeds governance deeper into daily operations, and makes it easier for teams to monitor data quality and enforce standards on their own.

Impact across the enterprise

Since adopting Purview Unified Catalog, we’ve seen tangible results across our data estate and our data governance practices in marketing and across all verticals within the company. Here are some companywide highlights:

• Better consolidation: We’ve unified five catalogs into one.
• Increased scale: We added 250 data sources onboarded in six months, representing roughly 10 million assets.
• Higher internal adoption: We set up more than 50 governance domains, an effort we supported with reusable training assets, guides, and onboarding materials.

The benefits also include and extend beyond marketing:

• Teams across the company are gaining increased confidence in their data definitions.
• Compliance and privacy obligations are being met more effectively.
• Business value is being generated through better, more trusted use of data.
• Organizations are benefiting from faster time-to-insight.

Launching the marketing governance domain

We’re using Purview to combine essential capabilities like data governance, classification, and quality checks across our Microsoft services, which creates a unified foundation for our enterprise-wide metadata management. These unified capabilities make Purview an indispensable tool for us, and for large-scale enterprises.

“With various role types like data curator and data reader, we can add more visibility into our data—where it lives, how it’s being used, and who are its primary owners. Clearly defining these parameters helps us use the data governance framework as a starting point and improve our data governance capabilities.”

Vinny Singh, principal program manager, Global Marketing Engines and Experiences

As early adopters of Purview Unified Catalog, the group launched the Marketing Governance domain, registering more than 200 data products using the Unified Catalog’s data map.

The products, spanning various datasets, are aligned with strict internal governance standards. This gives marketing the ability to govern, classify, and track data across its ecosystem—ensuring adherence to GDPR and other regulatory compliance measures.

“With various role types like data curator and data reader, we can add more visibility into our data—where it lives, how it’s being used, and who are its primary owners,” says Vinny Singh, a principal program manager in Global Marketing Engines and Experiences. “Clearly defining these parameters helps us use the data governance framework as a starting point and improve our data governance capabilities.”

Key takeaways

Our journey with Microsoft Purview Unified Catalog has generated key insights that you can apply to your own data governance efforts. These include:

• Start small: Don’t try to “boil the ocean.” Begin with three to five governance domains and scale from there.
• Leverage what you have: Data dictionaries, glossaries, and existing documentation provide a strong starting point for a governance platform founded on the Purview Unified Catalog.
• Focus on value, not enforcement: Governance resonates when teams see how it helps them, not when it’s mandated.
• Adapt to your organization: Each team at your company will use Purview differently. Flexibility helps encourage adoption.
• Build community: Data governance is not a solo effort. Collaboration among stakeholders produces stronger standards and better results.

Try it out

Sign up for a free trial of Microsoft Purview.

Related links

• Learn about Microsoft Purview Unified Catalog.
• View a practical handbook for securing your data with Microsoft Purview.
• Explore how to use Security Copilot in Microsoft Purview to protect your data.
• Find out how we’re riding the wave of agents washing over Microsoft with good governance.
• Discover more about Microsoft Purview data governance.

The post Powering data governance at Microsoft with Purview Unified Catalog appeared first on Inside Track.

• Deploying Microsoft 365 Copilot: A next-generation business tool
• Chapter 1: Getting governance right
• Chapter 2: Implementation with intention
• Chapter 3: Driving adoption to capture value
• Chapter 4: Building a foundation for support
• Chapter 5: Extending Copilot through agents
• Applying our deployment lessons at your company

Deploying Microsoft 365 Copilot: A next-generation business tool

Welcome to the new era of productivity

Generative AI has captured the world’s attention, and businesses are taking notice.

According to our Work Trends Annual Report, 70% of people would delegate as much work as possible to AI to lessen their workloads.

“I’m inspired by the transformative power of AI. I’ve been impressed with how quickly our employees have put it to work for them.”

Andrew Osten, general manager, Microsoft Digital

Capitalizing on this trend will mean the difference between surging ahead or getting left behind, including here at Microsoft, where we’re the first enterprise to deploy Microsoft 365 Copilot fully.

“I’m inspired by the transformative power of AI,” says Andrew Osten, general manager of Business Operations and Programs in Microsoft Digital, the company’s IT organization. “I’ve been impressed with how quickly our employees have put it to work for them.”

He would know. His team is responsible for driving usage and adoption of Copilot and any new features to more than 300,000 employees and vendors across the world.

“Customers are looking to us to share what we’ve learned as the first enterprise to deploy Copilot,” Osten says. “Our team has a unique opportunity to help them deploy and get to value as quickly as possible.”

Meet Microsoft 365 Copilot

Copilot combines the power of large language models (LLMs) with your organization’s data to turn your employees’ words into some of the most powerful productivity tools on the planet—all within the flow of work. Employees can access intelligent assistance through Microsoft 365 Copilot Chat or the apps they use every day, including Word, Excel, PowerPoint, Outlook, Teams, and more, to provide real-time intelligent assistance. It also forms the foundation for new, agentic capabilities that apply the power of Copilot orchestration to more specific knowledge sources and tasks.

According to our Work Trends annual report, employees who use AI are seeing significant benefits.

Organizations like ours that are unlocking AI assistance within employees’ everyday workflows are poised to gain a distinct advantage in terms of productivity, engagement, and innovation.

“We’re using it to reduce our IT expenses and enhance our productivity,” Osten says. “We’re also excited by its potential to create a lasting competitive advantage for us here at Microsoft and for our customers.”

Our mission in Microsoft Digital is to empower, enable, and transform the company’s digital employee experience across devices, applications, and infrastructure. We also provide a blueprint for our customers to follow in the form of this guide for deploying and adopting Copilot.

“The contents of this guide are based on the lessons we’ve learned deploying Copilot,” Osten says. “The tips and ideas you’ll read here will help you accelerate your own time to value with Copilot so you can realize the same benefits as our employees.”

Are you an executive?

View our condensed, CIO version of the guide here.

Chapter 1: Getting governance right

Maintaining privacy, security, and compliance while respecting regulatory frameworks.

Before you begin your Microsoft 365 Copilot implementation, you’ll want to consider how this tool impacts your data. Copilot employs LLMs that interact with data and content across your organization. It uses information your employees can access to transform user prompts into personalized, relevant, and actionable responses throughout Microsoft 365 apps.

Giving your employees this level of access means proper data hygiene is essential. At Microsoft Digital, we use sensitivity labeling to empower our employees with access while also protecting our data. Our colleagues on the product side designed Copilot to respect labels, permissions, and rights management service (RMS) protections that block content extraction on relevant file labels. By implementing effective sensitivity labeling practices, you can rest assured that anything you intend to remain private or confidential will stay that way.

Pick the governance path that’s right for you

This chapter outlines the highly robust, best-case scenario we created at Microsoft, but we know not every organization has a fully deployed data governance system and strategy. If you’re in that position, don’t worry! You can use techniques like Restricted SharePoint Search that provide value and protection without exposing Copilot to your internal resources.

Laying the groundwork with proper labeling

Throughout our internal governance efforts within Microsoft Digital, we’ve developed four labeling practices that make up our foundation for appropriate policies and settings.

Responsible self-service

Support and enable your employees to create new workspaces like SharePoint sites, ensuring your company data is on your Microsoft 365 tenant and employees don’t simply re-use and overload existing spaces with mismatching permissions. That enables your employees to take full advantage of Copilot in ways that align with your organizational data hygiene while you keep your company’s information safe.

Top-down defaults

Label containers for data segmentation by default to ensure your information isn’t overexposed. At Microsoft, we default our container labels to “Confidential\Internal Only.” That ensures alignment with our policies and settings that limit external sharing. We use Microsoft Purview to manage this process.

Consistency within containers

Derive file labels from their parent containers. Being consistent here boosts security across every layer and reduces the administrative burden on your employees to label every file they create. Copilot will reflect file labels in chat responses, so employees know the level of confidentiality behind each portion of AI-created responses.

Employee awareness

We train our employees to understand how to handle and label sensitive data. By making your workers active participants in your data hygiene strategy, you increase accuracy and your overall security posture.

Self-service with guardrails

The data hygiene practices we outlined above form a foundation for compliance and security, but backstopping those efforts through Microsoft 365 features adds an extra layer of protection. That’s a core principle of Zero Trust.

At Microsoft Digital, we use Microsoft Purview Data Loss Prevention (DLP) policies to define the rules and actions for detecting and protecting sensitive data across Microsoft 365, SharePoint, OneDrive, and Teams. DLP policies support vulnerable data types and scenarios that require protection. Those include any kind of information that might introduce inappropriate access to company data or intellectual property. Examples include access to credentials like keys or tokens, personally identifying information, financial data, or non-public source code.

Sign-in information, reports, and dashboards are available via Purview to help our team monitor and analyze content activity and compliance across the organization. They also provide insights into the volume, location, and usage of sensitive data, as well as any incidents and alerts that indicate potential data breaches or violations.

For example, an employee might label something as “General,” but it contains credentials or other sensitive end-user identification information (EUII). In those instances, Purview will automatically block the file from access beyond its owner or reapply a more appropriate label.

Between proper labeling and backstopping self-service through DLP guardrails, we’re able to keep Copilot Chat from surfacing documents it shouldn’t share in the wrong context or to the wrong people. Using Purview and other tools at our disposal, the five practices below help us keep our employees and our company’s data safe.

Trust, but verify

Empower self-service with sensitivity labels, but verify them by checking against DLP standards, then use auto-labeling and quarantining when necessary. Internally, we’ve configured Microsoft Purview DLP to detect and control sensitive content automatically.

Expiry and attestation

Put strong lifecycle management protocols in place that require your employees to attest containers to keep them from expiring. We don’t keep items that don’t have an accountable employee or that might not be necessary for our work.

Controlling the flow

Limit oversharing at the source by enabling company-shareable links instead of forcing employees to grant access to large groups. At Microsoft, we add an extra layer of highly confidential items that users can only share with specific people on a need-to-know basis. To enforce these behaviors, you can set default link types based on labels through Purview.

Oversharing detection

Even under the best circumstances, accidents happen. When one of our employees does overshare sensitive data, we use Microsoft Graph Data Connect extraction in conjunction with Microsoft Purview to catch and report oversharing.

International compliance: No size fits all

Europe has extra requirements in the form of EU Data Boundary regulations and works councils, internal organizations that provide employee co-determination on workers’ rights or regulatory issues, including performance management or monitoring. Our Copilot deployment meant we needed to partner closely with our Microsoft works councils when launching AI technology with complex data and privacy implications.

Your experience will vary depending on your industry and where you operate, but we’ve learned that it’s best to work closely with local subsidiaries to ensure you have a complete picture of a region’s regulatory situation. Local insiders are poised to liaise with their works councils, as we’ve done at Microsoft, or other bodies through direct relationships. Start the process early so you can manage feedback cycles effectively, make adjustments, synthesize any answers that works councils need, and resolve any concerns through configurations that make sense for your employees.

Learning from Microsoft’s governance, security, and compliance practices

Bring the right people into the conversation

Don’t keep this conversation in the IT sphere alone. Bring in all the relevant security, legal, and compliance professionals.

Build a foundation for automation

Microsoft Purview DLP has powerful intelligent detection, but it relies on establishing good defaults.

Think about how your employees will use Copilot

Determine the primary use cases for Copilot. The kinds of collaboration and access employees need will affect your default labeling architecture.

Take this opportunity to train employees

If you’ve been looking for an excuse to refresh employee knowledge around data privacy, let this moment be your milestone. It will be far easier to start with a clean data estate.

Don’t overwhelm your users

Make labeling simple and intuitive and ensure it isn’t overwhelming. Employees should have a limited set of choices to keep things comprehensible. It’s also valid for different employees to see different choices.

Balance good governance with time to value

Because of the scope and complexity of our deployment, we took a very thorough approach to governance. If speed is your priority, you might consider a faster deployment with a less comprehensive governance approach, for example, using Restricted SharePoint Search to constrain both Enterprise Search and Copilot experiences to a curated set of SharePoint sites of your choice.

Key takeaways

Use these tips to tackle governance, security, and compliance at your company. It’s based on what we learned deploying Copilot internally here at Microsoft.

