• Introduction: Understanding device readiness and lifecycle
• Chapter 1: Plan and standardize
• Chapter 2: Acquire
• Chapter 3: Onboard
• Chapter 4: Operate
• Chapter 5: Optimize
• Chapter 6: Refresh or reassign
• Chapter 7: Retire
• Conclusion

In a global organization such as Microsoft—with more than 220,000 employees connecting and working together from offices and remote workspaces scattered around the world—we enable our workers to stay productive from almost anywhere, on a wide range of devices.

At Microsoft Digital, the company’s IT organization, we provide our employees with that flexibility while helping keep the devices they use secure, compliant, and supportable at enterprise scale.

“Every digital experience depends on the readiness of the devices our employees use every day. Organizations that treat device management as a strategic lifecycle capability rather than a series of operational tasks are better positioned to adapt to change, reduce risk, and take advantage of new innovations with confidence.”

Brian Fielder, vice president, Microsoft Digital

We balance employee experience, security posture, and operational efficiency as part of modern device management.

This IT playbook explains how we think about device readiness at Microsoft Digital, where we serve as Customer Zero for the company. It covers the tradeoffs we manage, the lifecycle patterns that hold up over time, and the signals we monitor to understand whether we’re reducing risk or quietly accumulating it.

We also share resources you can use to apply modern device management in your own enterprise.

“Every digital experience depends on the readiness of the devices our employees use every day,” says Brian Fielder, vice president of Microsoft Digital. “Organizations that treat device management as a strategic lifecycle capability rather than a series of operational tasks are better positioned to adapt to change, reduce risk, and take advantage of new innovations with confidence.”

Understanding device readiness and lifecycle

Effective device readiness must be sustained across the full device lifecycle, from planning and acquisition through operation, refresh, and retirement. In an enterprise environment, a device is considered ready only when we can:

• Identify it and confirm ownership
• Govern it through identity and policy
• Keep it secure and compliant over time
• Support and recover it when something goes wrong
• Remove it cleanly when its role ends

When these conditions work together, device readiness becomes an operating advantage. We can move employees onto approved devices faster, enforce security and access requirements more consistently, and make lifecycle decisions with better data and less guesswork. The result is a device estate that is manageable, trustworthy, and productive at enterprise scale.

The device lifecycle model

At Microsoft Digital, we use the device lifecycle as a control system for readiness. We connect decisions, standards, and signals across the full lifecycle, so we can manage readiness over time rather than just check on it at isolated points. Each stage reinforces the next, and any issues show up early enough for us to correct them before they spread.

The stages serve specific functions:

• Plan and standardize: Sets the device standards and guardrails that make the rest of the lifecycle workable at scale.
• Acquire: Brings devices in as approved enterprise assets that can be tracked and managed from the start.
• Onboard: Gets employees up and running quickly through automated setup and policy-driven enrollment.
• Operate: Ensures devices are secure, up-to-date, and dependable over time.
• Optimize: Uses telemetry to reduce friction and improve how the lifecycle performs.
• Refresh or reassign: Keeps devices useful longer by replacing or repurposing them before they become a problem.
• Retire: Removes devices cleanly so access is closed off, data is protected, and disposition is complete.

The device lifecycle is a durable operating model that we manage holistically. Weakness in any phase often appears after the original decision was made and far from the point where the issue began.

We describe each lifecycle stage using the following structure:

Readiness question

What must be true at this stage to enable readiness

What good looks like

Our outcome-oriented goals for the stage

Signals

How we identify drift or technical debt

Microsoft Digital operating practices

Systems and processes we use internally to make this stage durable and repeatable

Stakeholder lens

Who contributes or has expectations at this stage

Being Customer Zero

How we stress-test internally in Microsoft Digital and learns at scale

Chapter 1: Plan and standardize

In this stage, we define the standards, controls, and supported patterns that make the rest of the device lifecycle manageable. Good planning reduces downstream exceptions and gives later stages a more stable foundation.

Readiness question

Are our device standards enforceable in practice, and do they reduce downstream cost, risk, and fragmentation?

What good looks like

Clear, role-based device standards limit variance and make onboarding and support repeatable at scale. These standards are grounded in capabilities that our platforms and tools can enforce, not aspirational policy language.

Signals

Signals help us detect when a lifecycle stage is falling out of alignment with its intended outcomes, becoming harder to manage, or creating downstream cost and complexity. These signals include:

Exception rates that grow over time (more devices falling outside supported standards), which later shows up in higher support costs and weaker servicing consistency.

Policies that are documented but not enforced through device health and access controls, including modern management capabilities such as Intune compliance policies and Conditional Access. Some examples are minimum OS requirements, encryption standards, and Microsoft Defender health. Documented but unenforced controls create “paper compliance” instead of true operational compliance.

Device and OS coverage statements that become hard to verify. This could manifest as device counts, OS mix, and ownership mix becoming directional rather than telemetry-backed, which weakens credibility and decision making.

Microsoft Digital operating practices

These practices show how we design the operating model to make the target state durable and repeatable. They represent the baseline decisions we make around rollout control, visibility, and enforcement to help prevent drift before it emerges.

• Early partnership with chip providers such as Intel, AMD, Qualcomm, and NVIDIA enables upstream testing, validation, and feedback on next-generation hardware. As Customer Zero for the Windows product group, our team in Microsoft Digital co-develops and is an early adopter of the Copilot+ PC experience, so readiness, performance, and security are tested and proven before fleet-wide deployment.
• For Windows devices, readiness standards are anchored to enforceable hardware and security capabilities, including TPM 2.0, Secure Boot, Microsoft Pluton, Credential Guard, and Windows Hello for Business compatibility.
• Devices are sourced from approved OEM catalogs and built to order, then validated against supported firmware, BIOS or UEFI, driver configurations, and setup experience. This approach reduces downstream support, servicing risk and providing white-glove device preparation while supporting a consistent, secure out-of-box experience.
• Apple (macOS/iOS) readiness is anchored to managed enrollment, encryption (for example, FileVault on macOS), and phishing-resistant sign-in via Platform SSO and passkeys.
• Android readiness requires Android Enterprise Work Profile support, verified device integrity (not compromised), and minimum OS and security patch levels enforced via MDM posture and Conditional Access.

Stakeholder lens

The following stakeholder groups shape planning, depend on its outcomes, and can detect misalignment quickly when expectations drift:

Employees

Benefit from clear, role-appropriate device expectations.

Endpoint engineering

Defines standards and automation foundations.

Security

Partners on baseline posture and control requirements.

Procurement and finance teams

Influence catalog decisions and lifecycle cost models.

Being Customer Zero

Before broad deployment of new capabilities, enterprise endpoint teams and product groups align on an enterprise readiness contract that spans trust and safety, manageability, and recoverability. We validate identity-bound access and tenant trust boundaries early, so we don’t introduce unmanaged enterprise risk.

Chapter 2: Acquire

In this stage of device lifecycle management, we bring devices into the environment as known, trackable enterprise assets. The goal is to make sure every device enters the lifecycle with the identifiers, registrations, and sourcing controls needed for clean provisioning and management.

Readiness question

Are devices known, owned, and visible before employees need them?

What good looks like

Procurement and asset registration are predictable and integrated, ensuring devices enter the environment as known enterprise assets, already associated with inventory systems and ready for automated provisioning.

Signals

These signals show where breakdowns in sourcing, registration, or asset control can create problems later in the lifecycle.

Procurement lead times cause day-one onboarding delays when devices arrive before they are ready for provisioning.

Inventory and asset records drift from reality, causing organizations to lose a single source of truth for device status, ownership, and lifecycle stage. This can create gaps in offboarding, recovery, and audit evidence.

Devices reach employees before registration is complete, such as when Autopilot or Apple Business Manager registration isn’t done before delivery. This forces catch-up work and introduces exceptions.

Microsoft Digital operating practices

These practices show how we design the operating model to make the target state repeatable:

• Acquisition readiness and zero-touch deployment begin with an integrated sourcing and provisioning model. This model defines how we source and acquire devices globally, apply persona-based configuration, fulfill local language requirements, and preload the latest supported operating system and drivers.
• When devices are delivered, they arrive asset-tagged, bundled, and preregistered with Windows Autopilot. This enables a consistent, secure, hands-off setup experience from first power-on.
• Acquisition readiness is tied to asset registration and inventory accuracy. Corporate devices are registered at purchase, associated with enterprise asset identifiers, and tracked continuously from order through retirement.

Stakeholder lens

These groups influence acquisition decisions and depend on those decisions being accurate, timely, and supportable:

Procurement and finance

Own sourcing and logistics.

IT

Validates compatibility and enterprise readiness.

Security

Advises on supply chain and trust considerations.

Being Customer Zero

Our early adoption program accelerates learning while preserving governance. We operate under explicit guardrails, including security posture, data boundaries, and regulatory requirements, so speed doesn’t bypass enterprise controls.

Chapter 3: Onboard

In this stage, we turn a device into a usable, trusted work endpoint through automated setup, enrollment, and policy enforcement. A strong onboarding experience helps employees become productive more quickly without weakening identity or compliance controls.

Readiness question

Can employees be productive on day one, experiencing minimal friction without bypassing identity, policy, or compliance?

What good looks like

Onboarding is fast, predictable, and largely hands-off for IT while remaining identity-bound and policy-driven. Day-one productivity comes through repeatable automation rather than exception handling.

Signals

These signals tell us when onboarding is becoming inconsistent, support-heavy, or harder to scale cleanly:

“Time to productive” increases or varies significantly (the onboarding path is no longer repeatable at broad scale).

Enrollment Status Page (ESP) failures increase (setup blocks, app install delays, and policy install failures).

Enrollment-related support calls or tickets rise during onboarding windows, indicating that friction is shifting from automation to human support.

Post-onboarding surveys and helpdesk ticket analysis show declining satisfaction or repeated “same issue” patterns.

Bring-your-own-device (BYOD) enrollment confusion increases as employees are unclear on what’s managed, what data is collected, or what happens when access is revoked.

Microsoft Digital operating practices

These practices show how the operating model makes the target state repeatable:

• Devices are approved and certified before reaching employees. Corporate Windows and Apple devices are sourced from approved OEM catalogs and registered through Windows Autopilot or Apple Business Manager, then associated with user identities and asset systems.
• For Windows devices, Autopilot registration occurs via OEM or partner APIs whenever possible; manual hardware hash registration is reserved for exceptions. Assigned Autopilot profiles define Entra ID join behavior, Intune enrollment, Out‑of‑Box Experience configuration, required applications, and baseline policies.
• The Enrollment Status Page acts as a gate that can’t be bypassed. Devices cannot be used until required apps, updates, and policies are successfully installed. If setup fails, reset is blocked, and errors are captured for IT remediation.
• Apple Business Manager is used exclusively for corporate‑purchased Apple devices; personally owned Apple and Android devices follow Intune BYOD enrollment paths. Android devices enroll using the Android Work Profile mechanism; Google Zero Touch is not used in this environment.
• Before access to corporate resources is allowed, devices must meet certification requirements, including Intune enrollment, encryption (BitLocker or FileVault), supported OS versions, Defender for Endpoint health, and required hardware security capabilities. Conditional Access enforces these requirements at sign‑in.
• Virtual onboarding options such as Azure Virtual Desktop are used for contractors, regulated roles, or temporary fallback access.

Stakeholder lens

These groups own setup, experience the outcome directly, and rely on tight alignment for onboarding to work smoothly:

Employees

Expect a power-on-and-go experience.

IT

Owns provisioning automation and exceptions.

Identity and security teams

Validate trust and access enforcement.

Being Customer Zero

The readiness contract explicitly validates manageability, zero-touch onboarding readiness, and identity enforcement before capabilities advance beyond early internal cohorts.

Chapter 4: Operate

In this stage, we keep devices secure, current, and reliable through ongoing servicing and operational discipline. The aim is to maintain a stable experience over time while minimizing manual intervention and operational noise.

Readiness question

Can devices remain secure, reliable, and current over time without constant manual intervention, and can known vulnerabilities be remediated quickly without manual escalation?

What good looks like

Continuous servicing and support preserve productivity while minimizing operational noise and exposure windows.

Signals

These signals help us see when operations are getting noisier, less predictable, or more reactive than they should be:

Update compliance thresholds are missed, meaning more devices are at risk because of outdated patches or repeat failure patterns.

Rollbacks, pauses, or halted rollouts become frequent, which indicates that the ring and validation strategy isn’t catching issues early enough.

Helpdesk tickets trend upward for update failures or device remediation (operational noise is rising instead of staying quiet).

Firmware- or driver-related instability increases, which requires additional validation, staging, or rollback controls.

Zero-day response requires repeated emergency actions; this signals that baseline update hygiene isn’t consistently holding.