1) Labeling

• Develop a labeling taxonomy. This should include:
  • Classification levels, not exceeding five primary labels and five sub-labels
  • Descriptions clearly outlining a label’s meaning for employees
  • Examples to clarify usage for employees
• Determine policies and settings that correspond with labels. Consider the following:
  • Storage type and location
  • External allowance
  • Encryption
  • Access control
  • Data destruction
  • Data loss prevention
  • Public disclosure
  • Logging and tracking access
• Establish container defaults
• Configure container labels to set the default file label in document libraries
• Initiate an employee education initiative

2) Data loss prevention

• Configure Microsoft Purview DLP standards and quarantining protocols
• Establish lifecycle management and attestation protocols
• Configure Microsoft Graph Data Connect to discover where you’re oversharing

3) International compliance

• Initiate conversations with local subsidiaries
• Engage works councils or other advocacy bodies
• Address concerns
• Determine the feasibility of regional deployment and segment if necessary

Key actions:

How we did it at Microsoft

• Read our guide to governing Microsoft 365 Copilot. This step-by-step resource, based on our internal experience at Microsoft, will help you adapt your existing governance standards to reflect the new realities of AI.

• Check out how we’re using sensitivity labels to empower our employees while making Microsoft more secure. This article shows you how having a pre-established plan for labeling helped our Microsoft Digital team quickly deploy Copilot with proper guardrails.

• See how we’re riding the wave of agents that’s washing over Microsoft through good governance. This post talks about the ways good governance also pertains to enterprise AI extensibility through agents.

Further guidance for you

• Conduct the Microsoft 365 Copilot Optimization Assessment to understand your level of AI adoption and chart your course.
• Access Microsoft Learn documentation to read more about data, privacy, security, and Zero Trust principles.
• Follow the company’s governance and security overview for Copilot Studio to ensure your agents are managed safely and efficiently.

Chapter 2: Implementation with intention

Building a strategy for licensing, administration, and rolling Microsoft Copilot out to different groups within your organization.

Implementing Microsoft 365 Copilot isn’t as easy as just turning on licenses and alerting your users. It takes organizational partnerships, early assessments of your concrete business needs, and careful planning.

Design for the “who”

Copilot is a new concept in business software. At the time of our implementation, we were the first company to roll it out anywhere in the world, and our Microsoft Digital implementation team had to choose from countless ways to approach a licensing strategy—different mechanisms of licensing, automation, management, and the list goes on. Regardless of your overall approach, we’ve learned from experience that it almost always makes sense to start with pilot groups who can validate the tool and enable the rest of your organization.

For us, that looked like this:

Phase zero

We gave our Copilot engineering team early access so they could test Copilot from an insider’s perspective. We shared our early feedback with the product team.

• Product engineers

Phase one

We issued a limited number of on-demand licenses in core scenarios to get validation and feedback for key use cases.

• Sales
• Marketing

Phase two

We extended licenses to teams who needed to understand and support Copilot or provide approval for use.

• Support
• HR
• Legal
• Security
• Works councils

Phase three

We deployed Copilot and started working on adoption across the entire company.

• Advancing in phases, we deployed Copilot to more than 300,000 Microsoft employees and external staff.

Scaling out your licenses

After you decide on the general shape of your rollout, you can begin building your licensing strategy. Fortunately, if your organization uses Microsoft 365, you’ll already have access to most of the apparatus you need. The inherent flexibility of Microsoft 365 licensing means you can easily adjust your strategy as you progress based on scale, organization changes, or any other factors.

At Microsoft Digital, we started with individual licenses at the single-user level. As our implementation scaled, we tied licensing automation to Microsoft 365 security groups to implement targeted licensing changes at scale. Those groups could include tailor-made subsets of employees or entire organizations within Microsoft, and we keyed our automation logic to their expanding and contracting eligibility.

We highly recommend defining a phased rollout strategy and structuring your groups accordingly. That creates accountability and gives your IT admins a crucial point of contact for understanding the licensing needs of different groups within your organization.

Based on our implementation experience, there are three main benefits to using security groups:

Optimize licensing costs: Create groups that reflect your business needs and goals that align with your respective business sponsors. Sync your licensing status changes with group membership changes. That way, you can assign the right licenses to the right users and adjust easily if you require frequent changes, for example, in your early initial validation phase, to avoid paying for licenses you don’t need or use.

Refine admin costs: Group-based licensing lets your admins assign one or more product licenses to a group. This depends on your rollout strategy and progress. Your admins will be able to streamline your group setup at scale, reducing your admin overhead. This strategy is helpful, considering all the licenses you likely need to manage.

Enhance compliance and security: This ensures that only authorized users receive licenses and get access to resources, enhancing your security and compliance. Your admins can use audit logs and other Microsoft Entra services to monitor and manage your group-based licensing activities.

Pre-adoption communications

Given the excitement around AI tools, one of the biggest challenges during our phased implementation was support requests from employees outside our initial pilot groups. Most of our support requests at this stage were essentially asking, “Where’s my license?” It was a key learning for our Microsoft Digital implementation team.

You can easily avoid the issue through clear and honest communication. For example, when you alert your initial implementation groups about their Copilot access, you could simultaneously deploy “Coming soon” emails to the rest of your organization. That will help you avoid any confusion while simultaneously generating excitement and boosting general adoption when the time comes.

In the end, what’s most important is building a strategy for getting all users access to Copilot, structuring your rollout, and helping people build the daily habit of using AI. While leadership sponsorship is especially important in later phases of adoption, it’s also crucial here as a way of identifying who should be part of pilots and subsequent cohorts. Leaders can help communicate those decisions.

The bottom line is that your IT implementation team can’t work in isolation. Communication—especially from organizational leadership—will be a key part of your licensing and implementation strategy.

Learning from our implementation 

Design for the “who”

When you determine your initial cohorts, base your decisions on which roles have the largest coverage and will provide the most relevant feedback.

Get your groups in place

Be thoughtful about your Microsoft 365 groups and make sure everyone knows who owns them and who’s responsible.

Engage your support team from the start

This is a new technology, so your support teams will receive requests. Ensure they’re ready by giving them early access.

Manage expectations to minimize blowback

Proactively help users understand why they have licenses or don’t. Note that your rollout strategy might be subject to change.

Bring leadership on board early

Executive sponsorship isn’t just useful for adoption. Leaders will also help you identify the key use cases within their organizations to determine if they belong in early rollout phases.

Product feedback at every level

Encourage feedback for employees in your early implementation phases, because that will guide your wider adoption efforts.

Key takeaways

Use these tips to help you with your internal implementation and admin process. They are based on our experience here at Microsoft.

1) Get ready

• Perform the Microsoft 365 Copilot optimization assessment
• Identify key implementation phases and groups
• Secure leadership involvement
• Build out your implementation plan and map it to a licensing strategy

2) Onboard and engage

• Assemble security groups and assign responsibilities
• Build an automated Microsoft 365 licensing management workflow
• Enable roles for Copilot reports and the Copilot dashboard
• Assign licenses and configure them using the setup guide
• Analyze pilot data:
  • Access in-app feedback
  • Facilitate feedback sessions
  • Analyze usage reports
• Deploy communications: For strategy around this element, see the next section

Key actions

How we did it at Microsoft

• Learn about the councils steering our AI projects here at Microsoft.
• Find out how we’re unlocking the value of Microsoft 365 Copilot at Microsoft.

Further guidance for you

• Use the Microsoft Copilot Success Kit to accelerate your implementation with templates, onboarding guides, and technical readiness resources.
• Follow step-by-step instructions for assigning licenses and onboarding users in the Microsoft 365 admin center.

Chapter 3: Driving adoption to capture value

Effective adoption: From readiness to empowerment

The fact that your employees are excited to try out a powerful new technology platform isn’t enough. We found that you need strategic, coordinated change management efforts to drive Microsoft 365 Copilot adoption.

That way, you can be sure to get your employees onboard at the right time in the ways that you want. The idea is to give them the freedom to be themselves with proper guardrails.

Consider breaking your company-wide adoption into cohorts, for example, subsidiaries or business groups. We divided our adoption along two vectors: internal organizations like legal or sales and marketing, and regions like North America or Europe. Different cohorts have different focuses, but the strategy is similar.

Microsoft 365 Copilot change management

Effective change management needs careful planning. Our adoption efforts took inspiration from the Microsoft Engagement Framework, which we’ve developed specially for driving adoption of our products. If you’re an adoption specialist or change manager, you might notice similarities with Prosci’s ADKAR model, which progresses through awareness, desire, knowledge, ability, and reinforcement.

Whichever framework you choose, the techniques we use here at Microsoft will apply. Either way, the process starts with your people.

Get ready

Begin by working with your company-wide adoption leads, then identify members of your target cohorts who will support the adoption, including change managers, leadership sponsors, and employee champions.

Champions boost adoption by filling several important roles:

• Pinpointing key usage scenarios for Copilot based on their cohort’s culture or processes.
• Deciding on the best methods of communication.
• Providing insights that help adoption leaders build out their rollout plans.
• Extending the reach of our adoption team through peer-to-peer support and guidance.
• Most importantly, demonstrating the value of Copilot and showing their peers how powerful this tool can be in their day-to-day work.

When champions socialize their tips and tricks, our experience at Microsoft Digital has revealed that it’s best to share specific prompts and the value they provide as a concrete entry point for users. For example, a champion could say, “I saved three hours drafting this sales script in Microsoft Word using this prompt,” then share their Copilot prompt as a place for peers to start. You’ll find advice below for how you can effectively incorporate champs into your adoption efforts.

Works councils also play a key role at this stage. They offer the benefit of local cultural expertise and can help you identify challenges employees face in their jurisdictions. Even something as simple as understanding proper modes of address helps smooth the road to adoption through effective communication.

Each of these sets of stakeholders has a role to play in your rollout. We recommend using Microsoft Copilot adoption resources to build out your adoption plan.

Onboard and engage

At Microsoft, we implemented this phase across each adoption cohort. Because every group will have its own champions and leadership sponsors, it’s important to treat each of them as its own organization, with its own unique adoption needs.

In advance of our general rollout, we deployed jump-start communications with links to learning opportunities:

• Localized training took the form of Power Hours in different languages and time zones. These training sessions demonstrated key Copilot scenarios across Microsoft 365 apps.
• Self-learn assets included user quick-start guides, demo videos, and the Microsoft Copilot Academy to accommodate different learning styles and preferences.

From our experience at Microsoft Digital, pre-rollout communications fulfill two needs. First, this messaging is a great opportunity to launch your champion communities because early access to Copilot licenses and learning material helps peer leaders build their expertise. Second, these communications build your general adoption population’s desire and excitement for their incoming Copilot licenses, then prepare them to hit the ground running when they finally get access. Clear messaging also helps ward off questions from eager employees asking why they don’t have licenses yet.

After your Copilot licenses are live, your launch-day welcome communications are relatively simple. Just invite employees to access Copilot, play with this new tool, and start to experiment with how it can fit into their daily workflows. It’s also helpful to include information about where employees can get support. There are many possible vectors for deploying these communications, but a multi-pronged effort that includes Microsoft Viva Amplify will deliver the maximum impact.

For support in building out your own communication plan, our adoption team has created a user onboarding kit for Copilot. These ready-to-send emails and community posts can help you onboard and engage your users.

Deliver impact

After everyone has access, it’s time to promote Copilot usage and ensure your employees are getting the best possible experience and the most value. For Microsoft’s cohorts, employee champions and leadership sponsors were essential levers.

It’s important to remember that Copilot isn’t just another tool. It introduces a whole new way of working within employees’ trusted apps. At Microsoft Digital, we took great care to encourage employees to be adventurous and lean into a mindset shift to see it as part of their daily work—not just something they play with when there’s time.

Microsoft Viva Engage or a similar employee communication platform is a helpful forum for peer community support. In our case, it provided an organic space for champions to share their expertise and change managers to provide further recommendations and adoption content. For employees who explore best on their own, Copilot Lab provides in-the-flow learning opportunities to build their prompt skills.

Meanwhile, leadership sponsors diversified our communications strategy by deploying and amplifying messaging through executive channels like org-wide emails or Microsoft Viva Amplify. Because we broke our adoption out by both organization and region, employees benefited from two sets of communications, each focusing on the scenarios that are most relevant to them.

Extend and optimize

Finally, successful adoption depends on measurement, feedback, and listening.

Understanding overall usage patterns and impact is crucial to optimizing adoption. Our Microsoft Digital team employed a combination of controlled feature rollout (CFR) technology while tracking usage through Microsoft 365 Admin Center, the Copilot Dashboard, and Viva Insights. Together, these tools gave us the visibility and tracking we needed to establish and communicate adoption patterns. Meanwhile, IT admins and user experience success managers accessed simple in-app feedback through Microsoft 365 admin center. But to really maximize value, our Microsoft Digital employee experience teams conducted listening sessions and satisfaction surveys.

All of these insights are helping us establish a virtuous cycle to drive further value and better adoption for future rollouts, extend usage to new and high-value scenarios, incorporate Copilot into business process transformation, and understand custom line-of-business opportunities.

Driving user enablement with Microsoft Viva 

We used Microsoft Viva to help enable our 300,000+ global users. Microsoft Viva is an Employee Experience Platform that brings communication and feedback, analytics, goals, and learning into one unified solution. Our team in Microsoft Digital used Viva across a range of change management scenarios, including building awareness, communicating with our employees, providing access to readiness and learning resources, and measuring the impact of our deployment. 

Accelerating Microsoft 365 Copilot with Viva

Viva Connections

Sharing key news related to deployment and enablement, generating “buzz,” and tying Copilot to Microsoft culture.

Viva Amplify

Producing and efficiently distributing employee communications to build awareness and excitement.

Viva Learning

Courses and training for our employees on how to maximize value from Copilot, inclusive of building effective prompts.

Viva Engage

Actively engaging employees, providing leader updates, listening to feedback, and enabling Champs community.

Viva Insights

Using the Microsoft 365 Copilot Dashboard beta to identity actionable insights and usage trends.

Viva Pulse

Instant feedback from employees on their Copilot experience to fine-tune our landing and adoption approach.

Viva Glint

Understanding employee sentiment and gauging the overall effectiveness of our Copilot deployment effort.

Consider these examples:

• A human resources professional might use Copilot to create job descriptions by prompting it to suggest essential skills, qualifications, and responsibilities for a prospective role.
• A salesperson could ask Copilot to generate a table comparing their company’s flagship product with a competitor’s to address customer questions more efficiently.
• A finance professional might prompt Copilot to review and summarize a new contract to reduce the time it takes to search for key data.

Any single approach would never be adequate to address every different discipline and use case. With the rise of agents, specialized AI-powered assistants that customize and focus the capabilities of Copilot, certain roles derive the most value from tailored assistance for specific tasks.

So, we created a playbook that our employees can use to construct their own role-based scenarios according to their individual teams’ unique needs.

We designed it to help adoption professionals accomplish the following objectives:

1. Understand the top responsibilities, challenges, needs, and wants of prioritized roles.
2. Articulate and communicate hero scenarios by clearly depicting how Copilot can enable them.
3. Share deliverables that include roles, scenarios, and prompts with the wider organization to drive awareness, adoption, engagement, and value.

Through internal testing and scenario crafting, we developed a four-part framework for creating, delivering, and socializing hero scenarios across any organization. These are the steps you can follow to create Copilot support content for adoption efforts tailored to specific roles.

Phase 1: Ready

This phase will help your organization, department, or team prepare for the process. It involves aligning with leadership and sponsors who will be accountable for driving value using Copilot. It’s also where you’ll select the priority roles, draft outlines of those roles so you can clarify your understanding of their needs and wants, and seek out feedback from leaders, managers, and subject matter experts.

Phase 2: Engage

Engaging with employees is the key to uncovering Copilot’s core value. In this phase, you’ll identify participants from your priority roles who demonstrate enthusiasm and early aptitude with the tool. From there, you can choose an approach, which might include in-person group sessions, virtual Microsoft Whiteboard sessions, one-on-one interviews, Microsoft 365 Loop collaboration, or whatever modality works best, then communicate the process to participants. Whatever you choose, the final step in this phase is conducting your employee engagements to document existing and aspirational Copilot usage scenarios.

Phase 3: Deliver

Ideating hero scenarios is how you discover value. The delivery phase defines that value and organizes it into a useful, consumable format. It starts with reviewing and analyzing the outcomes of your sessions to gain insights and identify themes. Now is the time to document your hero scenarios and the value they add, as well as blockers and accelerators. Finally, you’ll provide your output: a comprehensive deck that includes your priority roles, hero scenarios, next steps, and more.

Phase 4: Share

The final phase of this process involves socializing your scenarios across your team or organization to realize value. If you’re part of a large organization, it’s helpful to radiate these outputs beyond the target group as an opportunity for further Copilot momentum. This stage includes diving deeper into blockers and accelerators that can help your organization as a whole speed time to value.

Learning from our adoption of Copilot

Cascade adoption efforts through localization

Regional differences, priorities, even time zones—they can all block your centralization efforts. Your insider adoption leaders within each adoption cohort can help.

Empower your employee champions with trust

Monitor your user-led adoption communities at the start to provide support. As this community of power users becomes product experts, they’ll take over.

Empower employees as innovators

You’ll be surprised by what your employees dream up. Provide every opportunity for them to share their favorite tips and usage scenarios.

Create excitement, but set expectations

Encourage a healthy mindset around what Copilot can accomplish and where it fits. Don’t overpromise.

Gamify learning to build engagement and experience

Friendly competitions or cooperative challenges like prompt-a-thons generate excitement and invite creativity.

Understand that for many, AI is emotional

Overcome AI hesitancy by encouraging employees to tackle easy tasks with Copilot assistance. That will help minimize reluctance through practice.

Key takeaways

Use these tips as your guide as you build out and implement your adoption plan. They are based on our own experience internally at Microsoft.

1) Get ready

• Identify and ramp up the person who will lead adoption for your organization
• Create an adoption team and identify who will lead each workstream within each cohort, including:
  • Change managers
  • Executive sponsors
  • Employee champions
• Conduct a kickoff meeting with your adoption team and set up a meeting cadence and workflow
• Identify users and usage within your cohorts:
  • Pinpoint key usage scenarios, for example, CRM-connected email communication for salespeople or customer-facing copy support for marketers
  • Identify cohort-specific personas, for example, software engineers, customer support specialists, and business operations project managers
• Determine communication preferences for each cohort and their personas and optimize messaging for each
• Define success criteria with KPIs and a success measurement plan
  • Examples include usage by app or feature and user sentiment
• Complete user enablement strategy training
• Define a user experience and feedback strategy
• Build deployment communications and an enablement asset library
  • Localize for international audiences

2) Onboard and engage

• Deploy readiness communications with onboarding content:
  • Led by cohort adoption team
  • Led and amplified by leadership sponsors
• Launch champion communities
• Deploy launch communications
  • Led by cohort adoption team
  • Led and amplified by leadership sponsors
• Socialize employee engagement communities
• Run live learning sessions
• Provide self-learning opportunities
• Upscale the working environment with digital banners, posters, and other promotional materials to help employees visualize Copilot

3) Deliver impact

• Promote usage through internal cohort channels
  • Follow-up communications
  • Viva Engage champion posts
• Report on KPI success at predetermined intervals
• Facilitate listening
  • Satisfaction surveys
  • Listening sessions
• Gather and amplify success stories
• Apply learnings to further adoption activities
• Nurture existing champions through a technical training track
• Develop reinforcement, resistance, and maintenance plans

4) Extend and optimize

• Explore new high-value scenarios
• Investigate business process transformation via agents, Copilot Studio, plugins, and connectors
• Source custom line-of-business opportunities

Key actions

How we did it at Microsoft

• Learn how we’re unlocking the value of Copilot at Microsoft. This post shares our overall journey from planning to measuring impact.
• Discover ways we’re approaching change management around Microsoft 365 Copilot. This story identifies the challenges and opportunities around adopting a new and revolutionary technology.
• See how we’re using Microsoft Viva to drive Copilot adoption. This article shares how Microsoft Viva acted as a comprehensive adoption tool during our Copilot rollout.
• Find out how we made Microsoft 365 Copilot adoption fun with Camp Copilot. This post shares how a grassroots, gamified community initiative became one of our most effective adoption drivers. Pair that story with this one that shares our seven tips for driving a successful virtual Copilot skilling event, and check out this article about our three-week Copilot Expo.
• Find out how a Copilot Champs community can help you drive adoption. This post shares how peer-to-peer adoption support helps drive greater engagement for employees.
• Learn how we’re helping our employees build daily Copilot habits with these 10 tips and check out our tips list for engineers.
• Explore how we created a Center of Excellence to guide our adoption and use of AI.
• Learn how we’re applying the Microsoft Responsible AI Standard here in Microsoft Digital and across the company.

Further guidance for you

• Explore the company’s essential guide to Copilot adoption for practical steps to onboard, engage, and measure impact across your organization.
• Use the Microsoft Customer Hub to access community resources and skilling events that can help you build a champions network and foster peer-to-peer support.

Support for adoption leaders

• Access the Copilot scenario library to understand different user needs and opportunities.
• Gain insights from your Microsoft Copilot Viva Insights dashboard.
• Deepen Copilot engagement through the Microsoft Modern Collaboration Architecture (MOCA).
• Help users build daily habits around Copilot using our suite of user engagement tools and templates.

Resources for IT practitioners

• Offer your employees training opportunities through a Microsoft Learn training module.
• Access different Copilot user help and learning resources.
• Give your employees a leg up on creating AI prompts with this blog post.

Chapter 4: Building a foundation for support

Setting your Support team up for success

Empowering employees means making sure they have access to the right support channels, especially if they have concerns with a new technology. The fact that Microsoft 365 Copilot operates across a wide spectrum of Microsoft 365 apps adds complexity to your support apparatus.

As a result, it’s important to give your support teams early access along with your earliest pilot implementations. For Microsoft Digital, that included members of our internal support teams who help Microsoft employees when they run into technical issues, as well as our Customer Experience and Support team that engages with external customers to troubleshoot problems with new Microsoft products. We also invited subject matter experts for Microsoft 365 apps featuring Copilot experiences, including Teams, Outlook, and more.

A small group of users across both internal and external support teams, as well as our Microsoft 365 subject matter experts, gained access at first, and we encouraged them to experiment and try to break features. This was a crucial learning phase for Microsoft Digital because it surfaced interesting issues that wouldn’t come up if our teams didn’t have access and an opportunity to experiment.

Building insights and product experience was step one, but we needed to collect that knowledge so it would be actionable in real situations. To accomplish that, we created a special Teams channel where our support team members collaborate with pilot users of Copilot and representatives of the product group. From there, we worked with marketing and communications professionals to start building our support team’s knowledge base, which would also serve as the foundation for our user-facing content.

Eventually, the time came to provide access to our wider support team. At that point, our support pilot members operated as learning leaders. When it came time to share their knowledge, it took the form of informal brown-bag sessions. We also engaged in shadow/reverse-shadow role-playing exercises so our support agents could practice addressing common issues.

Principles of good support

Available

Highly available and in-context support should deliver both self-help and assisted experiences wherever and however the user needs them.

Consistent

Coherent and seamless access to support across the entire journey eliminates frustration and keeps the experience consistent.

Contextual

Base your support experience on where the user is and what they’re trying to accomplish.

Personal

Anchor support in a user’s goals and preferences.

Strategizing for support

Building experience and knowledge is one thing, but coming up with your approach to support requires planning and a strong idea of your users’ ideal experience. At Microsoft Digital, we take a “shift-left” approach. That means we save our human support staff time by attempting to create excellent self-service options for our users. As a result, they won’t need to access a human agent unless they’re at a genuine impasse.

Shift-left principles can apply to many different support contexts, but with Copilot, we’ve found that the most important upfront action is ensuring your employees have accessible self-service support channels and communicating their availability. That might come through in-app support or access to knowledge bases.

Work with your adoption teams to ensure they include those self-service support vectors in their rollout communications. For us, self-service was able to answer many of our users’ questions, and for any extra-tricky issues, we had them access human-led support.

Seven things we learned preparing our Microsoft 365 Copilot support

Preliminary access

Select your initial support specialists. Include people with different Microsoft 365 app focuses, support tiers, and service audiences.

Communication hub

Establish a community space where your support team can connect and collaborate on issues. Invite non-support professionals as needed.

Knowledge base

Start a collaborative document and add learnings. This will eventually evolve into your knowledge base for internal support.

Widen access

Host information sessions with the wider support team and extend access so all relevant support professionals can ramp up.

Rehearse

Conduct role-playing and shadowing sessions so support teams can build practical knowledge and confidence.

Support go-live

Get your support resources and processes ready and push them live in advance of your Copilot deployment. Consider a dry run.

Track

Determine a tracking cadence and gather data on Copilot issues that arise so support teams can identify trending issues and tickets.

Common questions, issues, and resolutions

As the first enterprise organization to go through the Copilot deployment process, we’ve identified a few challenges and questions you might have. Feel free to add these to your support knowledge base and employee-facing communications.

We’re getting questions about why particular employees don’t have licenses.

Ideally, your adoption communication waves solve this issue by alerting employees when to expect their licenses and when they receive them. Otherwise, consider having a readily available link that answers licensing questions for users or directs them to their relevant managers or admins. You can also automate this process.

Users are coming to us with questions that would be better served by adoption and employee material, and that isn’t our role as support.