Microsoft Digital operating practices   

These practices show how we design the operating model to make the target state durable and repeatable:

• Windows Autopatch: Provides a single, integrated update management experience in Intune. It combines Windows Update for Business policy-based controls with automated, telemetry-driven deployment across staged rollout waves, including built-in issue detection, pause, and rollback capabilities.
• Windows Hotpatch: Helps reduce disruption by applying certain security updates without requiring a restart, which supports continuity for eligible devices.
• Intune Vulnerability Agent: Extends vulnerability visibility and supports coordinated remediation through device management workflows.
• Enterprise App Management: Gives us a structured way to manage application deployment, updates, and policy alignment across managed devices.
• Update Compliance: Via Azure Monitor, it provides insight into installation rates and failure patterns, triggering remediation workflows when thresholds are crossed. Firmware and driver updates are validated with OEM partners and staged using the same ring-based deployment model.
• Minimum OS requirements: Non-Windows devices must meet minimum operating system requirements, encryption standards, and endpoint protection requirements before they can access corporate resources. These are enforced by Intune compliance policies and Conditional Access.

Stakeholder lens

These groups keep the environment running smoothly and depend on that stability every day:

Employees

Experience quiet, predictable updates that minimize disruption and maintain productivity.

IT operations teams

Own reliability and remediation, so updates are delivered safely at scale.

Security teams

Use device compliance and update posture as critical signals for access control and risk reduction.

Being Customer Zero

We use a staged rollout approach to test new updates and features with progressively larger groups of users. This helps us identify issues early, validate performance and reliability at scale, and continuously improve quality before broad deployment across the company.

Chapter 5: Optimize

In this stage, we use telemetry and operational insight to improve how the device lifecycle performs. Optimization helps us reduce friction, close recurring gaps, and make better decisions about where to invest effort.

Readiness question

Are fleet health, compliance, and cost improving or merely visible?

What good looks like

Telemetry and automation inform decisions that decrease friction, eliminate compliance gaps, and improve lifecycle efficiency.

Signals

These signals show when optimization has stalled and the environment is absorbing effort without reducing friction or risk:

Known vulnerabilities remain open longer than expected, increasing exposure to risk.

Compliance rates stop improving from one release to the next, despite ongoing remediation efforts.

Routine issues continue to require manual intervention instead of being handled through automation.

Devices repeatedly cycle in and out of compliance, indicating deeper issues in the environment.

Operational reviews spend more time addressing recurring problems and less time improving the overall service.

Microsoft Digital operating practices

These practices help us improve the environment over time and reduce operational overhead. They focus on measuring results, standardizing management, and automating routine work where possible.

• Optimization investments are guided by metrics such as patch compliance, automation coverage, remediation success rates, and operational workload.
• We standardized Windows devices on Microsoft Entra ID join and retired older management models such as Workplace join, Hybrid Azure AD join, and Active Directory join. This reduced complexity, simplified policy enforcement, and created a more consistent management experience across devices.
• Windows Autopatch, Hotpatch, and automated remediation help us keep devices current while minimizing disruption for employees.
• Intune firmware, driver, and Enterprise App Management capabilities extend the same update and deployment discipline beyond the operating system to hardware components and third-party applications.
• Microsoft Security Copilot, Microsoft 365 Copilot, and Copilot in Power BI help our teams analyze Intune and Defender data more quickly, making it easier to identify issues and prioritize remediation efforts.

Stakeholder lens

Optimization in a cloud-native model depends on aligned ownership across engineering, security, and employee experience teams. Modern management gives us a standardized, policy-driven foundation.

Employees

Experience fewer disruptions and faster issue resolution.

Endpoint engineering and modern management teams

Drive standardization, policy enforcement, and continuous fleet health optimization.

Security and CISO partners

Rely on device compliance signals to enforce secure access with minimal manual intervention.

Product groups

Incorporate Customer Zero feedback from our team in Microsoft Digital to improve enterprise readiness.

Being Customer Zero

As Customer Zero for modern management, we validate optimization capabilities at enterprise scale and feeds insights directly to engineering. This helps ensure solutions are designed for real-world scenarios.

• Modern Management transformation (Entra ID–based) simplified management and closed Conditional Access gaps.
• Validation of Autopatch and Hotpatch improves update readiness and rollout quality before broad release.
• AI-powered investigations unified Defender and Intune signals to accelerate issue triage and remediation.

Chapter 6: Refresh or reassign

In this stage, we decide whether a device should continue in service, move to a new owner, or be replaced. When done with intention, refreshing and reassignment extend an asset’s value while reducing avoidable support issues and lowering security risk.

Readiness question

Are devices refreshed or reassigned before they become performance or security debt?

What good looks like

Condition-based refresh and reassignment maximize asset value while minimizing disruption.

Signals

These signals help us see when device reuse, replacement, or support timing is slipping out of a manageable rhythm:

Hardware health telemetry trends indicate looming failures, including battery wear, thermal events, and disk health degradation, which creates unplanned downtime risk.

Firmware or driver update readiness becomes inconsistent across models (this increases the cost of servicing and support).

Refresh timing becomes reactive (devices are replaced after repeated failures instead of during planned lifecycle windows).

Reassignment or reuse requires more manual work (this signals that reset and reprovision flows aren’t consistently repeatable).

Microsoft Digital operating practices

These practices show how we make device refresh and reassignment consistent, scalable, and repeatable. They aren’t reactive steps we take when aging devices, inventory gaps, or fulfillment delays become visible. They’re the standard processes, controls, and decision points we use to keep devices moving through their lifecycle efficiently and to prevent those issues from occurring in the first place.

• Windows lifecycle guardrails: Align firmware servicing and hardware support windows to a 48-month refresh baseline so devices remain within OEM-supported windows for security and firmware updates.
• Condition-based refresh signals: Battery wear, thermal events, disk health, and firmware servicing status are monitored through telemetry to trigger proactive refresh actions before devices become performance or security debt.
• Catalog discipline: Devices are sourced from the approved OEM catalog, with model selection tied to persona, lifecycle stage, and firmware support roadmap. This reduces drift between deployed inventory and supported hardware.
• Defined recovery workflow: Reassignment is supported through a documented recovery workflow where applicable. Previously owned corporate Windows devices are returned to the out-of-box experience using WinRE-based reset initiated locally or through Company Portal, enabling redeployment to new owners through Autopilot. Recovery USB is the documented fallback when WinRE can’t be used.
• Sustainability and cost outcomes: Where possible, devices are reassigned, repurposed, or returned through approved recycling channels. This extends asset value and supports our broader sustainability commitments.
• Structured exception handling: Devices that fall outside refresh windows because of supply, persona, or business constraints follow a documented exception path with a defined review cadence so exceptions don’t become the norm.

Stakeholder lens

These groups help determine whether devices should be refreshed, reassigned, repaired, or retired:

Employees

Need reliable devices that support their work without repeated failure or disruption.

Endpoint engineering and support teams

Use telemetry and recovery workflows to guide refresh and reassignment decisions.

Procurement, finance, and sustainability teams

Balance device value, lifecycle cost, reuse, and responsible disposition.

Being Customer Zero

Our team in Microsoft Digital validates refresh and reassignment workflows at scale across device models, OEMs, and silicon partners before they reach employees. By partnering early with Windows product group, silicon partners, and OEM partners, we test Autopilot re-enrollment, WinRE-based recovery, and Company Portal reset flows in real conditions. We then turn lifecycle telemetry and friction points into direct product feedback as part of our Customer Zero commitment.

Chapter 7: Retire

In this stage, we close the lifecycle cleanly by removing devices from service in a controlled and auditable way. Retirement helps ensure that access is revoked, data is protected, and disposition is complete.

Readiness question

Can devices exit cleanly without leaving behind data, access, or audit gaps?

What good looks like

Offboarding is policy-driven, auditable, and integrated with identity, asset, and sustainability workflows.

Signals

These signals show where offboarding is leaving loose ends that can create exposure, confusion, or audit gaps later:

Orphaned devices appear (devices aren’t tied to an active person or remain enrolled after a lifecycle trigger).

Time to offboard grows (slow decommissioning creates prolonged access and data risk).

Wipe verification and audit artifacts are incomplete (this includes missing wipe confirmation logs, disposition certificates, or chain-of-custody records).

Devices remain enabled in Entra ID or continue to pass access checks after they should be decommissioned (these are access revocation gaps).

People repurpose devices informally, such as loaners or secondary devices, without clarity on what decommissioning means for audit and compliance versus reassignment.

Microsoft Digital operating practices

Offboarding triggers include employee exit, end of warranty, hardware health degradation, or compliance failure. These triggers launch automated workflows using Power Automate and ServiceNow, revoke access through Microsoft Entra ID and Conditional Access, and wipe devices through Intune or Configuration Manager. Data sanitization aligns with NIST 800-88 guidelines, and chain-of-custody controls support secure disposition.

Stakeholder lens

These groups help ensure retirement is controlled, auditable, and complete:

Employees and managers

Return devices and confirm lifecycle events that trigger offboarding.

IT operations and asset teams

Coordinate wipe, recovery, inventory updates, and disposition records.

Security, compliance, and sustainability teams

Validate access removal, data sanitization, chain of custody, and responsible disposition.

Being Customer Zero

Our team in Microsoft Digital validates retirement workflows at scale, including automated offboarding, access revocation, NIST 800-88 sanitization, and chain-of-custody controls across devices. By partnering with Microsoft Entra ID, Windows, and Intune teams, we surface real-world gaps such as orphaned devices, delayed wipes, and residual access. We then translate those gaps into product improvements that strengthen the retirement experience for customers and help keep our own audit and compliance posture durable.

Conclusion

Device readiness is not a one-time milestone. It is a lifecycle discipline that connects planning, sourcing, onboarding, operations, optimization, refresh, and retirement. At Microsoft Digital, we use that lifecycle to keep devices secure, employees productive, and operational decisions grounded in data.

“Device readiness is ultimately about enabling people to do their best work wherever and however they choose to work. By treating the device lifecycle as a strategic capability, we’ve transformed this function from an operational necessity into a source of innovation, resilience, and future growth across our organization.”

Senthil Selveraj, principal group product manager, Microsoft Digital

When every stage has clear standards, measurable signals, and repeatable mechanisms, the device estate becomes easier to manage and safer to scale. That helps us reduce exceptions, improve employee experience, and strengthen enterprise security without relying on manual effort as the default path.

“Device readiness is ultimately about enabling people to do their best work wherever and however they choose to work,” says Senthil Selveraj, a principal group product manager in Microsoft Digital. “By treating the device lifecycle as a strategic capability, we’ve transformed this function from an operational necessity into a source of innovation, resilience, and future growth across our organization.”

The post How we deploy and manage enterprise devices at Microsoft Digital appeared first on Inside Track.

But Radoi didn’t let it faze her. Even though she grew up without a computer in her home and studied criminal law in college, she found herself drawn toward a career in technology. After about a dozen years of working in IT roles for different companies, she was invited to apply at Microsoft Romania.

So, she interviewed and received an offer. At the exact same time, she learned (to her surprise) that she was pregnant. With this twist, she thought maybe that her offer would be withdrawn.

“I was pretty afraid to tell Microsoft about the pregnancy, because my experience with other companies in Romania is that they are not always supportive of women with children,” Radoi says.

But instead of uncertainty or rejection, she was met with support and understanding.

“It was amazing,” Radoi said of her future Microsoft manager’s reaction to her disclosure. “After a quick conversation with HR, he just said, ‘Welcome to Microsoft. We want you as part of our team, and this pregnancy doesn’t change that. It’s not an issue for us.’ It was the first time I’d received that kind of professional support and encouragement as a woman in IT.”

In the eight years since that conversation, Radoi has thrived in the supportive Microsoft culture and forged a successful career here.

“I really love what I’m doing here at Microsoft. I love my leaders, and I trust them. That’s huge for me. It’s allowed me to grow and do my best work.”

Adelina Radoi, senior IT field manager, Microsoft Digital

She now serves as a senior IT field manager for Microsoft Digital—the company’s IT organization—based in Bucharest. It’s been a great match, and she continues to learn and develop as a professional.

“I really love what I’m doing here at Microsoft,” Radoi says. “I love my leaders, and I trust them. That’s huge for me. It’s allowed me to grow and do my best work.”

Having a growth mindset

From the beginning, Radoi stood out for her ability to learn quickly and take on new challenges—even without a traditional technical background.

“Adelina is one of those people that consistently models what we call a growth mindset,” says Simon Price, who works with Radoi as a senior director of IT Field Management in Microsoft Digital. “She’s a resilient, energized individual who is always looking for opportunities to drive improvements. And she sees the wider picture really well.”

Radoi enjoys the collaborative aspect of her job, and she works seamlessly with her both her coworkers in Romania and Microsoft employees in other regions around the globe. Her strong communication skills really allow her to shine in this area.

“Her default mode is to collaborate with others and partner with them; she’s built a strong network in Microsoft Romania but also at a global scale, reaching out and building new connections.”

Simon Price, senior director, IT Field Management, Microsoft Digital

Radoi is a people person who speaks three languages—Romanian, French, and English—while understanding Italian, Spanish, and Portuguese (and is working on her Japanese). These traits and skills have allowed her to make a strong impression across Microsoft Digital.

“Her default mode is to collaborate with others and partner with them; she’s built a strong network in Microsoft Romania but also at a global scale, reaching out and building new connections,” Price says. “Others constantly talk about how great a partner she is, and how she’s willing to speak up and share her perspective.”

One project on which Radoi exemplified this mindset was I Am New, a portal for brand-new Microsoft employees where they can get up and running technically with minimal assistance. She helped come up with the concept and worked on the team that drove the initial development of the portal.