Work with your adoption team to preempt these issues with proactive communications. Update your self-help content and provide your support agents with ready access to different employee education resources, including your user-facing knowledge base, self-help videos, and Viva Engage communities focused on Copilot.

Teams are looking for integration support. Where do I send them?

Share this list of pre-built connectors to help your users integrate various data sources into your Microsoft Graph. This list shares the types of content supported.

Can employees put confidential information into Copilot?

As long as your employees are signed in to Copilot with their Entra ID, they can enter confidential information.

My organization has concerns about who owns the IP that Copilot generates. Does the Microsoft Customer Copyright Commitment apply to Copilot?

Microsoft does not own the IP generated by Copilot. Our universal terms state, “Microsoft does not own customers’ output content.” Those terms also include our Customer Copyright Commitment.

What’s the best way to verify the accuracy of the information Copilot provides?

Where possible, Copilot is transparent about where it sources responses from. It answers complex questions by distilling information from multiple web sources into a single response. Copilot provides linked citations to these answers so the user can verify further. 

Key takeaways

Use these tips as your guide as you build out and implement your adoption plan. They are based on our own experience internally at Microsoft.

1) Onboard and engage your support team

• Start with a small set of support leaders:
• General support
• Microsoft 365 product specialists
• Establish a Teams channel for communication and knowledge sharing
• Create a collaborative knowledge base foundation
• Widen access to the full Copilot support team
• Train your full support team:
  • Conduct information sessions
  • Conduct role-playing exercises
• Establish your escalation process
• Engage your internal communications team:
  • Finalize your user-facing knowledge base
  • Discuss the inclusion of knowledge base material and the support process in rollout communications

2) Deliver impact for your users

• Signal support availability in user communities on Viva Engage and other platforms
• Publish your user-facing knowledge base
• Establish self-service automations if applicable

3) Extend and optimize your services

• Review support issues and product feedback
• Calibrate the optimization of your support workflows

Key actions

How we did it at Microsoft

• Read how we’re using AI to transform our Support business at Microsoft. This article shares how our Support team in Microsoft Digital is using AI to overhaul the business from end to end.
• Learn how we deployed the Employee Self-Service Agent here at Microsoft. This blog post shares how we’re using this agent to help our employees with their support needs in the flow of where they work in Copilot.
• Discover ways that AI is revolutionizing support for corporate functions at Microsoft. This post shares an overview of areas where AI can help support teams do their work more effectively.
• See how we’re supercharging our support experience with Microsoft Dynamics 365 and AI. This story outlines ways that AI is boosting our HR team’s support for employees.

Further guidance for you

• Follow the Microsoft IT admin guide for Copilot onboarding, enablement, and support resources to ensure a smooth rollout.
• Access the Copilot Success Kit for launch day assets, support templates, and troubleshooting guides.

Chapter 5: Extending Copilot through agents

Unlocking more tailored experiences by enabling employees and teams to create agents

As organizations and employees have matured with respect to AI, agentic extensibility is expanding the frontiers of this technology. By using and even creating agents that surface knowledge, take actions, and reinvent workflows, employees can personalize AI’s capabilities to fulfill more specific needs.

At Microsoft, we’re leaning into the agentic future by empowering employees and teams to create agents of their own. Agents and their capabilities are incredibly varied. They range from pre-made out-of-the-box agents in Microsoft 365 embedded directly into Copilot Chat; to straightforward agents that employees create themselves using a simplified process also available through Copilot Chat; to Copilot Studio agent builder or SharePoint agent builder; all the way up to complex agents that can take action on behalf of users, designed using tools like Microsoft Copilot Studio and Azure AI Foundry.

Our goal has been to provide access and enable their use at appropriate levels for our employees and the company as a whole. To make that happen, we’ve adopted a maturity model for agentic AI deployment. Early phases focus on using Copilot, grounded in enterprise data, to enhance knowledge discovery and retrieval. Later phases will enable our employees to act on that knowledge and even fully automate business workflows.

Phases of maturity

Each of these levels of agentic capability requires different tools to create and depends on different policies to govern. In the simplest terms, this involves three levels of agent, each of which can handle progressively more complex tasks:

Retrieval agents

Employees use low-code solutions like Copilot Chat or Copilot Studio agent builder, or they can access ready-made agents in Microsoft 365 or SharePoint to quickly train models and retrieve knowledge for specialized scenarios.

Knowledge and action

Powered by built-in connectors in Copilot Studio, agents go beyond simple knowledge retrieval, offering next steps and actions that help employees defragment their day-to-day experience.

Workflow reinvention

Human-led, agent-operated teams perform fully autonomous actions to complete end-to-end workflows, enabling employees to focus on the highest value work while agents take care of repetitive tasks.

While the third level of maturity is still in its initial stages, our employees and teams are already creating retrieval agents and knowledge and action agents. Because retrieval agents don’t require special tooling, we allow employees to create them at will through Copilot Chat and simplified agent builders in Copilot Studio and SharePoint.

For more complex agents intended to meet enterprise needs across lines of business or the company as a whole, our developers use more full-featured tools like Copilot Studio or Azure AI Foundry. For these kinds of agents, we apply the same rigor, reviews, and software development lifecycle (SDL) we use as part of our standard internal app development.

As you explore the different kinds of agents available to your users and decide how and where to enable them, adoption.microsoft.com provides an excellent place to start. It provides three different approaches to creating agents: Microsoft 365 Copilot, Azure AI Foundry, and Copilot Studio. Once you determine who should have access to each of these creation methods, you can follow our advice on driving adoption for this new practice.

Of course, all of this choice adds complexity, so maintaining visibility and control over the agents your employees create can be a challenge. As a result, we take a matrixed approach to creating and governing agents based on different parameters. They include the type of agent, how the user creates it, its knowledge sources, the need for custom tooling, sharing and publishing permissions, and more. It will be helpful to review our strategy in full to help you think through the different parameters behind your agents, in addition to the processes and policies you’ll need to put in place to govern them.

Keeping agents safe and effective through good governance

As you enable your employees and teams to create and use agents, you’ll need structures in place to govern these tools. At Microsoft, we incorporated elements of our tenant’s minimum bar for governance into our policies for managing agents. These measures include Microsoft Information Protection, a functional inventory, activity logging, lifecycle management, and the ability to properly isolate agents against crossing data boundaries.

Our general governance strategy operates at the container level, but agents bring extra functionality to the table. To govern these capabilities, we introduced further controls like sharing limits, breadth of knowledge sources, agent metadata, and information about an agent’s behaviors. The result is a proactive approach to governance backstopped by reactive structures that catch any issues.

As you think about governing your own agents, consider the four core principles we’ve established at Microsoft Digital.

We empower employees to create and share simple, low-risk agents

We provide a safe space and personal flexibility that allows individual employees to experiment without implicating company data or content users don’t own.

We capture and vet sensitive data flows at the enterprise level

More complex or far-reaching agents owned by teams or lines of business need enterprise documentation to account for external audits or security and privacy validation. Builders need to demonstrate that they’ve thought through the security and privacy implications of their agents, so these projects go through approval process flows similar to other professionally developed apps before we trust them with potentially sensitive data.

We protect data designated confidential or higher

We contain data flows to tenant mandates and only trust suitable storage destinations for content. That depends on the ability to gate which connectors can work with particular source data and sensitivity labels.

We honor the enterprise lifecycle 

Both user-based and attestation-based lifecycles come into play. We treat agents that individual employees own like any other user-created app and delete them when that individual leaves the organization. Agents owned by teams have a lifecycle defined by the tenant and tied to attestation, the SDL, and accountability confirmations.

Once you have your governance policies and procedures in place, you can begin your rollout to users through many of the same strategies and processes we’ve discussed in this guide.

Learning from our experience with agents

Connect with relevant stakeholders

Establish early communication and collaboration with members of your security, legal, compliance, IT, and other teams who can help you define ways to configure Copilot Studio agent builder safely.

Trust and empower

Provide safe spaces with appropriate guardrails for individual employees to experiment with simple agents. Copilot Studio agent builder is a great place to start.

Expand enterprise capabilities

Empower a small number of trusted creators to experiment with more powerful agent-building tools under the close watch of IT, Governance, Security, Privacy, Data, and HR teams. This will reveal gaps in process and policy and inform future reviews.

Solidify labeling and data

Revisit your labeling structures and data flows. It will be important to have these structures in place to support this new agentic environment. Start by learning from our experience governing Copilot at Microsoft.

Extend your review process

Adapt any review processes you already have in place to agents, including security, privacy, and accessibility. Embed those reviews into your publishing workflow for agents operating above the individual level. Consider adding reviews for Responsible AI.

Prevent agent sprawl

Establish a reasonable enterprise lifecycle for agents that includes attestation. That will keep agents from sprawling or remaining in place after employees have left your organization or simply no longer need a particular agent.

Key takeaways

Use these tips as your guide as you build out and implement your adoption plan. They are based on our own experience internally at Microsoft.

1) Plan and adapt

• Connect with stakeholders on relevant teams, including Security, Legal, Compliance, HR, and IT.
• Revisit your overall governance and labeling policies and procedures and update them to reflect the needs of agents.
• Plan and document your intended review process.
• Build your matrix of agent capabilities and parameters and map governance policies and procedures to each aspect of agents.
• Decide how your SDL procedures will map to agents.

2) Run pilots with select teams

• Determine your pilot teams. IT and other teams who will be responsible for determining policy are good places to start, for example, Security and HR.
• Establish a feedback and monitoring pipeline.
• Fine-tune your review and remediation procedures based on your learnings.

3) Enable agents across your organization

• Ensure Purview DLP, Microsoft Information Protection, and other backstops are in place before widely enabling agents for users.
• Deploy adoption communications and change management efforts.
• Enable simple agent builder capabilities for your general workforce.
• Enable more complex agent creation for developers on IT and line of business teams.

Key actions

How we did it at Microsoft

• Read about how embracing the agentic future is enabling us to become an AI-first Frontier Firm. This post shares our vision for using agents to transform the workplace.
• Read how good governance is helping us ride the wave of agents washing over Microsoft. This article outlines the governance strategies we’ve adapted to the new agentic moment.
• Find out more about our deployment of Microsoft Agent 365 at Microsoft. This post shows how we’re using this new platform to manage our agent ecosystem. 
• See how we’re unlocking enterprise AI extensibility with Copilot Studio. Read about Microsoft’s internal AI development initiatives and how we’re using and governing Copilot Studio.
• Learn how we’re enabling our employees to build custom retrieval agents. These agents are empowering our workers to accomplish more.
• Discover how we’re keeping our network infrastructure healthy at Microsoft with an employee-built AI agent. This article shares how our team built an agent to improve how we manage our network.
• Access our internal learnings and a step-by-step guide for unleashing API-powered agents at Microsoft. This article explains API agents and shares lessons from our journey using them internally.

Further guidance for you

• Explore the company’s official AI agent adoption guidance to plan, govern, and operate agents using proven frameworks and best practices.
• Visit the AI Agents Hub on Microsoft Adoption to access webinars, quick-start guides, and business scenarios for Copilot, Foundry, and Copilot Studio.
• Follow the Microsoft 365 Copilot Agents Deployment Blueprint to enable agents at scale with security, governance, and measurement strategies.
• Download the Agentic Automation Adoption Guide to learn migration strategies, governance models, and business value for agentic automation.
• 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 we use to guide our use of AI at Microsoft.

Applying our deployment lessons at your company

You’ve learned from our Copilot deployment. It’s time to get started on yours.

Embarking on your Microsoft 365 Copilot deployment journey might seem daunting, but by capitalizing on the lessons that we’ve learned during our internal deployment, you can both speed up the process and avoid any pitfalls.

“Deploying Copilot internally has inspired us to dive deeper into the power of AI assistance, which is enabling us to enhance our employee experience.”

Stephan Kerametlian, business program management senior director, Microsoft Digital

By anchoring your work in careful planning and using the steps and resources provided in this guide, you can unleash a new era of productivity through Copilot.

You’re not in this alone. If you’re looking for support or knowledge on any aspect of your deployment, reach out to our customer success team.

For inspiration around ways that Copilot can become your employees’ AI assistant at work, read stories about how we’re using AI within Microsoft Digital and Microsoft as a whole.

“Deploying Copilot internally has inspired us to dive deeper into the power of AI assistance, which is enabling us to enhance our employee experience,” says Stephan Kerametlian, a business program management senior director within Microsoft Digital. “With the lessons we learned from our deployment, we’re confident that we can support businesses around the world as they achieve more through the next generation of intelligent experiences.”

Key takeaways

This guide reflects our learnings and the processes we followed during our internal rollout of Microsoft 365 Copilot. This last set of tips summarizes the major actions you can take to get started with Copilot at your company.  

• Start with strong governance: Build a clear labeling and data protection strategy before deploying Copilot to safeguard sensitive information and meet compliance needs.
• Pilot, then scale: Roll out Copilot in phases, beginning with pilot groups to gather feedback and refine your approach before expanding companywide.
• Communicate early and often: Proactive communication and leadership sponsorship are essential for managing expectations and driving successful adoption.
• Empower champions: Identify and enable employee champions to share best practices, tips, and real-world scenarios that help others get value from Copilot.
• Invest in training: Provide tailored learning resources and support to help users build confidence and skills with Copilot in their daily workflows.
• Measure and optimize: Track usage, collect feedback, and continuously refine your deployment to maximize impact and uncover new opportunities.
• Plan for support: Set up self-service and human support channels early so employees can get help quickly and keep momentum going.
• Extend with agents: As your organization matures, explore agentic AI to automate workflows and unlock even greater productivity gains.

Key actions

How we did it at Microsoft

• Read our IT playbook for becoming a Frontier Firm in the era of AI.
• Review our five-step AI maturity guide for IT leaders.
• Use our detailed Copilot governance readiness guide—based on our internal experience here at Microsoft—to manage the governance of your Copilot deployment.
• Learn how our IT organization is powering our companywide digital transformation.
• View our condensed, CIO version of this guide.

Further guidance for you

• Use this technical readiness guide to manage your Copilot deployment (PowerPoint download).
• Check out this content map to help you organize your Copilot deployment (PowerPoint download).
• Find the best overall Copilot deployment and adoption guidance at adoption.microsoft.com.

Try it out

• Learn how to get ready for Microsoft 365 Copilot at your company.

The post Deploying Microsoft 365 Copilot in five chapters appeared first on Inside Track.

Generative AI has captured the world’s attention, and businesses are taking notice.

According to our annual Microsoft Work Trends report, 70% of people would delegate as much work as possible to AI to lessen their workloads.

Capitalizing on this trend will mean the difference between surging ahead or getting left behind, including here at Microsoft, where we were the first enterprise to fully deploy Microsoft 365 Copilot.

“I’m inspired by the transformative power of AI,” says Andrew Osten, general manager of Business Operations and Programs in Microsoft Digital, the company’s IT organization. “I’ve been impressed with how quickly our employees have put it to work for them.”

He would know. His team is responsible for driving usage and adoption of Copilot and any new features to more than 300,000 employees and vendors across the world.

“Customers are looking to us to share what we’ve learned as the first enterprise to deploy Copilot. Our team has a unique opportunity to help them deploy and get to value as quickly as possible.”

Andrew Osten, general manager, Microsoft Digital

Our mission in Microsoft Digital is to empower, enable, and transform the company’s digital employee experience across devices, applications, and infrastructure. We provide a blueprint for our customers to follow as Customer Zero for the company, and as such, we’ve created this guide for deploying and adopting Microsoft 365 Copilot that’s based on our experience here at Microsoft.

“Customers are looking to us to share what we’ve learned as the first enterprise to deploy Copilot,” Osten says. “Our team has a unique opportunity to help them deploy and get to value as quickly as possible.”

Are you an IT pro?

View the full version of this deployment and adoption guide here.

Chapter 1: Getting your governance right

Before you even begin your Microsoft 365 Copilot implementation, you’ll want to consider how this tool impacts your data. Copilot uses Large Language Models (LLMs) that interact with data and content across your organization and uses information your employees can access to transform user prompts into personalized, relevant, and actionable responses.

Giving your employees this level of access means proper data hygiene is a priority. At Microsoft Digital, we use sensitivity labeling to empower our employees with access while also protecting our data. Microsoft 365 Copilot was designed to respect labels, permissions, and rights management service (RMS) protections that block content extraction on relevant file labels. That ensures private or confidential information stays that way.

This chapter outlines the highly robust, best-case scenario we created for Microsoft, but we know not every organization has a fully deployed data governance strategy. If you’re in that position, don’t worry! You can use Restricted SharePoint Search to provide instant value and protection without exposing Copilot to all of your internal SharePoint sites.

Laying the groundwork with proper labeling

We’ve developed four data labeling practices that make up our foundation for appropriate policies and settings.

Responsible self-service

Enable your employees to create new workspaces like SharePoint sites, ensuring your company data is on your Microsoft 365 tenant. That enables your people to take full advantage of Copilot in ways that align with your organizational data hygiene while you keep your company’s information safe.

Top-down defaults

Label containers for data segmentation by default to ensure your information isn’t overexposed. At Microsoft, we default our container labels to “Confidential\Internal Only.” We use Microsoft Purview to manage this process.

Consistency within containers

Derive file labels from their parent containers. Consistency boosts security and reduces the administrative burden on your employees for labeling every file they create. Copilot will reflect file labels in chat responses so employees know the level of confidentiality of each portion of AI-created responses.

Employee awareness

We train our employees to understand how to handle and label sensitive data. By making your employees active participants in your data hygiene strategy, you increase accuracy and improve your security posture.

Self-service with guardrails

The data hygiene practices above form a foundation for compliance and security, but backstopping those efforts through Microsoft 365 features adds an extra layer of protection. Here’s how:

Trust, but verify
Empower self-service with sensitivity labels, but verify by checking against data loss prevention standards, then use auto-labeling and quarantining when necessary. We’ve configured Microsoft Purview Data Loss Prevention to detect and control sensitive content automatically.

Expiry and attestation
Put strong lifecycle management protocols in place that require your employees to attest containers to keep them from expiring. We don’t keep items that don’t have an accountable employee or that might not be necessary for our work.

Controlling the flow
Limit oversharing at the source by enabling company-shareable links instead of forcing employees to grant access to large groups. To enforce these behaviors, you can set default link types based on labels through Purview.

Oversharing detection
Even under the best circumstances, accidents happen. When one of our employees does overshare sensitive data, we use Microsoft Graph Data Connect extraction in conjunction with Microsoft Purview to catch and report oversharing.

International compliance: No size fits all

Europe has extra requirements in the form of EU Data Boundary regulations and works councils, organizations that provide employee co-determination on workers’ rights or regulatory issues. Our Microsoft 365 Copilot deployment meant we needed to partner closely with our Microsoft works councils to address complex data and privacy implications.

Your experience will vary depending on your industry and where you operate, but we’ve learned that it’s best to work closely with local subsidiaries to ensure you have a complete picture of a region’s regulatory situation. Local insiders are poised to liaise with works councils or other bodies through direct relationships. Start the process early so you can manage feedback cycles effectively and resolve any concerns through configurations that work for your employees.

Learning from our governance, security, and compliance practices

Bring the right people into the conversation

Don’t keep this conversation in the IT sphere alone. Bring in all the relevant security, legal, and compliance professionals.

Build a foundation for automation

Microsoft Purview Data Loss Prevention has powerful intelligent detection, but it relies on establishing good defaults.

Think about how your employees will use Copilot

Determine the primary use cases. The kinds of collaboration and access employees need will affect your labeling architecture.

Take this opportunity to train employees

If you’ve been looking for an excuse to refresh employee knowledge around data privacy, let this moment be your milestone.

Don’t overwhelm your users

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

Key takeaways

Use these tips to tackle governance, security, and compliance at your company. It’s based on what we learned deploying Copilot internally here at Microsoft.

• Establish a clear labeling framework that defines classification levels, maps labels to the right policies (such as access control, encryption, DLP, and storage rules), sets container defaults, and ensures employees understand how to apply labels correctly.
• Implement comprehensive data loss prevention controls by configuring Microsoft Purview DLP standards and quarantines, defining lifecycle and attestation processes, and using Microsoft Graph Data Connect to identify and remediate oversharing.
• Engage globally to meet international compliance needs by partnering with local subsidiaries and works councils, addressing regional requirements and concerns, and determining where segmented or region‑specific deployments are necessary.

Key actions

How we did it at Microsoft

• Read our guide to governing Microsoft 365 Copilot. This step-by-step resource, based on our internal experience at Microsoft, will help you adapt your existing governance standards to reflect the new realities of AI.

Further guidance for you

• Conduct the Microsoft 365 Copilot Optimization Assessment to understand your level of AI adoption and chart your course.

Chapter 2: Implementation with intention

At the time of our deployment, we were the first company to roll out Microsoft 365 Copilot and agents at scale, and our implementation team had to choose from different licensing strategies. We’ve learned from experience that it makes sense to start with pilot groups who can validate the experience and enable the rest of your organization. For us, that looked like:

Phase zero

We provided early access to our Copilot engineering team to test-drive the tool from an insider’s perspective. 

• Product engineers

Phase one

We issued a limited number of on-demand licenses in core scenarios to get validation and feedback for key use cases.

• Sales
• Marketing

Phase two

We extended licenses to teams who needed to understand and support Copilot or provide approval for use.

• Support
• HR
• Legal
• Security
• Works councils

Phase three

We deployed Copilot and started working on adoption across the entire company.

• Advancing in phases, we deployed Copilot to more than 300,000 Microsoft employees and external staff.

Scaling out your licenses

After you decide on the general shape of your rollout, you can begin building your licensing strategy. In Microsoft Digital, we started with individual licenses at the single-user level. As our implementation scaled, we tied licensing automation to Microsoft 365 groups to implement targeted licensing changes at scale. Those groups could include subsets of employees or entire organizations within Microsoft, and we keyed our automation logic to their expanding and contracting eligibility.

We highly recommend defining a phased rollout strategy and structuring your groups accordingly. That creates accountability and gives your IT admins a crucial point of contact for understanding the licensing needs of different groups within your organization.

There are three primary benefits to using groups:

Optimize licensing costs: Create groups that reflect your business needs and goals that align with your respective business sponsors. Sync your licensing status changes with your group membership changes. That way, you can assign the right licenses to the right users and adjust easily if you require frequent changes (e.g., in your early initial validation phase) and avoid paying for licenses you don’t need or use.

Refine admin costs: Group-based licensing enables your admins to assign one or more product licenses to a group. This depends on your rollout strategy and progress—your admins will be able to streamline your group setup at scale, reducing your admin overhead, which is helpful considering all the licenses you likely need to manage.

Enhance compliance and security: This ensures that only authorized users are licensed and have access to resources, enhancing your security and compliance. Your admins can use audit logs and other Microsoft Entra services to monitor and manage your group-based licensing activities.

Pre-adoption communications

Given the excitement around AI, one of the biggest challenges during our phased implementation was support requests from employees not within our initial pilot groups. Most of our support requests at this stage were essentially asking, “When do I get access?”

You can easily avoid the issue through clear and honest communication. For example, when you alert your initial implementation groups about their Copilot access, you could simultaneously deploy “Coming soon” emails to the rest of your organization. That will help you avoid any confusion while simultaneously generating excitement.

Your IT implementation team can’t work in isolation. Communication, especially with organizational leadership, is a key part of your licensing and implementation strategy.

Learning from our implementation

Design for the “who”

When you determine your initial cohorts, base your decisions on which roles have the largest coverage and will provide the most relevant feedback.

Get your groups in place

Be thoughtful about your Microsoft 365 groups and make sure everyone knows who owns them and who’s responsible.

Engage your support team from the start

This is a new technology, so your support teams will receive requests. Ensure they’re ready by giving them early access.

Manage expectations to minimize blowback

Proactively help users understand why they have licenses or don’t. Note that your rollout strategy might be subject to change.

Bring leadership on board early

Executive sponsorship isn’t just useful for adoption. Leaders will also help you identify the key use cases within their organizations.

Product feedback at every level

Encourage feedback for employees in your early implementation phases because that will guide your wider adoption efforts.

Key takeaways

Use these tips to help you with your internal implementation and admin process. They are based on our experience here at Microsoft.

• Prepare your organization for Copilot by performing the Microsoft 365 Copilot optimization assessment, defining implementation phases and audience groups, securing leadership sponsorship, and mapping your rollout plan to a clear licensing strategy.
• Onboard users and activate your environment by assembling the right security groups, building an automated licensing workflow, enabling roles for Copilot reports and dashboards, assigning and configuring licenses, and gathering early signals from pilot usage and feedback.
• Drive engagement through targeted communication by analyzing in‑app and qualitative pilot feedback, reviewing usage data, and delivering clear, ongoing communications aligned with your adoption strategy.

Key actions

How we did it at Microsoft

• Learn about the councils steering our AI projects here at Microsoft.
• Find out how we’re unlocking the value of Microsoft 365 Copilot at Microsoft.

Further guidance for you

• Use the Copilot Success Kit to accelerate your implementation with templates, onboarding guides, and technical readiness resources.