“I’m incredibly proud of I Am New,” she says. “The idea grew from a challenge many of us were seeing across Microsoft: How do we create the best Day 1 experience for new hires? Romania was one of the early voices raising this need, and the feedback we received locally closely mirrored insights from colleagues across other Microsoft countries. What started as a shared challenge became a collaborative solution that now helps new hires feel connected, informed, and empowered from day one.”

Price says that the project showed the kind of outsized impact that Radoi has had in her work.

“It gave her the opportunity to work within a global team and develop a solution that would help every new Microsoft employee,” he says. “She embraced the challenge, partnered effectively with others, and had a large-scale impact on the effort.”

Serving as a mentor and role model

After going through her own challenges as a woman breaking into the IT field, Radoi knew she wanted to help mentor others in the same situation. That’s why she has played a pivotal role in several women-in-tech groups at the company.

“I’ve really enjoyed being a leader in these groups and serving as a mentor for other women,” she says. “As someone who faced unconscious bias in her career, I want to foster a nurturing environment and create a more inclusive workplace. I mentor new graduates and speak at various diversity events to contribute to the growth and empowerment of women in the field.”

One of Radoi’s own female mentors at the company is Silvina Olkies, a senior director of Global End User Support Services and Employee Experience.

“Adelina’s very genuine and brings a real warmth to the workplace. She’s proactive and is always focused on giving back in different ways. And she’s gone through complex challenges and embraced transformation throughout her career.”

Silvina Olkies, senior director of Global End User Support Services and Employee Experience, Microsoft Digital

Olkies has gotten to know Radoi by working with her on various employee resource group initiatives over the years. She admires how Radoi brings her authentic self to her job and strives for human connection and community.

“Adelina’s very genuine and brings a real warmth to the workplace,” Olkies says. “She’s proactive and is always focused on giving back in different ways. She’s gone through complex challenges and embraced transformation throughout her career.”

Olkies remembered fondly that Radoi even composed and performed a special song to celebrate her 25^th anniversary at the company, exhibiting some of her many talents. (Radoi also studied ballet seriously in her youth and credits this experience with contributing to her discipline and work ethic.)

“She’s the only field manager I know that has had an office project going on just about every day of her entire time on the job. Her site has grown almost double in size during her time here, and it’s kept her extremely busy.”

Anders Jepsen, director, Field IT Management, Microsoft Digital

Connecting Microsoft with the world

Microsoft Romania is growing fast, and that has given Radoi plenty of challenges during her first eight years with the company. Part of her role involves coordinating the technology aspects of new buildings, and she has had an extraordinary number of those to handle so far.

“She’s the only field manager I know that has had an office project going on just about every day of her entire time on the job,” says Anders Jepsen, who has been Radoi’s manager since she started with the company. “Her site has grown almost double in size during her time here, and it’s kept her extremely busy.”

As a rising star in the tech field, Radoi has had offers to move to jobs in other parts of the world. It would be a natural step for an ambitious career professional.

“I’m so happy that Microsoft Romania exists. For me, it’s very important to remain in my country. I love my people; I love Romania.”

Adelina Radoi, senior IT field manager, Microsoft Digital

But she’s decided to stay in Romania, where she’s raising her daughter and doing all she can to bring new technologies—such as AI—to her native country.

“I’m so happy that Microsoft Romania exists. For me, it’s very important to remain in my country. I love my people; I love Romania,” she says. “A lot of the best Romanian minds are going overseas. But if we are all leaving Romania, who is going to do something for this country? So I’ve turned down the offers to leave.”

She’s further demonstrated this commitment with her outreach work for Microsoft. She has served as the Microsoft representative at various government meetings and discussion panels in Romania, dealing with issues such as privacy, child safety, and AI. She’s also done podcasts and other engagements.

“She’s constantly looking for new opportunities to widen her scope,” Jepsen says. “Whether it’s traditional customer engagements, diversity and inclusion events, speaking at universities, or other outreach, she’s a role model for the field manager position. Her biggest challenge is knowing her own limits.”

As a mother, the work on child safety and AI is particularly close to Radoi’s heart.

“Children are really curious and excited about AI, and it’s everywhere,” she says. “As a country, I think we need to do more to teach about AI, and to make sure the digital world is safe for our kids. So I’ve gotten involved in that work, brainstorming how to develop the best technology safeguards for our children.”

And her advice when she speaks to young people about careers in technology, or mentors others who aim to follow in her footsteps? She tells them to set ambitious goals and remain true to themselves.

“Follow your heart and instincts—believe in yourself and create your own image,” she says. “Compare yourself with who you were one, two, or three years ago. What you have you learned? How have you improved yourself? If you like the answer, then you are on the right path.”

The post Rising to the challenge: How Adelina Radoi makes a difference in IT at Microsoft appeared first on Inside Track.

At Microsoft, we’ve found that sharing our AI transformation stories—especially how individuals and teams have harnessed the power of AI to address common business, technical, and operational challenges—is the key to accelerating our customers’ AI transformation. As Customer Zero, we test our technology, products, and approaches in-house first, then use the lessons learned to help our customers get the most out of technology.

“AI transformation only becomes real when it becomes part of how work gets done. Our role is to lead with our own experience and share what we’re learning, so our customers can move faster from ambition to execution.”

Lorraine Bardeen, corporate vice president, Microsoft Frontier Company

Working across numerous teams at Microsoft, we’re building a library of reusable evidence and lessons learned. These will enable our customers to go from experimentation to operational impact with greater speed and confidence.

In our experience, progress came from prioritizing the best AI use cases, grounding them in real workflows, and building repeatable patterns that teams could trust. That principle shapes our Customer Zero strategy, which is to bring those patterns together so that customers can learn from the same questions about AI that we’ve been working through internally here at Microsoft, including:

• Where to start
• How to build confidence
• How to govern consistently
• How to turn isolated wins into a sustainable, AI-powered competitive advantage

“AI transformation only becomes real when it becomes part of how work gets done,” says Lorraine Bardeen, corporate vice president of the Microsoft Frontier Company. “Our role is to lead with our own experience and share what we’re learning, so our customers can move faster from ambition to execution.”

This is why our Customer Zero insights are so important: They’re a direct channel for sharing what our teams here at Microsoft are learning as we apply AI in the day-to-day work of sales, operations, supply chain, finance, customer service, software engineering, IT, and other business functions.

Learn more

Microsoft Frontier Company: AI engineering that amplifies and protects your intelligence

Turning learnings into practice

Shortening the distance between strategy and execution for our customers and giving them concrete examples of what scale looks like in practice is a key mission for our Customer Zero team. Our goal is to help readers start with our larger Microsoft AI transformation story and then move to focused examples, role-specific lessons, and practical assets that can be adapted for their own organizations.

The leadership lens is part of what makes our Customer Zero journey valuable, showing customers how organizations can build momentum through funding the right priorities, exercising practical governance, and facilitating change management that helps people adopt new ways of working.

“The most important thing you can do is create a clear, funded set of priorities in an AI operating model,” Bardeen says. “And those priorities need to be supported by human-centered change and adoption.”

Our AI transformation stories make that guidance tangible by illustrating how specific teams at Microsoft approached familiar business problems, what and how they changed, and actionable insights that customers can apply to their own businesses.

AI transformation at scale

The result is an evidence base that shows how we transformed, so you can learn from our journey across all three of the patterns we’ve identified within Frontier transformation:

Pattern 1

Human with assistant

Every employee has an AI assistant that helps them work better and faster

Pattern 2

Human-agent teams

Agents join teams as “digital colleagues,” taking on specific tasks at human direction

Pattern 3

Human-led, agent-operated

Humans set direction and agents execute business processes and workflows, checking in as needed

Across each of these patterns, we seek to answer a critical question: What does AI transformation actually look like when it successfully moves beyond pilots and into enterprise-scale operations?

Here are examples of each of these patterns in action, along with what we’ve learned as Customer Zero in deploying, managing, and leveraging these solutions across Microsoft.

Human with assistant

In Microsoft Customer Service and Support, new technical support engineers no longer have to spend weeks getting up to speed before they can contribute with confidence. Instead, they work on real customer cases from the start, with an AI assistant embedded directly in their workflow. The assistant surfaces relevant knowledge, recommends next steps, and helps guide decision making in the moment. In our Customer Zero pilots, onboarding competency assessments were completed up to 3.3 times faster.

The lesson is simple but powerful: Learning is more effective when it happens in the flow of work, where employees can build skills while solving real problems.

Human-agent teams

Our supply chain planners have traditionally spent hours comparing demand signals, reviewing forecasts, and analyzing scenarios before making decisions. Today, agents automate much of that work. Planners can interact with the system using natural language and rapidly explore different options.

The primary benefit is faster, higher-quality planning decisions. By automatically comparing demand plans, surfacing meaningful changes, and explaining their impact through natural language and visualizations, the agents reduce the effort required to analyze planning data. Planners spend less time gathering and reconciling information and more time evaluating exceptions and responding to changes in demand. Internal telemetry estimates the solution saves up to 80 hours per planning cycle.

Our Customer Zero experience here reinforced that the quality of the user experience matters. Simple changes, like adding richer visualizations, helped make agent-assisted planning easier to understand and encouraged broader use across teams.

Human-led, agent-operated

In Microsoft Finance, AI agents are helping collections teams move faster and make better decisions. Connected to SAP and Dynamics 365, agents can predict late payments, identify potential customer disputes, categorize and summarize cases, route inquiries to the right owner, and provide AI-generated recommendations that help teams focus on the highest-priority work.

By reducing the manual effort required to assess customer accounts and resolve issues, the AI solution has cut case-handling time by 22 percent and reduced customer inquiry-handling times by as much as 60 percent. Collection teams are resolving inquiries up to 2.5 times faster, while improved automation and decision support helps accelerate quote-to-cash processes, contributing to a 48 percent reduction in time from quote to deal close. The result is not just time savings, but faster customer responses, improved operational efficiency, and more capacity for our finance professionals to focus on the activities that have the highest business impact.

Across all these scenarios, a consistent pattern has emerged: The biggest gains come when AI is embedded into established processes, supported by strong governance, and designed around the realities of daily work.

“Our responsibility is to lead by doing—and to share those lessons openly. Customer Zero is how we help our customers turn AI from opportunity into operational reality.”

Lorraine Bardeen, corporate vice president, Microsoft Frontier Company

Moving faster with greater confidence

While our journey is far from over, we’ve already identified numerous practical themes for leaders who are ready to embrace AI transformation within their own organizations. That is the promise of Customer Zero: to share our own operational lessons with customers while those lessons are still timely enough to be useful.

“Our responsibility is to lead by doing—and to share those lessons openly,” Bardeen says. “Customer Zero is how we help our customers turn AI from opportunity into operational reality.”

For organizations trying to move from AI ambition to enterprise execution, the guidance you’ll find here will reduce uncertainty and accelerate progress. Microsoft is still learning, and that’s part of the point. By sharing practical evidence from across our business as it happens, we can help our customers move faster with greater confidence, better context, and a clearer sense of what transformation looks like in the real world.

The post From AI ambition to enterprise execution: Our Customer Zero journey appeared first on Inside Track.

At Microsoft, we’ve been working to close that gap with a new kind of agent: Copilot Cowork. Rather than a strict instruction-and-response approach, Cowork has taken the leap to multi-step action across an employee’s Microsoft 365 environment.

From task assistants to true digital coworkers

For years, the promise of AI at work has centered on task assistance, like creating drafts quickly, delivering better summaries, and providing faster answers. But knowledge retrieval isn’t partnership.

“Cowork really sheds light on the art of the possible when it comes to agents, without the need for employees to build them for themselves. It has ignited people’s imagination around what agents can do.”

Stephan Kerametlian, senior director, Microsoft Digital

In Microsoft Digital, the company’s IT organization, we’ve been pursuing deeper impact for AI. We want agents to work alongside our employees, take on multi-step tasks, and produce real outputs, all while keeping the humans that direct their work in the loop.

Copilot Cowork represents that shift. It behaves less like a chatbot and more like a capable teammate that plans, executes, and checks in as it goes along.

“Cowork really sheds light on the art of the possible when it comes to agents, without the need for employees to build them for themselves,” says Stephan Kerametlian, a senior director in Microsoft Digital. “It has ignited people’s imagination around what agents can do, and it helps them understand that they don’t need to be a developer to get high-quality outputs very quickly.”

Microsoft 365 Copilot agents

Copilot Cowork is just one of the agents available through Copilot. Each is most effective in a specific set of scenarios.

Using Copilot Cowork to move from conversation to action

We created Copilot Cowork for workflows that span multiple steps, people, and applications. While traditional chat experiences answer questions or generate content, Cowork can interpret a request, create a plan, gather relevant context, and carry work forward over time.

“Work IQ packages relevance, ranking, and context into something AI can actually act on. It understands who you work with, what you’re working on, and which content matters in a given situation, helping the AI identify the right material and infer the steps needed to deliver an outcome.”

Swapna Malekar, principal product manager, Microsoft Digital

This agent can develop documents, coordinate meetings, generate research, create web applications, and manage ongoing tasks. It also provides visibility into its progress and requests approval before taking sensitive actions.

Work IQ, Microsoft’s intelligence layer for enterprise context, is the foundation of these capabilities. It helps Cowork understand relevant files, meetings, chats, collaborators, and organizational signals so that the agent can identify the right information for the task at hand. Cowork then uses that context to determine the steps needed to deliver the requested outcome.

“Work IQ packages relevance, ranking, and context into something AI can actually act on,” says Swapna Malekar, a principal product manager in Microsoft Digital. “It understands who you work with, what you’re working on, and which content matters in a given situation, helping the AI identify the right material and infer the steps needed to deliver an outcome.”

Our employees are already using Cowork to tackle work that would otherwise require multiple prompts and applications. With a single request, they can create presentations, establish Teams chats for collaboration, schedule recurring follow-up activities, begin building strategy documents, and more.

Cowork can also pull together emails, chats, documents, and news sources into a briefing or transform existing content into an interactive web experience. Throughout the process, employees can refine the work, answer clarifying questions, and approve actions before Cowork moves forward.

Helping employees embrace this watershed moment

During early adoption efforts for Cowork, we learned that successful usage depends as much on behavior change as on technology. Employees who approached Cowork simply as a better chatbot often saw incremental gains.

“Cowork introduces a new way of thinking about work, where you hand off a complex task, close your computer, and the work continues on your behalf. For adoption specialists, the goal is to help employees recognize where that capability fits naturally into their day.”

Petra Glattbach, senior business program manager, Microsoft Digital

People who learned to think in terms of outcomes uncovered far more value. Instead of asking for a draft, they delegated a process. Instead of requesting a summary, they assigned a research task with a defined deliverable.

Agent Launchpad is an instructional program we’ve designed to develop our employees’ agentic AI skills. This effort, alongside other readiness resources, is encouraging people to identify recurring workflows, experiment with multi-step requests, and refine their collaboration techniques over time.

“Cowork introduces a new way of thinking about work, where you hand off a complex task, close your computer, and the work continues on your behalf,” says Petra Glattbach, a senior business program manager in Microsoft Digital. “For adoption specialists, the goal is to help employees recognize where that capability fits naturally into their day.”

This process isn’t about replacing existing ways of working overnight. It’s helping employees recognize where a digital coworker can reduce manual effort, accelerate execution, and create more time for higher-value work.

Boosting our role as Customer Zero with Cowork

Based on our experience as Customer Zero, the most effective Cowork users start with a real business problem and then explore how an agent can help solve it to drive core business outcomes.

For Jody Ryan, principal cloud solution architect for Microsoft 365 Copilot AI Business Solutions, Cowork quickly became part of her daily workflow. She’s used it to build interactive HTML experiences, create adoption sites, and rapidly prototype customer-facing concepts during live conversations.

“Cowork has become a powerful partner in my day-to-day work, helping me turn information into action so I can be more present, proactive, and impactful with my customers.”

Jody Ryan, principal cloud solution architect, Microsoft 365 Copilot AI Business Solutions

In one scenario, Cowork helped her transform a customer discussion into a working web prototype that she refined in real time based on live feedback. Going from whiteboarding to prototype to Agent was a natural progression that helped her customers visualize the real impact of Microsoft’s agentic capabilities.

One of the greatest surprises for Ryan was how much she enjoyed the customizable approach to human-in-the-loop approval checkpoints. For example, Cowork will find a time for a meeting, build a deck, and draft the invite email, but then pause for her review before sending it out. It’s all about identifying patterns of work and enabling Cowork to be part of them.

“Cowork has become a powerful partner in my day-to-day work, helping me turn information into action so I can be more present, proactive, and impactful with my customers,” Ryan says.

Employees in all kinds of roles across Microsoft are echoing Ryan’s experience. People are feeling the genuine evolution that agents like Cowork represent.

The skills we’ve learned over the last three years have been leading to this moment. By demonstrating the tangible impacts of AI through adoption initiatives, celebrating wins, and setting employees free to explore and create AI solutions to business challenges, we’ve positioned ourselves to capitalize on this next leap into more advanced AI tools.

One of our most important lessons has been that cultivating an AI-ready workforce throughout our Frontier Transformation journey has built a sense of confidence and capability with AI. Now that true agentic partnership is a possibility, that journey has prepared our people to get the most value out of tools like this.

The next chapter in the agentic workplace

The response to Copilot Cowork within Microsoft has taken us through an inflection point where AI has moved beyond assistance and into genuine execution. Our internal adoption efforts clearly demonstrate that shift.

“When agents can reason over organizational knowledge and then take action on our behalf, they become a powerful force for productivity and a critical aspect of how we lead Frontier Transformation.”

Brian Fielder, vice president, Microsoft Digital

Within three weeks of its internal release, Cowork had 20,000 users. Employees are actively exploring new ways to use the agent, from streamlining meeting preparation and follow-up work to creating content, conducting research, and managing complex projects.

We’ve learned that different teams often have different uses for different agents. But Cowork is emerging as a tool that can accommodate efforts reaching across a wide array of apps, data sources, workflows, and scenarios.

That’s a big shift, and employees are excited. Interest has been so strong that we’re expanding our enablement efforts, including new, Cowork-focused learning experiences within Agent Launchpad.

As we continue to evaluate Cowork, product feedback is helping us improve reliability, strengthen connections to enterprise data and external systems, and refine the quality of outputs. Even though it’s still in the early stages, teams are finding that Cowork can reduce administrative burden and help work move faster.

“Work IQ is helping unlock a new era where AI can understand the context behind our work, not just the content,” says Brian Fielder, vice president of Microsoft Digital. “When agents can reason over organizational knowledge and then take action on our behalf, they become a powerful force for productivity and a critical aspect of how we lead Frontier Transformation.”

For us at Microsoft, Cowork is more than a new agent. It’s providing a glimpse into a new future of work, where digital coworkers help employees focus more of their time on the aspects of their job that matter most.

The post From AI assistant to capable teammate: How Copilot Cowork is changing the way we work at Microsoft appeared first on Inside Track.

Once captured, meeting data becomes a source of organizational knowledge that helps employees catch up on discussions, revisit decisions, track commitments, and surface unresolved issues. AI-powered experiences such as Microsoft 365 Copilot and Work IQ can connect insights across meetings, helping teams understand how conversations, decisions, and workstreams evolve over time and ensuring important information doesn’t get lost.

Although these features have proven to be incredibly useful to our employees and our wider organization, there are also concerns about how retaining Microsoft Teams meeting data and AI insights might affect our security posture, records retention policy, and privacy. Just like any other company, we at Microsoft must balance these different factors accordingly.

At Microsoft Digital, the company’s IT organization, we’re leading cross-disciplinary conversations on this topic to help ensure that we get it right.

The value of Teams meeting data

Within a meeting, the Microsoft 365 Copilot sidebar experience helps our late-joining employees catch up on what they’ve missed, provides intelligent prompts to review unresolved questions, summarizes key themes, and creates notes or action items.

“The value of a meeting should not end when the meeting ends. Transcription and AI help transform conversations into durable knowledge—making decisions easier to find, commitments easier to track, and information easier to access for everyone who needs it.”

Chanda Jensen, senior product manager, Microsoft Digital

The benefits of AI in meetings extend beyond the live meeting experience as well. When meeting content is available after the meeting, AI can transform conversations into accessible organizational knowledge. Transcripts and underlying documentation—including, notes, decisions, and action items—enable participants to revisit discussions, catch up on missed meetings, verify decisions, and accelerate follow-up work.

Our Microsoft Teams meeting data retention efforts focus on three key categories of artifacts: Underlying documentations, transcripts, and the AI-generated artifacts that help power Microsoft 365 Copilot and Work IQ experiences.

Transcription provides the underlying documentation that makes these AI-powered experiences possible. By making transcription and AI capabilities available in meetings while preserving organizer, administrative, compliance, and sensitivity controls, organizations can choose how these capabilities are used while enabling users to benefit from more effective collaboration and knowledge retention.

“The value of a meeting should not end when the meeting ends,” says Chanda Jensen, senior product manager in Microsoft Digital. “Transcription and AI help transform conversations into durable knowledge—making decisions easier to find, commitments easier to track, and information easier to access for everyone who needs it.”

Policy considerations for meeting data retention

The value of these tools is clear, but data-retention obligations also play an important compliance role that organizations like ours need to consider.

“When individuals generate recordings or other artifacts during meetings, we tend to think of them as an individual’s data, but they actually represent the company’s data. We want to empower individuals, but we have to remember the retention and volume impacts of these artifacts on the company can be substantial.”

Rachael Heade, director of records compliance, Microsoft Corporate, External, and Legal Affairs (CELA)

First, producing and retaining this kind of data can be complex if it isn’t governed properly. For us at Microsoft, this data represents day-to-day general business practices that elevate productivity, and factors such as security and privacy must be considered when managing it. Second, data-rich artifacts like video recordings require a lot of space, quickly eating up cloud storage budgets.

“When individuals generate recordings or other artifacts during meetings, we tend to think of them as an individual’s data, but they actually represent the company’s data,” says Rachael Heade, director of records compliance in Microsoft’s legal division. “We want to empower individuals, but we have to remember the retention and volume impacts of these artifacts on the company can be substantial.”

In light of these potential impacts, some organizations simply opt out of enabling Microsoft Teams meeting recordings.

Asking the right questions to assemble the proper guardrails

Leaders in Microsoft Digital and Corporate, External, and Legal Affairs (CELA), our legal division, are working to balance the benefits of Microsoft Teams meeting data retention with our compliance obligations, aiming to provide empowering experiences for our employees while also keeping company data safe.

“Organizations are always concerned about centralized control over the retention and deletion of data artifacts,” Heade says. “You have excited employees who want to use this technology, so how do you set them up so they can use it confidently?”

Like many policy conversations, getting this right starts with the governance team in Microsoft Digital and our internal partners asking employees from across the company who are responsible for data governance the right questions:

• When should a meeting be recorded and when shouldn’t it?
• What kind of data gets stored?
• Who can initiate recording, and who can access it after the meeting?
• How long should we retain meeting data?
• Where does the data live while it’s retained?
• How can we control data capture and retention?
• What does this mean for eDiscovery management?

These questions help us think about the proper data-retention guardrails. Our IT perspective is only one part of the puzzle, so we’re actively consulting with CELA, corporate security, privacy, the Microsoft Teams product group, the company’s data custodians, and our business customers throughout this process.

“As an organization, this is about thinking through your tenant position and getting it to a reasonable state.”

David Johnson, tenant and compliance architect, Microsoft Digital

Our conversations have brought up distinctions that any organization should consider as they build policy around Microsoft Teams meeting retention:

• The length of time a meeting’s data remains fresh, relevant, or useful
• Consideration of the difference between AI-generated archival content versus the full meeting transcript
• The different risks inherent with recordings compared to transcriptions
• Establishing default policies while allowing limited variability and flexibility when employees require it

“As an organization, this is about thinking through your tenant position and getting it to a reasonable state,” says David Johnson, tenant and compliance architect in Microsoft Digital.

From sharing perspectives to crafting policy

Our policies around Microsoft Teams meeting data retention continue to evolve, but we’ve already implemented some highly effective practices, policies, and controls. Every organization’s situation is unique, so it’s important that you speak to your legal professionals to craft your own policies. But our work should give you an idea of what’s possible through the out-of-the-box features within Microsoft Teams.

The policies we’ve put in place represent a mix of technical defaults, meeting options, and empowering employees to make informed decisions about usefulness and privacy. They also build on the foundations of our work with sensitivity labeling, which helps secure data across our tenant.

Here are some of the practices we follow and features we use:

• Transcript attribution opt-out gives employees agency and reassures them that we honor their privacy.
• Recommending that employees “tell and confirm” before recording empowers and supports our people to speak up when they don’t believe the meeting should be recorded or don’t feel comfortable with this choice. Employees in the meeting can also stop the recording, if needed, or determine if automatic recording was set up but is not appropriate.
• Visual indicators that a meeting is being recorded and that transcription has started, allowing users to request that a meeting stop being recorded or to leave the call.
• User education, through an internal recording smart-use statement document, helps employees understand the implications of recording, when not to record, and when not to speak in a recorded call.
• We do not use compliance recording. While compliance recording could enforce full consent collection, unmuting themselves, we decided that opt-outs and user notices provided sufficient agency to our employees.
• We offer meeting labels that limit who can record, meaning only the organizer or co-organizer can initiate recordings for meetings labeled “highly confidential.”
• Meeting labels are informed by content shared within the meeting. If content is shared in the meeting that has a higher label than the meeting itself, the organizer is prompted to re-label it.
• Meeting labels are inherited and applied to all meeting artifacts, recordings, transcripts, and notes. This means that meeting knowledge remains protected, and any AI consumption of that recording will automatically inform the consumer of the sensitivity and required protections.
• Only meeting organizers can download meeting recordings, keeping the meeting data contained and restricting sharing.
• The default OneDrive and SharePoint meeting expiration is set to 90 days to ensure we minimize the risk of data leakage or cloud-storage bloat.
• The default Meeting AI Archive is set to an 18-month retention policy. That allows questions and decisions from the meeting to be leveraged by the team for post-meeting insights but ensures that data is not kept forever.  
• Deletion is applied in a consistent manner under the business general categories of our retention schedule. The schedule supports our designation of the Teams artifacts as productivity tools and resources, but not as official company records. This stance controls data volume, reduces review and production burden, and ultimately reduces risk (including security and privacy factors).