Chapter 3: Driving adoption to accelerate value

The fact that your employees are excited about trying out Copilot isn’t enough. We found that you need strategic, coordinated change management to drive usage and adoption.

To do this effectively, you will need to empower change agents in your organization. These are not part-time roles; they are dedicated resources across your company who are responsible for the change management function, including creation of a deployment and adoption plan, facilitating principled change management practices, communicating and engaging with employees, preparing employee readiness and learning opportunities, and then measuring the success of your deployment across the enterprise. At a high level, your strategy should consist of the following five steps.

Microsoft 365 Copilot change management

How we drove adoption in Microsoft Digital

At Microsoft, we broke our company-wide adoption efforts into cohorts, for example, subsidiaries or business groups. Depending on the size of your enterprise, you may benefit from this approach as well. We divided our adoption along two vectors: internal organizations like legal or sales and marketing, and regions like North America or Europe. Different cohorts have different focuses, but the strategy is similar. At Microsoft, we did this in four phases:

Get ready

Effective change management requires careful planning. Begin by identifying and then working with company-wide change management leads. Next, identify members of your target cohorts who will support the adoption, including change managers, leadership sponsors, and employee champions.

Champions will be crucial to your adoption by filling several powerful roles:

• Pinpointing key usage scenarios for Copilot based on their cohort’s culture or processes.
• Providing insights that help adoption leaders build out their rollout plans.
• Most importantly, demonstrating the value of Copilot and showing their peers how powerful this tool can be in their day-to-day work.

When champions socialize their tips and tricks, our experience at Microsoft Digital has revealed that it’s best to share specific prompts and the value they provided as a concrete entry point for users. For example, a champion could say, “I saved three hours drafting this sales script in Microsoft Word using this prompt,” then share their Copilot prompt as a place for peers to start.

Works councils also play a key role at this stage. They offer the benefit of local cultural expertise and can help you identify the challenges employees face in their jurisdiction. Even something as simple as understanding proper modes of address helps smooth the road to adoption through effective communication.

Each of these sets of stakeholders has a role to play in leading your own rollout. We recommend using Microsoft 365 Copilot adoption resources to build out your own adoption plan.

Onboard and engage

At Microsoft, we implemented this phase across each adoption cohort. Because every group will have its own champions and leadership sponsors, it’s important to treat each of them as its own organization, with its own unique adoption needs.

In advance of our general rollout, we created “jump-start” communications with links to learning opportunities:

Localized training took the form of Power Hours in different languages and time zones. These training sessions demonstrated key Copilot scenarios across Microsoft 365 apps.

Self-learn assets included user quick-start guides, demo videos, and Microsoft Viva Learning modules to accommodate different learning styles and preferences.

Pre-rollout communications fulfill two needs. First, this messaging is a great opportunity to launch your champion communities. Second, these communications build your employee population’s desire and excitement for their incoming Copilot licenses, then prepare them to hit the ground running when they get access.

After your Copilot licenses are live, your launch-day welcome comms are straightforward. Invite employees to access Copilot and to start experimenting with how it can fit into their work. There are many possible vectors for deploying these communications, but a multi-pronged effort that includes Microsoft Viva Amplify will deliver the maximum impact.

For support in building out your own communication plan, our adoption team has created a user onboarding kit for Copilot. These ready-to-send emails and community posts can help you onboard and engage your users.

Deliver impact

After everyone has access, it’s time to promote Copilot usage and ensure all employees are having the best possible experience and gaining the most value. For our cohorts, employee champions and leadership sponsors were essential levers.

It’s important to remember that Copilot isn’t just another tool. It introduces a whole new way of working within employees’ trusted apps. At Microsoft, we took great care to encourage employees to adapt a mindset to see it as part of their daily work—not just something they play with when there’s time.

Microsoft Viva Engage, or a similar employee communication platform, is a helpful forum for peer community support. In our case, it provided an organic space for champions to share their expertise and change managers to provide further recommendations and adoption content. For employees who explore best on their own, Copilot Lab provides in-the-flow learning opportunities to build their prompt skills.

Meanwhile, leadership sponsors diversified our communications strategy by deploying and amplifying messaging through executive channels like org-wide emails or Viva Engage Leadership Corner posts.

Extend and optimize

Understanding overall usage patterns and impact is crucial to optimizing usage. Our Microsoft Digital team used a combination of controlled feature rollout (CFR) technology while tracking usage through Microsoft 365 admin center and the Copilot Dashboard in Viva Insights. Together, these tools gave us the visibility and tracking we needed to establish and communicate adoption patterns.

Meanwhile, IT admins and user experience success managers can access simple in-app feedback through Microsoft 365 admin center. And to really maximize value, our Microsoft Digital employee experience teams conducted listening sessions and satisfaction surveys.

All these insights are helping us establish a virtuous cycle to drive further value and better adoption for future rollouts, extend usage to new and high-value scenarios, incorporate Copilot into business process transformation, and understand custom line-of-business opportunities.

Driving user enablement with Microsoft Viva

Our team in Microsoft Digital used Microsoft Viva to help enable our 300,000-plus global users. Microsoft Viva is an Employee Experience Platform that brings together communication and feedback, analytics, goals, and learning in one unified solution. Our team used Viva across a range of change management scenarios, including building awareness, communicating with our employees, providing access to readiness and learning resources, and measuring the impact of our deployment.

You can see a few of the specific ways we used Viva to accelerate employee adoption below.

Accelerating Microsoft 365 Copilot with Viva

Viva Connections

Sharing key news related to deployment and enablement, generating “buzz,” and tying Copilot to Microsoft culture.

Viva Amplify

Producing and efficiently distributing employee communications to build awareness and excitement.

Viva Learning

Courses and training for our employees on how to maximize value from Copilot, inclusive of building effective prompts.

Viva Engage

Actively engaging employees, providing leader updates, listening to feedback, and enabling Champs community.

Viva Insights

Using the Microsoft 365 Copilot Dashboard beta to identity actionable insights and usage trends.

Viva Pulse

Instant feedback from employees on their Copilot experience to fine-tune our landing and adoption approach.

Viva Glint

Understanding employee sentiment and gauging the overall effectiveness of our Copilot deployment effort.

Learning from our adoption of Copilot

Cascade adoption efforts through localization

Regional differences, priorities, even time zones—they can all block your centralization efforts. Your insider adoption leaders within each adoption cohort can help.

Empower your employee champions with trust

Monitor your user-led adoption communities at the start to provide support. As this community of power users becomes product experts, they’ll take over.

Empower employees as innovators

You’ll be surprised by what your employees dream up. Provide every opportunity for them to share their favorite tips and usage scenarios.

Create excitement, but set expectations

Encourage a healthy mindset around what Copilot can accomplish and where it fits. Don’t overpromise.

Gamify learning to build engagement and experience

Friendly competitions or cooperative challenges like prompt-a-thons generate excitement and invite creativity.

Understand that for many, AI is emotional

Overcome AI hesitancy by encouraging employees to tackle easy tasks with Copilot assistance. That will help minimize reluctance.

Use Microsoft Viva to accelerate time to value

Viva supports user enablement through learning, effective communication, usage tracking, and employee sentiment.

Key takeaways

Use these tips as your guide as you build out and implement your adoption plan. They are based on our own experience internally at Microsoft.

• Prepare your organization for adoption by identifying your adoption lead, building a cross-functional cohort-based team, defining personas and key usage scenarios, establishing communication preferences and success metrics, completing enablement training, and creating a localized communications and asset library.
• Engage your cohorts and activate readiness by deploying targeted onboarding communications, launching champion communities, running live and self-paced learning experiences, and elevating visibility with digital materials that help employees understand how Copilot improves their daily work.
• Drive measurable impact across cohorts by promoting usage through internal channels, reporting on KPIs at planned intervals, gathering employee sentiment through surveys and listening sessions, spotlighting success stories, applying learnings to refine adoption activities, and nurturing champions through deeper technical training.
• Extend and optimize your deployment by exploring new high‑value scenarios, identifying opportunities for business process transformation with agents, Copilot Studio, plugins, and connectors, and sourcing custom line‑of‑business use cases that advance your organization’s Copilot maturity.

Key actions

How we did it at Microsoft

• Learn how we boosted our Copilot adoption with a companywide expo.
• Explore how we created a Center of Excellence to guide our adoption and use of AI.

Further guidance for you

• Access the Copilot scenario library to understand different user needs and opportunities.
• Deepen Copilot engagement through the Microsoft Modern Collaboration Architecture (MOCA).

Chapter 4: Building a foundation for support

Empowering employees means making sure they have access to the right support channels. The fact that Copilot operates across a wide spectrum of Microsoft 365 apps adds complexity to support scenarios. As a result, it’s important to get your support teams early access along with your earliest pilot implementations.

For us in Microsoft Digital, four principles define high-quality support:

Available

Highly available and in-context support should deliver both self-help and assisted experiences wherever and however the user needs it.

Consistent

Coherent and seamless access to support across the entire journey eliminates frustration and keeps the experience consistent.

Contextual

Base your support experience on where the user is and what they’re trying to accomplish.

Personal

Anchor support in a user’s goals and preferences.

Strategizing for support

Building experience and knowledge is one thing, but coming up with your approach to support requires planning and a strong idea of your users’ ideal experience. At Microsoft Digital, we take a “shift-left” approach. That means we save our human support staff time by attempting to create excellent self-service options for our users.

Shift-left principles can apply to many different support contexts, but with Copilot, we’ve found that the most important upfront action is ensuring your employees have accessible self-service support channels and communicating their availability. Work with your adoption teams to ensure they include self-service support options in their rollout communications.

Seven things we learned prepping to support Microsoft 365 Copilot

Preliminary access

Select your initial support specialists. Include people with different Microsoft 365 app focuses, support tiers, and service audiences.

Communication hub

Establish a community space where your support team can connect and collaborate on issues. Invite non-support professionals as needed.

Knowledge base

Start a collaborative document and add learnings. This will eventually evolve into your knowledge base for internal support.

Widen access

Host information sessions with the wider support team and extend access so all relevant support professionals can ramp up.

Rehearse

Conduct role-playing and shadowing sessions so support teams can build practical knowledge and confidence.

Support go-live

Get your support resources and processes ready and push them live in advance of your Copilot deployment. Consider a dry run.

Track

Determine a tracking cadence and gather data on Copilot issues that arise so support teams can identify trending issues and tickets.

Common questions, issues, and resolutions

We’re getting questions about why particular employees don’t have licenses.

Use employee change management communication waves to solve for this issue by alerting employees when they’ll have access to licenses.

Users are coming to us with questions that would be better served by adoption and employee material, and that isn’t our role as support.

Work with your adoption team to preempt these issues with proactive communications. Update your self-help content and provide your support agents with ready access to different employee education resources.

Teams are looking for integration support. Where do I send them?

Share this list of pre-built connectors to help your users integrate various data sources to Microsoft Graph. This list shares the types of content supported.

Can employees put confidential information into Copilot?

If employees are signed into Copilot with their Entra ID, they can enter confidential information.

My organization has concerns about who owns the IP that Copilot generates. Does the Microsoft Customer Copyright Commitment apply to Copilot?

Microsoft does not own the IP generated by Copilot. Our universal terms state “Microsoft does not own customers’ output content.”

What’s the best way to verify the accuracy of the information Copilot provides?

Copilot is transparent about where it sources responses. It provides linked citations to these answers so the user can verify further.

Key takeaways

Use these tips to manage your Copilot support efforts. They are based on our experience here at Microsoft.

• Enable and align your support team by starting with a core group of support leaders, establishing shared communication spaces and a collaborative knowledge base, expanding access to the full Copilot support team, training them through information sessions and role‑playing exercises, defining escalation paths, and partnering with internal communications to finalize user‑facing support materials.
• Deliver meaningful user impact by signaling support availability across employee communities, publishing a clear and accessible user-facing knowledge base, and standing up self-service automations where appropriate to empower users and reduce friction.
• Optimize and mature your support services by reviewing ongoing support issues and product feedback, and continually refining support workflows to drive efficiency, accuracy, and a better user experience.

Key actions

How we did it at Microsoft

• Read how we’re using AI to transform our Support business at Microsoft. This article shares how our Support team in Microsoft Digital is using AI to overhaul the business from end to end.
• Learn how we deployed the Employee Self-Service Agent here at Microsoft. This blog post shares how we’re using this agent to help our employees with their support needs in the flow of where they work in Copilot.

Further guidance for you

• Follow the Microsoft IT admin guide for Copilot onboarding, enablement, and support resources to ensure a smooth rollout.
• Access the Copilot Success Kit for launch day assets, support templates, and troubleshooting guides.