Balancing productivity with sensible data governance

At Microsoft, we apply different retention periods to different types of meeting data, based on their purpose and business value. Full meeting recordings and transcripts are governed by a default 90-day expiration policy, helping reduce privacy, security, and storage risks while ensuring employees can still benefit from recordings in the near term.

“The bottom line is that we rely on our employees to be good stewards of the company. Because we’ve got a good governance model in place for Teams and solid overall hygiene for our tenant, we’re well set up to deal with the evolution of the product and make these decisions.”

David Johnson, tenant and compliance architect, Microsoft Digital

Separately, AI-generated meeting knowledge—such as questions, decisions, and other insights extracted from meetings and used to support discovery and knowledge-sharing—can be retained for up to 18 months, allowing teams to benefit from the value of those insights long after the original transcript has expired.

These policies are designed to balance employee productivity with responsible data governance, ensuring that important information remains available when useful but is not retained indefinitely. They reflect the three core tenets we use to inform our governance efforts: empower, trust, and verify.

“The bottom line is that we rely on our employees to be good stewards of the company,” Johnson says. “Because we’ve got a good governance model in place for Teams and solid overall hygiene for our tenant, we’re well set up to deal with the evolution of the product and make these decisions.”

The net outcome of all of this work is that our organization is more confident in our approach to meeting knowledge, resulting in more meetings being recorded or transcribed and generating more valuable post-meeting artifacts.

We can’t specifically recommend that an organization follow our blueprint entirely, but asking questions similar to the ones we’ve outlined here can help you build a strong Teams data-governance foundation. With a firm grasp of the technology and close collaboration with key stakeholders, you can guide your own policy decisions and unlock more value for your employees.

The post Empowering employees after the call: Enabling and securing Microsoft Teams meeting data retention at Microsoft appeared first on Inside Track.

“To bring us together, we definitely got creative after COVID, and even more so when AI came in. We did a lot of fun team games where we would share and represent our region.”

Petra Glattbach, senior business program manager, Microsoft Digital

What began as a creative experiment to reconnect people in the post-pandemic era quickly became a way for globally distributed employees to reconnect with one another after years apart.

The comic book—the brainchild of Petra Glattbach, a senior business program manager based in Australia—grew out of a broader effort to rebuild personal connections across a region spanning Australia, New Zealand, Japan, China, Singapore, and India.

“To bring us together, we definitely got creative after COVID, and even more so when AI came in,” Glattbach says. “We did a lot of fun team games where we would share and represent our region.”

For many years before the COVID-19 pandemic, these teams had maintained a strong sense of community through regular regional gatherings and global conferences, creating a rhythm of in-person collaboration and camaraderie that they deeply valued.

Pre-pandemic, companies around the world hosted in-person events to keep their networks strong and make new connections. Remote work was an outlier, and most businesses operated on a face-to-face basis. COVID and its accompanying restrictions transformed professional interactions around the world, platforming remote work as the norm.

Microsoft was no exception.

Maintaining connection in a distributed world

Microsoft 365 Copilot was being rolled out during this time, and employees were encouraged to incorporate it into their daily workflows. Like many people across different businesses and industries, having our employees incorporate AI into daily work was equal parts exciting and anxiety-inducing for the Asia teams.

The employees in the Asia region thus found themselves trying to solve two challenging problems: How do you foster real workplace community when in-person meetups are infrequent and difficult? And how do you build your confidence with AI tools in the workplace? Maybe, they thought, there was a way to tackle both challenges at the same time.

The Asia teams initially focused on the community-building side of the quandary. They started small, organizing gatherings like themed quiz games, cultural trivia, and other online events designed to bridge the gap between the virtual and real worlds.

In the spirit of our company’s AI rollout, the Asia region also began experimenting with different forms of AI. They initially used it for more organizational tasks, such as event brainstorming and planning. However, once they began experimenting, it occurred to them that more creative, community-driven projects might be the perfect opportunity to encourage people to learn about AI while having fun and building closer relationships between geographically separated teams.

“What it really did for us was normalize the idea of using AI. It allowed us to move culturally from trying to use this new technology to do our jobs to the idea that it can help us in many different ways.”

Alan Stone, senior director of business programs, Microsoft Digital

The advent of Hello There

The teams started with some small-scale creative team games and experiences, such as making songs and short videos that represented their different cultures. For instance, every six months one of our teams would run an event they called “Hello There.” They’d randomly assign three people from different parts of the world to find a half-hour in their week for a conversation, and suggest a topic for the group to chat about.

At a time when the company was no longer regularly flying employees together in person, the initiative helped recreate the spontaneous personal connections that had once happened naturally during regional events and conferences. They then asked the participants to use AI to generate an image that told the story of the things they spoke about, which they posted in a dedicated Viva Engage channel.

The generated images were fun for everyone to see and comment on, and they helped employees learn more about one another. The experience gave globally distributed employees an opportunity to connect not just as coworkers, but as people, despite the distance between them.

“What it really did for us was normalize the idea of using AI,” says Alan Stone, senior director of business programs in Microsoft Digital. “It allowed us to move culturally from trying to use this new technology to do our jobs to the idea that it can help us in many different ways.”

Using AI for a creative group project

After a few regional cycles of iterative experimentation with these creative capabilities, our teams in Asia decided to use AI for a more ambitious group project—something that would require teams from multiple countries to work together toward the same goal. They wanted to create an item that represented the Asia region, celebrated their unique cultural attributes, and boosted the AI literacy of their employees.

That’s where Glattbach’s comic book idea came in. Inspired by the popularity of manga and graphic storytelling across Asia, she had the idea of showcasing both the technological capabilities and powerful collaborative nature of AI by creating a comic book using AI-driven tools.

She proposed the project in a meeting with Stephen Kerametlian, a senior director of business program management in Microsoft Digital. Knowing his love of James Bond films, his team created a comic book of Kerametlian as a Bond character, flying to visit employees in countries around the region. Kerametlian says receiving a personalized, AI-generated comic book from team members he hadn’t physically seen in years was a defining and personally touching moment.

“They even managed to get my wife and two-year-old son into the book,” Kerametlian says. “We’re an international team. Through that comic book, I was able to travel the world and ‘see’ my entire team, which I haven’t seen in person since before the pandemic. We had so much fun with it.”

Glattbach’s idea was then translated into a wider-reaching effort across the different teams based in Asia. Groups in different countries would each create a chapter of the shared narrative. Two regional leaders, Alan Stone, and Wai Leong Chan, were main characters in the story, traveling across Asia in a hot air balloon and touching down in each country to visit the team and learn about their culture.

The project enabled team members to learn to use AI while completing a fun and creative effort together. Regional leaders created a purposefully safe, low-stakes environment to encourage experimentation and play.

“It was an opportunity for people have a little bit of fun, knowing they’re experimenting and learning in a safe environment,” says Jane Davis, a director of business programs in Microsoft Digital who’s based in Australia. “I love how easy it was for everybody to get involved and experiment with AI as a shared community.”

The teams prioritized joy and creativity over the final output, encouraging people to work together and fostering an environment where they could learn from one another. Each team shared prompts, compared results, and built on others’ ideas to come up with their country’s chapter of the comic book.

“We unlocked a ton of usage and value from Copilot through something as simple as a comic book. It turned out to be very powerful, and inspired people to use the technology for their own work as well.”

Stephan Kerametlian, senior director of business program management, Microsoft Digital

This larger comic book project was a runaway success. It became a companywide example of successful peer-to-peer learning, cross-cultural exchange, and creative collaboration.

“We were able to rally communities around it; we didn’t have to beg people to join the work sessions, because they were super-popular,” Kerametlian says. “We unlocked a ton of usage and value from Copilot through something as simple as a comic book. It turned out to be very powerful, and inspired people to use the technology for their own work as well.”

Participation grew rapidly; employees were genuinely excited to get involved.

“AI is strengthening our day-to-day productivity,” says Shunsuke Kubota, a senior field IT manager in Microsoft Digital, who is based in Japan. “We’re generating more connections, resulting in more sharing of ideas. I think it’s kicked off an incredible creativity and innovation loop.”

And that’s a win for everyone at Microsoft.

“People think that AI is replacing human connections. We purposely chose to think about creative ways that our community can use AI as an enabler of human connection across geographies in Asia, especially since Microsoft Digital is a global team.”

Jane Davis, director of business programs, Microsoft Digital

Future collaborations with AI

The success of this AI collaboration project has inspired teams across Microsoft to adopt a similar approach to community building, including groups in Europe and the Middle East.

“People think that AI is replacing human connections. We purposely chose to think about creative ways that our community can use AI as an enabler of human connection across geographies in Asia, especially since Microsoft Digital is a global team,” Davis says. “We’re asking, how do you come together as a community? How do you do it in a virtual way, in a way that enables social and experimental learning?”

As these efforts illustrate, when used intentionally and purposefully, AI technology is a powerful tool for amplifying and enhancing human ties in a widely distributed world. As the technology continues to advance, we believe it offers an opportunity to rethink how people connect, learn, and grow together.

“AI is something that’s driving community,” Davis says. “People are discovering these new, innovative opportunities to connect, and AI is their creative partner in making it happen.”

The post How Microsoft Digital Asia used AI to create human connection across continents appeared first on Inside Track.

At Microsoft, that question emerged from a much larger challenge: How to rapidly upskill our customer and partner-facing teams in a way that is scalable, role-specific, and grounded in real business conversations. To bring this to life, we focused on how our sellers could practice and apply these skills in real-world scenarios.

One example of this approach is Agent J.ai. This agent is an on-demand, AI-powered coach we designed to enhance the skills and performance of 64,000 of our Microsoft Commercial employees serving in sales roles. And it’s not just any AI-powered coach—it’s modeled off of Judson Altoff, CEO of our commercial organization.

For our sales, partner, and service teams, this project represented a shift in how we approach skilling internally. We’re not just delivering training on Microsoft AI solutions; we’re using AI itself to power how those skills are learned in practice.

At Microsoft, this is what Customer Zero looks like in real-world scenarios—using our own AI solutions to solve a core business challenge: Helping customer-facing teams build skills for real customer and partner conversations at scale.

We’re building and testing AI tools inside the organization, observing what consistently works, and sharing practical insights with our customers.

Learn more

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

Upskilling employees on Microsoft AI solutions

Upskilling thousands of employees at scale—and giving them customized instruction that fits their specific needs—is a tremendous challenge for any organization. Teams have limited time, and there’s the issue of making sure the content is up to date and in line with organizational strategy. These teams also need to access accurate, specific information across a vast content repository.

Our internal AI Transformation team had already embarked on a global campaign to educate and train our customer and partner-facing teams on Microsoft AI solutions. They had developed job aids, demos, and videos tailored to sales scenarios like account planning, opportunity qualification, and customer meeting prep. They’d also launched campaigns, Learning Days, and contests like “The Road to 60,” which encouraged teams to reach 60% daily active usage of our Al solutions.

These initiatives showed positive results, with a 68% boost in customer planning efficiency for an account executive, and a 62% gain in agile workflows for a business program manager. But the teams also realized that AI itself could help scale and customize this training even further. That’s where Agent J.ai came in.

Upskilling with a familiar AI coach

According to Stacey Herod, a senior learning manager here at Microsoft, project members gathered focus groups of employees in varying roles across the commercial organization to help understand the issues that customer and partner-facing teams face. They pinpointed challenges with speaking the language of the customer, establishing executive presence, connecting solutions to business outcomes, and understanding how to best serve as a trusted advisor to customers.

The team wanted to develop a coaching experience that could simulate real customer conversations and reflect how strong sellers actually operate in the field.

At the center of that experience is a familiar voice modeled after Althoff. For customer and partner-facing teams, Althoff’s approach represents how experienced leaders define the vision and share deep expertise that speaks to our customers’ needs.

“It started as a fun experiment—replicating Judson as a mini version of himself, where you could always have him by your side, coaching you in real time,” Herod says. “But it quickly revealed an opportunity to scale executive-level guidance. Now, customer and partner-facing teams can bring their own scenarios and engage with a trusted voice—making learning more personal and impactful.”

Instead of a one-to-many model, we can now interact with and learn from that perspective on demand.

Learning isn’t about just accessing information; it’s about understanding how experienced leaders navigate conversations, connect solutions to business outcomes, and build trust with customers and partners. Modeling the experience on that perspective helped translate those behaviors into a scalable, practical format.

Althoff was fully onboard with the project and closely involved with its creation, adhering to the company’s principles around responsible and ethical AI use. We also knew that having executive buy-in and sponsorship is critical when it comes to increasing adoption of tools like Agent J.ai across the company.

“We want people to feel comfortable practicing conversations with artificial intelligence, so they can refine their approach and be prepared ahead of a customer meeting, not practicing with their customers.”

Stacey Herod, senior learning manager, Microsoft

Rethinking how skills are built with AI

At Microsoft, this shift is already underway. Agent J.ai is part of a broader ongoing effort to rethink how skills are built with AI, and our employees are trusting the experience. Importantly, conversations that our employees are having aren’t tied to individual identities, rather the content is curated to the specific needs around upskilling employees. That sense of privacy made it easier for sellers to engage openly and use it without hesitation.

“There’s behavioral change happening at the agentic level—people’s level of comfort using agents,” Herod says. “We want people to feel comfortable practicing conversations with artificial intelligence, so they can refine their approach and be prepared ahead of a customer meeting, not practicing with their customers.”

Practice is central to this model.

“Agent J.ai fundamentally changes skilling—from static training to dynamic, scenario-driven coaching. It adapts in real time to each seller’s context, enabling hundreds of roles and limitless customer scenarios to be addressed instantly—something our industry has never achieved at this scale.”

Jennifer Wheeler, senior learning manager, Microsoft

Role-playing helps simulate realistic, highly customized conversations that customer and partner-facing teams might have with a certain type of client or executive. For example, an account executive might need to prepare for an upcoming conversation with a chief security officer.

Our AI coach’s training must be able to recognize that context.

“Agent J.ai fundamentally changes skilling—from static training to dynamic, scenario-driven coaching,” says Jennifer Wheeler, a senior learning manager at Microsoft. “It adapts in real time to each seller’s context, enabling hundreds of roles and limitless customer scenarios to be addressed instantly—something our industry has never achieved at this scale.”

An AI agent trained on curated content, Microsoft’s customer engagement and coaching frameworks, and a trusted persona could finally deliver what teams lacked—authentic conversation practice tailored to real scenarios, industries, and executive audiences.

Launching and refining our AI coach

We rolled Agent J.ai out quickly, with a focus on iterating based on real-world use. From the start, members of the cross-functional team prioritized capturing feedback and continuously refining the experience based on how sellers use the tool in practice.

“We are seeing the rise of voice-first subject matter expertise agents across industries, and Agent J.ai has demonstrated what’s possible in the Frontier.”

Max Felker, principal product manager, Microsoft

The team began the Agent J.ai project in earnest in March 2025 and shipped the first version just five months later in July. Input from super users helped test specific scenarios, while broader anonymous forms surfaced key insights.

One example: In the user experience, we discovered that using overly realistic or animated avatars for the AI coach could be perceived by users as gimmicky and might prove more expensive to produce. A simple photo avatar ended up doing the trick.  

As a voice-based coaching agent, Agent J.ai is designed for spoken, two-way conversations with users.

“We are seeing the rise of voice-first subject matter expertise agents across industries, and Agent J.ai has demonstrated what’s possible in the Frontier,” says Max Felker, principal product manager for Microsoft.

Incorporating updated industry trends and live events

Staying current is critical for customer-facing teams, especially when conversations shift quickly based on new announcements and industry trends.

“I had a couple of people who said, ‘This is incredible. This saved me hours, or weeks, and I was able to have it in 5 minutes.’ That’s something we never thought would be possible.”

Stacey Herod, senior learning manager, Microsoft

Typically, pulling information together for real customer conversations took time. Powered by Microsoft Azure and Azure AI, Agent J.ai can incorporate announcements from events like Microsoft Ignite, so the AI coach could help prepare sales teams for questions about the latest news.

“I had a couple people who said, ‘This is incredible. This saved me hours, or weeks, and I was able to have it in 5 minutes,’” Herod says. “That’s something we never thought would be possible.”

Others reported saving up to 50% of their standard prep time, not to mention more than $80 million in deals that were influenced through refined strategy and approach.

In the end, the team was laser-focused on making something valuable and accessible for their sales teams.

“If the Agent wasn’t useful, then our learners were never going to come back and use it,” says Herod, a seller in the past herself. “You have one shot to impress sellers. We’re a tough community.”

The post How Microsoft built an AI coach to scale personalized sales training appeared first on Inside Track.

During in-person briefings, I often remind our customers that generative AI systems like Copilot and Copilot Cowork are still quite new, and that they upend decades of human-computer interaction patterns. For over 40 years, we’ve trained knowledge workers to memorize patterns in the GUI to support their productivity.

With Copilot and agents, you can type nearly anything as a prompt and instantly get a detailed response, or create an agent to autonomously complete a business process on your behalf. It’s a very new way of working, and it’s no surprise that many knowledge workers—and even engineers themselves—are still adapting.

More than anything, our customers are looking for one key insight: They want to know how we measure and define the business value of Copilot and agents at Microsoft, especially through the lens of increased productivity and cost savings. After all, AI systems represent a significant investment for our customers, and leaders want to have confidence that they’ll yield sufficient ROI to justify the cost, knowing that the payback period isn’t immediate.

Six steps to proving value

So, how do you prove the value of AI-powered tools in the enterprise? There are six main steps that I share with customers that I believe are critical, based on the empirical evidence we’ve gathered in Microsoft Digital.

Our big mistake? We didn’t instrument all the arduous business processes that slow us down and impact effectiveness before our deployment, so that we could more easily prove the value of AI across our enterprise after deployment.

Here are further details on the six measurement steps to keep in mind:

1. Before you deploy, talk to people who are doing the frontline work in your organization. These should be hands-on managers or influential individual contributors (ICs), not executives or mid-level managers who are abstracted away from the work (although these individuals can be useful in helping identify the right SMEs to talk to).

For each role, identify three to five everyday pain points that could benefit from Copilot or agents. These could be operational, business, or technological processes, all of which can be improved by thoughtful applications of AI. This phase provides a great chance to use Six Sigma skills or other continuous improvement (CI) methodologies to evaluate your processes and identify waste, then consider how a combination of CI and AI could make them more efficient.

2. Once identified, instrument and measure each of the processes to get baseline data on the average time it takes to complete them. Then the hard work begins—you need to think deeply about how AI could make those processes better. This could be through defining a series of step-like prompts that take away the toil. It could be through further AI-powered automation to make some of the steps go away. It could be applications of AI that validate outputs to ensure that rework is minimized. More and more, it could be an agent that completes the work on the employee’s behalf.

If you lack detailed telemetry for the process in question, you have two options. The first is preferable: Build end-to-end telemetry, so you have observability throughout the process in question. While this is always the best way to reliably measure productivity gains at scale, there is also a second option: Identify a cross-section of ICs and measure them the old-fashioned way as they complete the process in question—with a stopwatch. Take the average of several people to get a better sense of how much time is typically needed to complete the task. Then consider how AI could make that process more efficient, while also improving the resulting business outcomes.

3. Concurrent with your investigation into ways that AI can improve productivity and reduce toil, you need to invest in enterprise AI skilling, ideally by role. The fact is, engineers are going to use AI differently than operations staff, who are going to use it differently than sales and marketing pros. Yes, there are common skills for each, but the best training is tailored to the role, grounded in the experience of using Copilot or agents to address challenges or opportunities that commonly arise in their day-to-day work.

It’s also important to ensure your employees understand the strengths and weaknesses of current AI models. No model is perfect, and understanding where reasoning errors can occur will help them avoid accepting AI-generated output that may be inaccurate. Human discernment and observability is a critical step in validating AI outputs.

A best practice is to gate access to generative AI; require employees to engage in both general and role-specific training prior to their provisioning to ensure they immediately unlock value in their work.

4. After you’ve trained your employees and given them the skills to succeed with AI and agents in the enterprise, begin your deployment in earnest. For tools like Copilot, we recommend deploying in cohorts by role, focusing on employees who will immediately see the greatest benefit. At Microsoft, we started with our sales team and then gradually deployed to the remaining employee population over the course of several months. This enabled us to scale up our skilling programs, governance strategies, and support function in anticipation of increased volumes. For agents, deploy to support the biggest pain points first, then monitor and observe agent performance before scaling to additional business or operational processes.
5. Post-deployment, give it a few weeks, then return to those same processes you identified in the first step and measure the average time savings. If time savings aren’t as significant as you hoped with either Copilot or agents, go back to process evaluation and employee skilling and continue to refine your approach.

This whole process is intentionally iterative—you’re not likely to get it exactly right on the first attempt. But if a process that used to take 30 minutes can now be completed in 10, you’ve obviously made a big difference that will save a lot of time when extrapolated across an entire fiscal year. Even better if that end-to-end process is now being completed by an autonomous agent, with a human reviewing and validating the output.

After you’ve succeeded in one domain, identify different role leaders and find another batch of processes that could be improved with a combination of continuous improvement and AI. There’s really no limit to the scope of ways you can positively impact your employees if you maintain a sharp focus on continually improving their experience with Copilot and agents.

6. The final step: Reclaim the value from those productivity savings and apply it to new business challenges or opportunities. Let’s be clear—most enterprises aren’t investing in AI solely to give their people back time. They’re trying to do more with less, enabling their workers to be more productive and generate more value for the company.

In this crucial final phase, you need to be thoughtful and deliberate about how you’ll use the time saved with AI. If your people are saving two hours per week on average, how will they use that time? There are innumerable ways you could redeploy that time to address new business challenges or opportunities. The key is to be intentional in maximizing value, so it aligns with your team and your company’s goals and ambitions. Then report those savings to the appropriate members of the leadership team, to help them understand how their AI investments are paying off.

There you have it—you now possess the tools to quantify the value of your generative AI investments in the enterprise, using them to recapture value and drive more business impact thanks to the power of Copilot and agents.

Learn more

Dive deeper into this topic with a framework that our organization put together to measure the impact Copilot is having at Microsoft.

Boosting AI adoption with structured change management

As you can see from the graphic above, it’s important to surround the AI value measurement process with structured change management and ongoing employee skilling. In Microsoft Digital, we’ve learned that even the most useful or intuitive technologies won’t see widespread adoption without a deliberate and sustained change management effort that’s localized to meet the disparate needs of a global organization.

In our case, that meant cultivating and supporting a community of Copilot Champions who have become the backbone of our global change management strategy. This community is now 10,000+ strong and even has its own Viva Engage forum, where our Copilot enthusiasts answer employee questions and share useful prompts.

Unfortunately, just doing AI skilling once won’t be enough. The pace of change with Copilot and agents is simply too great to do one training effort and then move on. Building an AI-forward culture takes time, and ongoing opportunities to learn and improve skills are one of the best ways to help your employees build the AI habit.

The Microsoft 365 Admin Center has an “AI adoption score” that can help you see—by cohort—how successfully people are building that habit. Having employees who use Copilot three times per week in any way is enough to build an AI-focused workforce, enabling your company to unlock the value of AI in the enterprise.

The promise of generative AI is significant, and there’s no doubt that you’ll see qualitative benefits in your organization even if you don’t instrument every process. But in an era of tight IT budgets, having the quantitative data necessary to calculate the ROI of your investments in Copilot and agents will make it far more likely that you’ll get financial support from your executive team to deploy and succeed at scale.

The post Unlocking the value of Microsoft 365 Copilot and agents at Microsoft appeared first on Inside Track.

This makes accurate content essential. If our content is stale, even the best agent or search engines will return wrong answers, which is frustrating to everyone.

“Knowledge management today is about making sure people can find the right answers the moment they need them. When knowledge stays current, employees get unblocked faster and productivity improves, making the overall support experience far more efficient.”

Silvina Olkies, senior director, Service Management, Microsoft Digital

That means the knowledge bases that get tapped for answers need be accurate, and for that to happen, they need to be updated frequently and without delay.

Internally here at Microsoft, that’s where our team got involved.

We’re Microsoft Digital, the company’s IT organization, and our team saw an opportunity to use AI to dynamically and proactively update our knowledge management systems.

Our first step was—in partnership with our Global Help Desk—to strengthen our self-serve help and reduce the number of steps users need to take to find the right answers. It’s a process that many of our own customers can apply to their knowledge management transformation.

“Knowledge management today is about making sure people can find the right answers the moment they need them,” says Silvina Olkies, a senior director of Service Management in Microsoft Digital. “When knowledge stays current, employees get unblocked faster and productivity improves, making the overall support experience far more efficient.”

The challenge: Fragmented knowledge, manual reviews

For our Global Help Desk, the challenge wasn’t just the volume of content, but also its condition. Support knowledge is spread across thousands of self-service articles, agent-facing systems, and SharePoint sites, all constantly evolving.

“If the knowledge isn’t accurate and current, the experience breaks down immediately. Bad content leads to bad answers.”

Kevin Verdeck, senior IT service manager, Microsoft Digital

In today’s fast-changing AI-powered world, it doesn’t take long for knowledge to become incomplete, out of date, or redundant. This shows up in the inaccurate answers employees might receive.

In an environment increasingly powered by search and AI, weak knowledge equals weak results.

“If the knowledge isn’t accurate and current, the experience breaks down immediately,” says Kevin Verdeck, a senior IT service manager in Microsoft Digital. “Bad content leads to bad answers.”

At our Global Help Desk, keeping that content current required a manual review process.

Our teams analyzed usage data, depended on support agents to report missing or outdated content, and worked through recurring review cycles that relied on subject-matter experts to help confirm whether articles were still accurate. This took significant time and coordination, and even then, some issues were identified only after employees had already hit a dead end.

The result was a system that was reactive and hard to scale.

When employees couldn’t find answers, issues were pushed to advanced support. Poor knowledge quality created poor outcomes, while those responsible for fixing it were struggling with maintaining it.

“One five-member team was reviewing 1,900 self-service KB articles and 1,700 agent-facing KB articles every six months, and that didn’t even include the many SharePoint sites,” Verdeck says. “It was basically their full-time job doing regular reviews.”