Chapter 5: Extending Copilot through agents

As organizations and employees have matured with respect to AI, agentic extensibility is expanding the frontiers of this technology. By using and even creating agents that surface knowledge, take actions, and reinvent workflows, employees can personalize AI’s capabilities to fulfill more specific needs.

At Microsoft, our goal has been to provide access and enable agents at appropriate levels for our employees and the company as a whole. To make that happen, we’ve adopted a maturity model for agentic AI deployment. Early phases focus on using Copilot, grounded in enterprise data, to enhance knowledge discovery and retrieval. Later phases will enable our employees to act on that knowledge and even fully automate business workflows.

Agentic AI at Microsoft

Each of these levels of agentic capability requires different tools to create and depends on different policies to govern. Because retrieval agents don’t require special tooling, we allow employees to create them at will through Copilot Chat and simplified agent builders in Copilot Studio and SharePoint.

For more complex agents intended to meet enterprise needs across lines of business or the company as a whole, our developers use more full-featured tools like Copilot Studio or Azure AI Foundry. For these kinds of agents, we apply the same rigor, reviews, and software development lifecycle (SDL) we use as part of our standard internal app development.

As you explore the different kinds of agents available to your users and decide how and where to enable them, adoption.microsoft.com provides an excellent place to start. It provides three different approaches to creating agents: Microsoft 365 Copilot Chat, Azure AI Foundry, and Copilot Studio.

All of this choice adds complexity, so maintaining visibility and control over the agents your employees create can be a challenge. As a result, we take a matrixed approach to creating and governing agents based on different parameters. They include the type of agent, how the user creates it, its knowledge sources, the need for custom tooling, sharing and publishing permissions, and more.

Keeping agents safe and effective through good governance

At Microsoft, we incorporated elements of our tenant’s minimum bar for governance into our policies for managing agents. These measures include Microsoft Information Protection, a functional inventory, activity logging, lifecycle management, and the ability to properly isolate agents against crossing data boundaries.

To govern agentic capabilities, we introduced further controls like sharing limits, breadth of knowledge sources, agent metadata, and information about an agent’s behaviors. The result is a proactive approach to governance backstopped by reactive structures that catch any issues.

As you think about governing your own agents, consider the four core principles we’ve established at Microsoft Digital.

We empower employees to create and share simple, low-risk agents

 We provide a safe space and personal flexibility that allows individual employees to experiment without implicating company data or content users don’t own.

We capture and vet sensitive data flows at the enterprise level 

More complex or far-reaching agents owned by teams or lines of business need enterprise documentation to account for external audits or security and privacy validation.

We protect data designated confidential or higher 

We contain data flows to tenant mandates and only trust suitable storage destinations for content.

We honor the enterprise lifecycle 

We treat agents that individual employees own like any other user-created app and delete them when that individual leaves the organization. Agents owned by teams have a lifecycle defined by the tenant and tied to attestation, the SDL, and accountability confirmations.

Once you have your governance policies and procedures in place, you can begin your rollout to users through many of the same strategies and processes we’ve discussed in this guide.

Learning from our experience with agents

Connect with relevant stakeholders

Establish early communication and collaboration with members of your security, legal, compliance, IT, and other teams who can help you define ways to configure Copilot Studio agent builder safely.

Trust and empower

Provide safe spaces with appropriate guardrails for individual employees to experiment with simple agents. Copilot Studio agent builder is a great place to start.

Expand enterprise capabilities

Empower a small number of trusted creators to experiment with more powerful agent-building tools under the close watch of IT, Governance, Security, Privacy, Data, and HR teams. This will reveal gaps in process and policy and inform future reviews.

Solidify labeling and data

Revisit your labeling structures and data flows. It will be important to have these structures in place to support this new agentic environment. Start by learning from our experience governing Copilot at Microsoft.

Extend your review process

Adapt any review processes you already have in place to agents, including security, privacy, and accessibility. Embed those reviews into your publishing workflow for agents operating above the individual level. Consider adding reviews for Responsible AI.

Prevent agent sprawl

Establish a reasonable enterprise lifecycle for agents that includes attestation. That will keep agents from sprawling or remaining in place after employees have left your organization or simply no longer need a particular agent.

Key takeaways

Use these tips to manage your Copilot support efforts. They are based on our experience here at Microsoft.

• Plan and refine your governance approach by aligning with Security, Legal, Compliance, HR, and IT; updating existing governance and labeling policies for agents; defining your review process; building a matrix that maps agent capabilities to governance controls; and determining how your SDL procedures apply to agents.
• Pilot with targeted teams to validate your controls by selecting groups such as Security, HR, and IT; establishing clear feedback and monitoring channels; and iterating on your review and remediation procedures based on insights from early adopters.
• Enable agents responsibly across the organization by ensuring foundational protections like Purview DLP and Microsoft Information Protection are in place, deploying adoption and change‑management communications, enabling simple agent‑builder capabilities for broad users, and unlocking advanced agent development scenarios for IT and line‑of‑business developers.

Key actions

How we did it at Microsoft

• Read about how embracing the agentic future is enabling us to become an AI-first Frontier Firm. This article shares our vision for using agents to transform the workplace.
• Find out more about our deployment of Microsoft Agent 365 at Microsoft. This post shows how we’re using this new platform to manage our agent ecosystem. 

Further guidance for you

• Follow the Microsoft 365 Copilot Agents Deployment Blueprint to enable agents at scale with security, governance, and measurement strategies.
• Learn about the Responsible AI policies and practices that we use to guide our use of AI at Microsoft.

Applying our lessons to your own Copilot deployment

Embarking on your Microsoft 365 Copilot deployment and agentic extensibility journey might seem daunting, but by capitalizing on the lessons that Microsoft Digital has learned from our internal deployment, you can both speed up the process and avoid any pitfalls.

“Deploying Copilot internally has inspired us to dive deeper into the power of AI assistance, which is enabling us to enhance our employee experience.”

Stephan Kerametlian, business program management senior director, Microsoft Digital

By anchoring your work in careful planning and making use of the steps and resources provided in this guide, you can unleash a new era of productivity through Copilot.

We’ve learned a lot on our journey with Copilot, and we’re happy that we get to share our experiences with you—hopefully they help you on your journey.

“Deploying Copilot internally has inspired us to dive deeper into the power of AI assistance, which is enabling us to enhance our employee experience,” says Stephan Kerametlian, a business program management senior director in Microsoft Digital.

You’re not in this alone. If you’re looking for support or knowledge on any aspect of your deployment, reach out to our customer success team.

Key takeaways

This guide reflects our learnings and the processes we followed during our internal rollout of Microsoft 365 Copilot. This last set of tips summarizes the major actions you can take to get started with Copilot at your company. 

• Start with strong governance: Build a clear labeling and data protection strategy before deploying Copilot to safeguard sensitive information and meet compliance needs.
• Pilot, then scale: Roll out Copilot in phases, beginning with pilot groups to gather feedback and refine your approach before expanding companywide.
• Communicate early and often: Proactive communication and leadership sponsorship are essential for managing expectations and driving successful adoption.
• Empower champions: Identify and enable employee champions to share best practices, tips, and real-world scenarios that help others get value from Copilot.
• Invest in training: Provide tailored learning resources and support to help users build confidence and skills with Copilot in their daily workflows.
• Measure and optimize: Track usage, collect feedback, and continuously refine your deployment to maximize impact and uncover new opportunities.
• Plan for support: Set up self-service and human support channels early so employees can get help quickly and keep momentum going.
• Extend with agents: As your organization matures, explore agentic AI to automate workflows and unlock even greater productivity gains.

Key actions

How we did it at Microsoft

• Read our IT playbook for becoming a Frontier Firm in the era of AI.
• Review our five-step AI maturity guide for IT leaders.
• Learn how our IT organization is powering our companywide digital transformation.
• View the full, detailed version of this guide.

Further guidance for you

• Read the Microsoft Work Trend Index Annual Report.
• Find the best overall Copilot deployment and adoption guidance at adoption.microsoft.com.

Try it out

• Learn how to get ready for Microsoft 365 Copilot at your company.

The post Microsoft 365 Copilot for executives: Sharing our deployment and adoption journey at Microsoft appeared first on Inside Track.

At Microsoft Digital, the company’s IT organization, we’ve been on the front lines of this AI-powered revolution, translating vision into reality and reimagining what’s possible for the enterprise.

“We’ve learned so many lessons over the past few years building AI-powered solutions and driving an AI-forward culture. We’re excited to share them with our customers and partners so they can learn from our journey.”

Brian Fielder, vice president, Microsoft Digital

As generative AI leapt into the mainstream with the arrival of models like OpenAI’s GPT-3.5 and transformative tools such as Microsoft 365 Copilot, the stakes for IT leaders have never been higher.

The challenge isn’t just about deploying the latest AI tools—it’s about architecting a foundation for sustained, responsible, and scalable change across the enterprise.

That’s where this guide comes in. We’re opening a window into our own AI evolution—sharing our hard-won lessons, proven frameworks, and actionable steps that can help you steer your organization from AI exploration to AI acceleration. Whether you’re just beginning your journey or ready to scale enterprise-wide adoption, this guide is built to empower you to make informed decisions, sidestep common pitfalls, and unlock the full promise of AI-driven transformation.

“We’ve learned so many lessons over the past few years building AI-powered solutions and driving an AI-forward culture,” says Brian Fielder, vice president of Microsoft Digital. “We’re excited to share them with our customers and partners so they can learn from our journey.”

Read on to discover how we moved from AI vision to AI reality here in Microsoft Digital. You’ll learn how you can drive measurable business outcomes while building a culture that’s ready for what’s next.

The five stages of AI-powered transformation

We have led Microsoft through five stages of AI maturity—from initial exploration to becoming an AI-driven enterprise. This has been a three-year journey, and you and your digital leaders will need to be prepared to take time to fully experience each of these stages to truly unlock the potential of AI to transform your enterprise.

What follows is a stage-by-stage summary of how we achieved our transformation, followed by a list of empowering actions you can take to help you on your own journey.

Mapping our journey to AI maturity

Stage 1: Awareness and foundation

Set a bold vision for your AI journey, anchored in clear business outcomes—avoid implementing “AI for AI’s sake.” Engage your executive sponsors early and form an AI Center of Excellence (CoE) to foster cross-functional collaboration and empower experimentation. Establish Responsible AI principles alongside your organization’s ethics team and assess your data readiness from the start—remember, “no AI without data.” By building these foundations, you’ll position your teams to confidently launch AI initiatives and drive meaningful transformation.

Target outcomes

A foundational strategy, governance principles, and leadership buy-in to kickstart AI projects.

Learn more

Learn how we’re responding to the AI Revolution with an AI Center of Excellence.

“At the Microsoft Digital AI Center of Excellence, we’ve learned that combining strong governance, data readiness, and a continuous-improvement mindset transforms AI pilots into enterprise-scale solutions,” says Nitul Pancholi, the AI CoE lead in Microsoft Employee Experience. “This guide distills our three-year journey into clear, actionable steps to accelerate responsible AI adoption, mitigate risk, and drive measurable business impact.”

Stage 2: Active pilots and skill building

To accelerate your AI journey, start by launching targeted pilot projects across diverse areas of your organization—think automated support chatbots or network analytics. Encourage experimentation and leverage hackathons to surface a broad range of ideas. Narrow these down to your most promising initiatives by evaluating business value against implementation effort and focus resources on a select group of high impact “big bets.”

Empower your teams by investing in upskilling: offer discipline-aligned learning paths, issue digital credentials, and celebrate progress to foster a culture of continuous learning and knowledge-sharing. Establish early-stage governance by requiring all pilots to undergo Responsible AI and architectural reviews. By following these steps, you’ll create early momentum, build internal expertise, and identify the AI solutions most likely to drive meaningful impact at scale.

Target outcomes

The first tangible benefits of AI: efficiency gains, time and cost savings, and quality improvements, and an internal talent pool emerging, paving the way to scale successful solutions.

Learn more

Find out how we’re embracing AI at Microsoft with new AI certifications.

Stage 3: Operationalize and govern

To scale and integrate AI solutions across your organization, move beyond pilot projects by deploying AI solutions directly into production and embedding them within core business workflows.

Strengthen your data and AI infrastructure—consider implementing a unified data platform and robust Machine Learning Operations (MLOps) pipelines—to support this transition. Formalize enterprise governance with clearly defined steering teams: empower your AI Center of Excellence to accelerate implementation and establish a Data Council to ensure data quality and “AI-ready” assets and a Responsible AI Office to oversee ethical use and compliance. Encourage collaboration among these groups and designate domain leads to ensure your AI initiatives consistently deliver tangible business value.