Turning raw data into knowledge

Our team in Microsoft Digital set out to build AI for Knowledge Management, a centralized system that could scale across multiple repositories, cut the manual work of keeping content current, reduce reliance on busy content owners, and prepare knowledge for people and AI to use.

“When you have a large volume of data, it’s a silent gold mine. The sheer brilliance lies in taking that data, making it sing, and letting it tell you exactly where the treasure is.”

Ankit Guddewala, software engineer II, Microsoft Digital

An AI pipeline solution made sense because we wanted to fix the issue at scale.

The real opportunity was to turn every resolved support ticket into a signal that looked at what the employee was asking for (nature of the issue or request), whether the answer was already documented, and how the AI and human agents handled it. So, we began with our large amounts of support ticketing data and systems as the starting point.

“When you have a large volume of data, it’s a silent gold mine,” says Ankit Guddewala, a software engineer in Microsoft Digital. “The sheer brilliance lies in taking that data, making it sing, and letting it tell you exactly where the treasure is.” 

The stages to complete the work happen as follows:

1. Ingest the raw support data: The team pulls in large volumes of incident data from our ticketing systems.
2. Clean and structure noisy data: Tickets can include conversation notes, incomplete details, inconsistent writing styles, and abandoned issues. We use the AI enrichment layer to turn those details into structured fields, such as reported versus actual problems and remediation steps.
3. Find patterns across incidents: We cluster tickets to help identify recurring issues and avoid cluttering the knowledge base with one-off scenarios.
4. Compare patterns against existing knowledge: We use the system to search current articles and rank how closely the resolution aligns to current content to determine what steps to take next. For example:
   • Below 40 percent: Create knowledge
   • 40 to 80 percent: Update existing knowledge with missing details
   • Above 80 percent: No update needed
5. Notify the appropriate knowledge managers: Subject-matter experts are notified by email so they can validate the change(s) and add more detail if needed.

“Our goal is to free people from the manual work of maintaining content so they can focus on improving its quality. With the right human-in-the-loop balance, AI can do the heavy lifting while people make sure the final knowledge is accurate and useful.”

Namrata Ladda, product manager II, Microsoft Digital

The result is far less time spent reviewing thousands of articles during every review cycle. We keep humans in the loop to validate the output. Their feedback helps tune the AI model, so it improves over time.

“Our goal is to free people from the manual work of maintaining content so they can focus on improving its quality,” says Namrata Ladda, a product manager in Microsoft Digital. “With the right human-in-the-loop balance, AI can do the heavy lifting while people make sure the final knowledge is accurate and useful.”

Impacts and what’s next on the journey

Using our new AI for Knowledge Management platform, our Global Help Desk teams can identify knowledge gaps without waiting for someone to report them. They’re using AI to generate structured article drafts so humans can focus on quality, not search through data.

The Global Help Desk projects the solution will save them an estimated 16,000 hours annually; result in a 10 percent reduction in support tickets; and reduce the number of advanced support escalations. This leaves everyone on the team more time to directly help employees more quickly, when needed.

“Turning our knowledge base from a static thing into a living knowledge base is a big step forward,” Ladda says. 

Other Microsoft teams, including HR, have expressed interest in leveraging the content management platform. Once the product has completed internal testing, AI for Knowledge Management will be released to all company employees and customers.

“This solution moves knowledge management from manual maintenance to an intelligent capability that helps organizations scale and apply what they know more effectively,” Olkies says.

The post AI for Knowledge Management: Keeping support content up-to-date at Microsoft appeared first on Inside Track.

At Microsoft, this shift is grounded in years of investment in our neurodiverse employees. From launching our Microsoft Neurodiversity Hiring Program to building a company-wide focus on neuro-inclusive employee experiences, we bring our lived experience to how we design and build our products.

We set out to better understand where our products could do more for all our users and challenge established accessibility norms.

“When we create space for neurodivergent voices to shape our products, we don’t just improve accessibility, we build better technology for everyone. Accessibility is strongest when it is informed by lived experience, and initiatives like our product feedback sessions make sure those voices directly influence how our products evolve.”

Neil Barnett, chief accessibility officer, Microsoft Accessibility

Neurodiversity Celebration Week became the launch point for a new approach to product feedback and co-design at Microsoft. We used the event to create an ongoing program that brings the perspectives of neurodivergent employees directly into the product development process, especially individuals who experience challenges with executive functioning tasks such as focus, learning, memory, organization, and task completion.

For our first feedback round, 150 neurodivergent employees evaluated 10 products across multiple divisions using real-world scenarios. Rather than focusing only on traditional accessibility checks, they shared insights on how products support everyday thinking, learning, and productivity.

The feedback provided product teams with a deeper understanding of where experiences can be simplified or made more intuitive and easier to navigate. More importantly, it established a repeatable process for gathering perspectives from people across the cognitive spectrum, helping teams build products that work better for more people.

“When we create space for neurodivergent voices to shape our products, we don’t just improve accessibility, we build better technology for everyone,” says Neil Barnett, chief accessibility officer in Microsoft Accessibility. “Accessibility is strongest when it is informed by lived experience, and initiatives like our product feedback sessions make sure those voices directly influence how our products evolve.”

How neurodiversity can help a broader audience

Neurodiversity refers to the idea that neurological differences—including autism, ADHD, and dyslexia—are a natural part of human diversity. It recognizes that there’s no single “right” way for the brain to work.

“Neurodivergent employees bring an innovative way of thinking and have ideas that help make our products better.”

Tarena Shanaberger, senior PM, Microsoft Accessibility

Product groups at Microsoft value feedback from our neurodiverse employees. However, without a consistent way to get it, product groups had to reach out with ad hoc requests through the different inclusion networks at the company.

We saw an opportunity to create both lasting change and more efficiency in this process.

“Neurodivergent employees bring an innovative way of thinking and have ideas that help make our products better,” says Tarena Shanaberger, a senior PM on the Microsoft Accessibility team.

Designing with lived experience

Neurodivergent inclusion is most valuable when it’s built in early in the product lifecycle, preferably before assumptions take hold and shape the product in ways that are harder to change later.

“Lived experience is a genuine form of expertise. You can have telemetry, design reviews, usability metrics, and KPIs that mean things to people who review data, but there are aspects of cognitive load and human behavior that become much more visible when you involve people who experience systems differently.”

Karl Niblock, architect, Engineering and Architecture Group Security

Karl Niblock, an architect in our Engineering and Architecture Group Security team, believes that early intent can result in experiences that delight users by making products “shockingly” easy and a joy to use, even for something as routine as logging into a tool.

Designing systems that help reduce unnecessary cognitive friction means more people can do their best work consistently and with more confidence. Those are the types of human-centered insights that aren’t possible to derive from data alone.

“Lived experience is a genuine form of expertise,” Niblock says. “You can have telemetry, design reviews, usability metrics, and KPIs that mean things to people who review data, but there are aspects of cognitive load and human behavior that become much more visible when you involve people who experience systems differently.”

When people share their experiences, everyone can learn and benefit.

“One of the patterns I often see is dead-end workflows, where a user follows the process exactly as instructed but ends up stuck, with no obvious next step,” Niblock says. “For someone with dyslexia or autism, the challenge is often not the task itself but the repeated effort of decoding similar-looking instructions and trying to determine what went wrong. By designing clear recovery paths, plain-language guidance, and visible next actions, we can dramatically reduce cognitive friction. Those improvements help neurodivergent users, but they also make products easier and less stressful for everyone.”

Building a better path for product feedback

Software engineer Jordan Cowe surfaced an idea to host an annual neurodiversity bug bash to uncover where products pose challenges for neurodivergent users. He explains that neurodivergent employees are often some of the first people to identify points of friction before release and says that the bug bash helped shine attention on these issues and encourage product group action on the feedback.

“If something affects a neurodivergent employee, it likely also affects many people who don’t identify as neurodiverse. Fixing these issues improves the products for everyone, making them easier to use and better at keeping users engaged.”

Jordan Cowe, software engineer II, Copilot Engineering team

This bug bash led to a structured way to bring in testers, distribute feedback results to product groups, and infuse key learnings into our products. Our testers asynchronously went through the instructions, followed the scenarios, recorded their screens, talked through their pain points, and completed a product survey. We used the results to rate product usability, which helped us identify issues and ideas for improving our products.

“If something affects a neurodivergent employee, it likely also affects many people who don’t identify as neurodiverse,” Cowe says. “Fixing these issues improves the products for everyone, making them easier to use and better at keeping users engaged.”

What feedback looks like from the product side

Microsoft AI UX researcher Audrey Aday used feedback from the co-design sessions to explore what makes an AI response feel inclusive, useful, and manageable. Tester feedback showed that the issue wasn’t capability, it was control.

“Instead of treating accessibility as a check-the-box exercise, we’re building relationships with neurodivergent employees and embedding their feedback into the full product development lifecycle. We’re moving away from ‘test this for us once’ to ‘build this with us, continuously.’”

Audrey Aday, UX researcher II, Microsoft AI Design

Testers consistently said Copilot can feel overwhelming when it returns too much information at once. They want more control (not less capability) over how much detail they see. In response, product teams are exploring custom instruction menus, smarter defaults, and adaptive personalization that learns individual preferences without creating extra work.

Product teams across Microsoft see lived experiences as a quality driver, not an edge case. Neurodivergent employees help teams spot usability opportunities that benefit everyone, especially around information overload, pacing, and clarity.

“Instead of treating accessibility as a check-the-box exercise, we’re building relationships with neurodivergent employees and embedding their feedback into the full product development lifecycle,” Aday says. “We’re moving away from ‘test this for us once” to ‘build this with us, continuously’.”

How the Inclusive Tech Lab supports co-design

Support also comes from the Inclusive Tech Lab, where Microsoft design teams work directly with users with disabilities to uncover exclusionary designs and identify new product opportunities.

“The best time to engage with us is when employees are starting something new or revising old designs. The Inclusive Tech Lab helps teams partner with disabled advisors early in the design process.”

Sarah Heinzen, senior designer, Microsoft Design and Research

The lab connects teams with people who bring their lived experience across different scenarios, such as limited mobility, photosensitivity, low vision, and neurodivergence. This co-design approach brings in users early and keeps them involved so their lived experience can shape product decisions from the start.

“The best time to engage with us is when employees are starting something new or revising old designs,” says Sarah Heinzen, a senior designer on the Microsoft Design and Research team. “The Inclusive Tech Lab helps teams partner with disabled advisors early in the design process.”

Building feedback into how we work

While the product teams work on incorporating feedback, we’re hard at work solidifying this continuous feedback loop and adding it to everyday product development at Microsoft.

Partnering with the Inclusive Tech Lab, we’re setting the foundation for making that a reality using:

• One consistent path to feedback. Product teams have a unified way to engage with employees from the disability and neurodiversity communities.
• A participant pool of testers. We’re building a diverse network of volunteers to make sure diverse lived experiences consistently inform product decisions.
• Inclusive ways of working. We’re establishing guidelines to make sure testing scenarios match real user needs.

The expectation is start early, stay engaged, and build with the people you’re designing for.

“Even as a software engineer and someone who’s neurodivergent, accessibility used to feel like something you thought about later,” Cowe says. “Now I pause early and ask: Does this make sense for real users? Is it clear? Is it usable? It’s changed how I build. I’m looking for the small friction points, the things people struggle with, and I’m catching them sooner. It’s made me a better engineer.”

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Here at Microsoft, we’re no different.

Complaints about the time and effort required to approve expense reports have been consistent from managers across our organization.

“We continuously heard feedback from our leaders that they were spending too much time approving expense reports,” says Michael He, a senior business program manager in the Greater China Region. “They didn’t know where the potential risk actually was, so they had to review everything in detail.”

At Microsoft Digital, the company’s IT organization, we’ve taken this challenge on by introducing an AI-powered Intelligent Risk Engine.

“The Intelligent Risk Engine cuts through complexity, pointing approvers straight to the expenses that warrant attention. This clears up the noise that has been driving review fatigue, especially at quarter’s end.”

Sangay Wangmo, Microsoft Digital experience director, Middle East and Africa

With this new tool, we shifted approvals from uniform, manual scrutiny to automated, risk-based decision making. This enables faster reviews, reduced cognitive load, and improved compliance outcomes.

“The Intelligent Risk Engine cuts through complexity, pointing approvers straight to the expenses that warrant attention,” says Sangay Wangmo, a Microsoft Digital experience director for our Middle East and Africa region. “This clears up the noise that has been driving review fatigue, especially at quarter’s end.”

Looking ahead, we plan to expand the tool to include automated approvals for low-risk cases, creating a more scalable, intelligent, and efficient process. This will ease the pain for our managers and allow them to focus on their strategic work.

Manual approvals in a complex compliance environment

Across our global enterprise, we handle nearly a million expense reports annually. In regions such as Central and Eastern Europe, the Middle East, and Africa (CEMA), our expense approval processes are shaped by diverse regulatory requirements in many different countries.

“As organizations scale, managers naturally have more direct reports, which means more approvals to process. At the same time, accountability for all compliance still sits with the manager, which adds pressure.”

Kethan Parbhoo, general manager, Central and Eastern Europe, Middle East, and Africa

To take one example, the Middle East and Africa—featuring multiple subregions, languages, and local policy nuances—presents a complex challenge for expense management. Applying a consistent risk lens to every case is difficult.

To ensure they stay compliant, leaders often review expense reports in detail, including verifying receipt accuracy, matching invoice data, and checking supporting information (like attendee lists). This level of review requires substantial time and attention, particularly for managers with large teams who receive a high volume of submissions. This becomes even more time-consuming as groups grow.

“As their organizations scale, managers naturally have more direct reports, which means more approvals to process,” says Kethan Parbhoo, a general manager in the Central and Eastern Europe, Middle East, and Africa region. “At the same time, accountability for all compliance still sits with the manager, which adds pressure.”  

The current tool, MS Expense, which was useful in a pre-AI environment, doesn’t provide an optimal user experience. The process was repetitive and depended heavily on manual validation. Existing tools provide limited support for prioritizing risk or simplifying these tasks, resulting in a similar effort being applied to both low- and high-risk expenses. 

As a result, leaders experience increased workload, slower approval timelines, and continued exposure to potential compliance gaps, despite careful review. 

The four top-level internal pain points of the old approval process can be summarized as: 

• Not knowing where the risk is 
• Approvals take too much time, especially at quarter’s end 
• Too much effort is spent on low-risk, routine reviews 
• Issues are found too late, triggering audits and resubmissions after the fact

The Intelligent Risk Engine is helping us address all of these in a unified, cohesive way.

“The system leverages a combination of AI-based risk checks and policy-driven risk checks. This produces a quantifiable baseline score that allows for easier comparison and risk assessment.”

Eric Carnrite, principal product manager, Travel and Expense

AI-assisted risk scoring embedded in MS Approvals 

The Intelligent Risk Engine that our team developed integrates with the existing MS Approvals system, shifting from volume-based checks to risk-based decisioning. We embed this analysis directly into the workflow.

The risk engine evaluates each expense report against multiple criteria, including receipt matching (which leverages AI and optical character recognition), spending patterns, and policy alignment. It assigns a risk score (1-100) and a risk level—1 at the low end and 5 at the high end—and then highlights specific areas that might require attention. 

“The system leverages a combination of AI-based risk checks and policy-driven risk checks,” says Eric Carnrite, a principal product manager for the Travel and Expense team. “This produces a quantifiable baseline score that allows for easier comparison and risk assessment.”

Expense risk score table

The tool also explains why something has been flagged. This allows approvers to quickly understand where to focus their review and to catch issues early, rather than after the fact. 

“AI is effectively doing the initial assessment that a human would otherwise have to do,” Parbhoo says. “It gives you a strong signal, so you can decide quickly where deeper review is needed.” 

With our new model, low-risk expenses can be reviewed and approved with minimal effort, while higher-risk items receive closer examination. It just makes sense to prioritize our work this way.

“Previously, a $10 coffee receipt required the same level of scrutiny as a $300 invoice, which doesn’t make sense at scale,” Wangmo says. “Leaders are forced to treat everything the same, even when the risk level is clearly different.”

Early returns indicate significant improvements. These include:

• Immediate productivity gains, as approvers stop reviewing all expenses manually
• Reduced rework and late‑stage audit findings 
• Stronger governance at scale, without adding headcount or introducing new manual processes

“It pulls the three parts together: Employees, approvers, and auditing and compliance. It will ultimately make it more proactive for all parties involved, rather than reactive—making sure the whole flow of the expense process is more meaningful.”

Michael He, senior business program manager, Greater China Region

Future direction: Expanded automation and standardization 

This kind of AI-powered technology will eventually allow us to pull everything together in one unified system, meeting the needs of all the major players in the expense management process.

“It pulls the three parts together: employees, approvers, and auditing and compliance,” He says. “This will ultimately make it more proactive for all parties involved, rather than reactive—making sure the whole flow of the expense process is more meaningful.”

And we’re not done innovating. The current risk engine implementation establishes a foundation for further automation, and that’s where we’re headed.

One planned enhancement is the automatic approval of low-risk expenses, subject to compliance approval. This could produce significant savings and greater efficiency across our organization.

“With auto-approvals, we’re not talking about a nominal amount,” Carnrite says. “At this point, we’re targeting up to 75 percent of expense reports being automatically reviewed and approved. This could save us around 150,000 to 200,000 person-hours a year—and that’s at the manager and director level.”

We’ve also added advanced optical character recognition (OCR) technology into our expense tools. This now allows for automatic categorization of expenses, so employees don’t have to enter the category manually for each item.

“The end goal is an AI agent that can proactively create an expense report for you and ask you to review it. You would just validate it and move it forward, instead of building it from scratch.”

Salvador Segura, director of business programs, Field Capability Services

Additional future improvements could include expanded use of AI for data validation, receipt processing, and identification of inconsistencies across submissions. Over time, the goal is to support a standardized approval framework that adapts to regional differences while maintaining consistent risk evaluation and reducing manual workload.

At the next level, we’re hoping to use AI to eventually fully automate the creation of expense reports as well. This would be essentially the Holy Grail for this function.

“The end goal is an AI agent that can proactively create an expense report for you and ask you to review it,” says Salvador Segura, a director of business programs in Field Capability Services. “You would just validate it and move it forward, instead of building it from scratch.” 

It’s this kind of AI-powered work environment that we’re pushing for at Microsoft Digital as we play a leading role in our company’s ongoing Frontier Firm journey.

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At that scale, Python dependencies have emerged as a significant source of risk for us—representing the third-largest vulnerability surface across the company.

The good news is that we have strong visibility into these vulnerabilities, with tools that continuously detect and surface risks across our codebases. The bad news is that turning those insights into action required a complex remediation process.

Updating a single code package often caused changes across multiple interdependent libraries. This required coordinated updates, validation, and testing to maintain system stability.

“When AI arrived, I saw it as a great opportunity to finally fix a very complex problem we had: The level of entanglement involved in Python code dependencies. A simple script wasn’t going to resolve it—you needed the power of AI.”

Humberto Arias, senior product manager, Microsoft Digital

Multiply this by thousands of projects throughout our enterprise, and vulnerabilities accumulated much faster than we could resolve them. To address this challenge, we turned to AI.

Microsoft Digital—the company’s IT organization—has developed an AI-powered solution called Python Dependency Remediation. Designed to work directly within the developer workflow, this solution analyzes dependency chains, applies required updates, and automatically adjusts the code. This enables our engineers to remediate vulnerabilities quickly and consistently at enterprise scale.

“I’ve worked for years in the vulnerability management space at Microsoft,” says Humberto Arias, a senior product manager in Microsoft Digital. “When AI arrived, I saw it as a great opportunity to finally fix a very complex problem we had: The level of entanglement involved in Python code dependencies. A simple script wasn’t going to resolve it—you needed the power of AI.”

The tool has shown so much promise that we have begun releasing it externally, so that millions of Python developers around the world can take advantage of it.

“I used to have this problem all the time. I upgrade one library, and then I’ve got to upgrade 17 other things, and something else breaks, and now my code is completely different.”

Rich Chiodo, principal software engineer, Python and Tools for AI

Get started

Try using the Python Dependency Remediation tool

Flexibility leads to dependencies and risk

Python is a very flexible language, which is why it’s so popular among software developers. But that same flexible nature—it can be used across a wide range of scenarios—also means it forms deeply interconnected dependency chains. When one code library is updated, it can trigger changes across many others.

“I used to have this problem all the time,” says Rich Chiodo, a principal software engineer on the team responsible for Python Tools and AI. “I upgrade one library, and then I’ve got to upgrade 17 other things, and something else breaks, and now my code is completely different.”

“Developers avoid the upgrades because the dependency web is so complex. This means the vulnerabilities accumulate over time and can become a real security risk.”

Chintan Sheth, principal engineering manager, Viva Glint

Because the code is so interdependent and remediation is time-consuming, many developers skip updating their code packages, which can lead to security vulnerabilities.

Security compliance was often seen as a burden because it slows people down.

“Developers avoid the upgrades because the dependency web is so complex,” says Chintan Sheth, a principal engineering manager on the Viva Glint product team. “This means the vulnerabilities accumulate over time and can become a real security risk.”

“After my manager mentioned it, I reviewed the idea on the hackathon page, and it looked really interesting to me. So I jumped in, and we created a prototype and a demo video with a quick solution. That’s how it started.”

Shiva Krishna Gollapelly, senior software engineer, Microsoft Digital

Hacking our way to a solution

Like some of the best internally developed tools and processes, Python Dependency Remediation came out of a Microsoft hackathon project. These grassroots events allow our engineers to tackle interesting technical challenges in a collaborative, creative way.

“After my manager mentioned it, I reviewed the idea on the hackathon page, and it looked really interesting to me,” says Shiva Krishna Gollapelly, a senior software engineer in Microsoft Digital and the lead developer on the project. “So I jumped in, and we created a prototype and a demo video with a quick solution. That’s how it started.”

The fact that this solution came from a hackathon highlights the ideas-driven culture that we promote at the company.

“This really speaks to our special culture of innovation,” says Snigdha Bora, a principal group engineering manager for Employee Experience. “After this emerged from the hackathon, our developers realized it could solve a problem at scale—that it was worth taking through the full development cycle so we can release it for all of Microsoft, and maybe beyond.”

Solving the issue with one click (and AI)

Because the challenge was not detecting vulnerabilities but fixing them, we had to rethink how we addressed Python dependencies.

“The extension automatically finds the right updates and then fixes the vulnerabilities, so developers don’t need to do the research, the manual upgrades and fixes, run test cases, debugging—all those things that used to take so much time. With our solution, it’s just one button click and it does all of that automatically.”

Shiva Krishna Gollapelly, senior software engineer, Microsoft Digital

In the past, when engineers received a vulnerability notification, they would have to step outside their development workflow and address the issue. What was needed was a solution that could be enacted within their normal workflow—integrating remediation directly into the tools they were already using.

So, we created the Python Dependency Remediation extension for Visual Studio Code, a common Python development environment. Once installed, engineers can address vulnerabilities in the flow of their work.

“The extension automatically finds the right updates and then fixes the vulnerabilities, so developers don’t need to do the research, the manual upgrades and fixes, run test cases, debugging—all those things that used to take so much time,” Gollapelly says. “With our solution, it’s just one button click and it does all of that automatically, with the help of AI.”

The extension uses the APIs built into Visual Studio Code to connect with any AI model the user has access to. (If there is no AI model available, Gollapelly explains, the extension will still make the package updates but won’t do the remediation fixes to the code.) It also produces a report of the changes for the developer to review in case there’s a snag that needs troubleshooting.

“This tool removes a significant burden from our developers,” Bora says. “We are shifting the entire remediation process left, embedding it early in the development workflow. Developers can review the changes and move forward immediately, making the whole process more efficient.”

“We’ve upgraded the library with new methods, calls, and structures. Now, let’s make sure everything works, check for errors in the code, etc. That’s the gap we’re bridging with AI.”

Angel Saldivia, software engineer, SharePoint

The result is that fixes and upgrades that used to take multiple hours of developer time now take minutes, and the code is much more reliable.

What the agent does in this solution is help close that loop, something that the engineer used to have to do.

“We’ve upgraded the library with new methods, calls, and structures,” says Angel Saldivia, a software engineer on the SharePoint product team. “Now, let’s make sure everything works, check for errors in the code, etc. That’s the gap we’re bridging with AI.”

From Customer Zero to global impact

One of the powerful things about working at Microsoft is that you get to help develop technology tools that can change the world. This is the case with Python Dependency Remediation as well.

“We realized this technology had much broader value. There are hundreds of millions of Python users worldwide, so the impact could be massive.”

Snigdha Bora, principal group engineering manager, Employee Experience

As Bora explains, while the solution was being developed it was presented to Guido van Rossum, the creator of Python (and a Microsoft employee). He immediately saw the incredible potential of the concept.

“He suggested that we could take this solution to the world, not just to Microsoft,” Bora says. “We realized this technology had much broader value. There are millions of Python users, so the impact could be massive.”

To help make this happen, Microsoft Digital approached Graham Wheeler, a principal group engineering manager on the Python and Tools for AI team. Wheeler’s team is responsible for shipping Pylance, a development extension for Visual Studio Code used by more than 180 million developers worldwide.

“One of the things we could do was provide a jumping-off point for this extension, so that when users installed Pylance they’d be prompted to also download Python Dependency Remediation. It can help raise awareness, because many users don’t actually do the dependency scanning and updating that they should.”

Graham Wheeler, principal group engineering manager, Python and Tools for AI

Wheeler and his team are in the process of incorporating the Python Dependency Remediation extension as an option during Pylance installation. This will open up a convenient vector for getting the tool in front of a huge audience, potentially revolutionizing Python development.

“One of the things we could do was provide a jumping-off point for this extension, so that when users installed Pylance they’d be prompted to also download Python Dependency Remediation,” Wheeler says. “It can help raise awareness, because so many users don’t actually do the dependency scanning and updates that they should. So, we’re helping with that challenge.”

Beyond Python, the AI-powered technology behind this extension might be applied to other dependency challenges as well. What started as a simple hackathon project could have huge ramifications for the future of software development.

“This solution can easily be adapted to other libraries, other programming languages,” Gollapelly says. “Whether you’re talking about C#, Angular, React, or another language, the concept is the same. The implications are vast.”

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