By putting these practices in place, you can drive successful scaling and operationalization of AI throughout your enterprise.

Target outcomes

Multiple AI use cases running at enterprise scale under robust oversight with cross-functional alignment on AI objectives and the business value they’re delivering.

Learn more

Read our guide for tackling Microsoft 365 Copilot governance your company.

Stage 4: Enterprise-wide adoption

To consolidate your gains and achieve AI adoption across the enterprise, make AI a core consideration in every new project and process.

Ask where AI-driven intelligence can deliver real impact, whether by boosting efficiency, enhancing user experiences, or unlocking new business value. Align AI initiatives with your organization’s strategic goals by empowering business leads to synchronize efforts and continuously update your AI roadmap. Cultivate a data-driven culture through ongoing, large-scale training and make AI tools a natural part of everyday work. Establish rigorous impact tracking with clear metrics for value delivered—such as time savings, cost reduction, and quality improvements—and review these outcomes regularly at the leadership level to maintain accountability.

By integrating these practices, you can drive AI adoption throughout your organization and ensure sustained, measurable impact.

“What’s unique about our approach is that every agent is engineered for responsible action. We design agents to operate within enterprise workflows, guided by policy-aware controls, telemetry integration, and human oversight,” says Faisal Nasir, the AI CoE and Data Council lead in Microsoft Employee Experience.

Through the AI Center of Excellence and the Data Council, we ensure agents are grounded in AI-ready data and undergo comprehensive architecture and governance reviews.

“This ensures our AI solutions are not only intelligent, but also accountable, governable, and fully production-ready,” Nasir adds.

Target outcomes

AI is a pillar of your operational strategy, backed by a data-driven culture and continuous monitoring of business impact.

Learn more

Learn how we’re approaching Microsoft 365 Copilot change management at Microsoft.

Stage 5: Transform your business with agentic AI

To drive a lasting AI-powered business transformation, organizations must embed AI into every aspect of their operations and culture.

Start by leveraging the expertise of your AI CoE to foster innovation, drive continuous improvement, and keep your AI initiatives evolving. Use structured mechanisms like a Kaizen funnel to crowdsource, prioritize, and advance ideas that extend the impact of AI across the enterprise.

Strengthen governance to address the advanced challenges of agentic applications, including responsible scaling of generative AI and effective mitigation of AI hallucinations. Focus on refining human-AI collaboration so your teams are empowered to offload routine tasks to AI agents and concentrate on higher-value work.

Another tactic that’s been highly successful in Microsoft Digital is “Fix, Hack, Learn” weeks, where employees are encouraged to identify opportunities to improve our services. Multi-disciplinary teams are empowered to innovate with AI to improve our organizational effectiveness, yielding multiple AI-powered breakthroughs that are already in production.

“In Microsoft Digital, continuous improvement is a driving force behind our AI transformation,” says Don Campbell, principal product manager within Microsoft Digital and member of our AI Center of Excellence. “By embedding it and AI into every layer of our operations, we’re not only optimizing how we work today, but we are also strategically preparing our processes to become agentic tomorrow. This disciplined approach ensures that when we make a process agentic, it’s not just automated—it’s intelligent, secure, and purpose-built to scale across the enterprise.”

Target outcomes

An organization transformed by AI, achieving significant efficiency gains and innovations, and recognized as a leader in enterprise AI adoption.

Learn more

Guiding hands: take a look inside the councils steering AI projects here at Microsoft.

What our experts have to say:

“In Microsoft Digital, continuous improvement is a driving force behind our AI transformation. By embedding it and AI into every layer of our operations, we’re not only optimizing how we work today, but we are also strategically preparing our processes to become agentic tomorrow.”

Don Campbell, principal product manager and CoE member, Microsoft Digital

“At the Microsoft Digital AI Center of Excellence, we’ve learned that combining strong governance, data readiness, and a continuous-improvement mindset transforms AI pilots into enterprise-scale solutions. This guide distills our three-year journey into clear, actionable steps to accelerate responsible AI adoption, mitigate risk, and drive measurable business impact.”

Nitul Pancholi, AI Center of Excellence lead, Microsoft Employee Experience

 “What’s unique about our approach is that every agent is engineered for responsible action. We design agents to operate within enterprise workflows, guided by policy-aware controls, telemetry integration, and human oversight.”

Faisal Nasir, AI CoE and Data Council lead, Microsoft Employee Experience

Enabling success—lessons from our journey as the company’s IT organization

Achieving AI maturity is dependent on a combination of technological, organizational, and cultural factors. These enablers support the successful adoption and integration of AI within the organization.

For IT decision-makers charting the course to enterprise-scale AI, the journey is about far more than technical implementation—it’s about activating the right enablers to unlock both rapid and sustainable business impact.

Successfully scaling AI means orchestrating executive vision, robust governance, responsible innovation, resilient data foundations, and a culture of empowered talent—all working in harmony. Each of these levers is crucial not only for accelerating the path from pilot to production, but also for ensuring that every AI initiative delivers measurable outcomes, mitigates risk, and creates lasting organizational value.

By prioritizing these foundational pillars, IT leaders can fast-track value realization, embed accountability, and transform AI from a promising experiment into a strategic engine for competitive advantage. The following items explore the essential enablers that drive AI maturity at pace and why they matter now more than ever for organizations determined to lead in the age of intelligent transformation.

Seven enablers of enterprise AI transformation

Executive sponsorship and governance

To accelerate AI maturity within your organization, start by securing strong executive sponsorship and establishing clear governance structures. Appoint dedicated AI leaders and form cross-functional teams such as an AI Center of Excellence and supporting councils with well-defined roles and responsibilities. Maintain alignment with your business strategy through regular steering meetings and roadmap reviews. This approach will ensure your AI initiatives remain focused, impactful, and strategically integrated across the enterprise.

Responsible AI by design

To embed ethics and effectively manage risk in every AI project, integrate Responsible AI principles from the outset. Establish a Responsible AI Council or similar oversight group to ensure all solutions are rigorously reviewed for ethical standards before launch. By instituting mandatory Responsible AI assessments, you’ll foster trust, safeguard your organization, and address potential issues proactively—setting a strong foundation for sustainable AI adoption. This not only reduces reputational and regulatory risk, it also enables faster adoption, strengthens stakeholder confidence, and ensures AI initiatives deliver lasting value aligned with your business goals.

Data foundation, architecture reviews, and technical readiness

Treat data as a strategic asset by establishing a unified data strategy—start with a Data Council to catalogue key sources, improve data quality, and implement robust governance and access controls. Build AI-readiness across your enterprise by embedding architecture reviews and design validation into your engineering lifecycle, ensuring every solution is scalable, composable, and compliant by design. Leverage architecture forums to crowdsource feedback, align on technical standards, and promote reusable patterns that accelerate delivery. With secure cloud environments, ML Ops pipelines, and standardized AI platforms in place, your teams will be equipped to develop and scale AI solutions quickly, safely, and consistently.

Talent, skills, and culture

To build an AI-ready workforce and foster a culture of innovation, prioritize company-wide training and upskilling programs that elevate AI literacy at every level. Establish a Center of Excellence and empower “AI champions” within teams to drive adoption and celebrate meaningful impact. Encourage open collaboration—share code, best practices, and project outcomes across your organization—to accelerate learning and scale success. By breaking down silos and enabling employees to experiment with intelligent solutions, you’ll create the environment needed for sustained growth and enterprise-wide transformation. In Microsoft Digital, we are not just training our employees to use AI, we are empowering them to co-create the future of their roles. When employees are empowered to build and govern their own agents, that is when transformation truly scales.

Impact tracking and accountability

To drive meaningful business impact with AI, start by defining clear, measurable success metrics—think hours saved, cost efficiencies, and quality improvements—that can be rolled up into an organizational AI scorecard. Review these outcomes regularly at the leadership level to keep the focus on what matters. For every major AI initiative, assign an accountable owner who champions the solution, communicates the business story, and manages performance reporting.

Foster transparency by consistently comparing targets to actual results and openly sharing lessons learned when goals are missed. By embedding accountability into your rhythm of business, you’ll enable agile decision-making, concentrate your efforts where AI delivers the most value, and nurture a culture of continuous improvement. In Microsoft Digital, we’ve defined an AI value measurement framework with six dimensions of value that you can use as benchmarks to determine the impact of your own investments.

Change management and communication

To drive successful AI adoption, treat it as a people-first transformation—not just a technology deployment. Start by developing robust deployment and adoption plans for your key solutions: invest in training, craft clear communications, and establish dedicated support channels such as FAQs and help desks. Maintain a steady pulse of communication with your stakeholders—consider newsletters, interactive town halls, and a centralized library of AI success stories to celebrate impact and progress. By prioritizing transparency and providing ongoing support, you’ll smooth the path to change, encourage enthusiastic adoption, and sustain momentum throughout your organization.

Continuous improvement, innovation, and partnerships

To drive continuous improvement and innovation with AI, keep a dynamic backlog of opportunities and support each with a clear value case and refresh your pipeline regularly. Adopt structured forums such as continuous improvement and Kaizen events to identify, evaluate, and prioritize new AI use cases that deliver tangible business outcomes. Use a robust prioritization framework to ensure focus on initiatives with the greatest impact.

Identify partner teams who can serve as early adopters and provide feedback to inform your continuing journey. By building a disciplined innovation pipeline and fostering a collaborative ecosystem, you create a foundation for ongoing experimentation, accelerated learning, and sustainable AI innovation across your organization.

Advancing your organization into the frontier of AI

To embrace the next era of AI, it’s time to look beyond traditional automation and prepare your organization for agentic AI frameworks and autonomous, interoperable agents. These advanced systems aren’t just digital assistants—they’re designed to plan, act, and collaborate across workflows with minimal intervention, offering the potential to fundamentally transform how work gets done.

Start by identifying areas where agentic AI can drive real business value. Empower domain experts within your teams to become Agent Leaders—individuals who can design, oversee, and govern agent ecosystems at scale. Align your AI strategy with forward-looking industry insights and best practices—sources like the 2025 Annual Work Trend Index: The Frontier Firm Is Born offer invaluable guidance for responsible AI adoption and organizational transformation.

Recognize that the impact will be significant. Industry analysts such as Gartner predict that by 2028, about a third of enterprise applications will feature agentic AI capabilities and over 15% of daily work decisions will be handled by AI agents.

Evolving from large language models to agents

To get ahead, foster a culture of experimentation. Host hackathons, pilot agentic AI prototypes, and develop governance frameworks that ensure responsible management of these emerging technologies. Treat your AI journey as a continuous process—a growth mindset and incremental progress are key. As AI evolves, so should your practices: be ready to adapt your governance, refine human-AI collaboration, and embrace new paradigms like fully autonomous agents.

Each stage of this journey unlocks new possibilities. Ensure your organization remains at the forefront of AI maturity by committing to continuous improvement and innovation. The future of work isn’t a destination—it’s a dynamic path. Evolve your strategy, cultivate expertise, and enable your teams to thrive in the rapidly advancing digital landscape, powered by AI innovation and continuous improvement.

Key takeaways

To help your organization progress on its AI journey, consider the following strategies:

• Invest in data infrastructure and AI platforms. Building robust data infrastructure ensures your organization is prepared to leverage AI, supporting scalable, innovative, and secure AI-driven solutions.
• Foster a culture of innovation and collaboration. Champion an AI-forward culture where innovation and collaboration drive the adoption of agentic AI.
• Develop AI expertise through training and development. Upskilling your teams empowers them to navigate the rapid advances of AI, drive innovation, and ensure your organization stays competitive as agentic AI transforms workflows and business outcomes across every industry.
• Align AI initiatives with strategic business goals. Ensuring AI initiatives align with business goals maximizes impact and positions your organization to succeed in the rapidly evolving world of agentic AI.
• Implement ethical AI practices based on Microsoft’s Responsible AI Principles. Adopting ethical AI practices builds trust, ensures responsible innovation, and prepares your organization to navigate the evolving landscape as AI becomes central to business operations and decision-making.

Try it out

Read our guide for deploying and driving adoption of Microsoft 365 Copilot.

Related links

• Learn how we’re using good governance to ride the wave of agents washing over Microsoft.
• Check out how we’re embracing this new ‘agentic’ moment at Microsoft.
• Take a look inside the councils steering AI projects here at Microsoft.
• Find out how we’re unlocking enterprise AI extensibility at Microsoft with Copilot Studio.

The post Enterprise AI maturity in five steps: Our guide for IT leaders appeared first on Inside Track.