Balancing the needs of speed, quality, and scalability has always been a formidable challenge for IT organizations, but few enterprises feel the strain as acutely as Microsoft. With tens of thousands of users—engineers, support staff, internal partners, and external vendors—working across multiple business lines, the company’s internal support systems must not only keep up with digital transformation but stay an innovative step ahead. For Microsoft Digital, the in-house IT arm, the rapid provisioning and management of virtual labs for diagnostics, development, and support might sound routine on paper; in reality, it has historically been riddled with bottlenecks, especially when it comes to support.
MyWorkspace, Microsoft’s internal platform for virtual lab provisioning, originally designed for diagnostics and testing, has evolved into a business-critical tool. As adoption soared, technical scaling—adding resources, storage, and compute power using Azure—proved relatively straightforward. The real challenge lay elsewhere: the avalanche of basic support requests each time groups of new users were onboarded. In an environment where complexity is a given, even simple onboarding or troubleshooting queries could quickly overwhelm Tier 1 support queues.
These requests, ranging from “How do I start a lab?” to “What does this error mean?” rarely demanded deep technical insight but appeared in such volume that even well-staffed support teams struggled to keep up. According to Microsoft Digital’s Joshua Deans, much of the complexity wasn’t in solving new technical problems, but in rediscovering and repeating solutions to common, often previously solved, issues. The company even set up a dedicated Teams channel where repeated conversations reaffirmed the need for a more sustainable solution.
The team built a generative AI assistant for MyWorkspace, trained not only on official documentation but also internal knowledge bases and, crucially, a trove of real support conversations between Tier 1 staff and users. This allowed the assistant to present not just static, scripted responses, but context-aware answers, adapting to the nuances of each user’s needs.
As Vikram Dadwal, principal software engineering manager at Microsoft Digital, emphasized, the real breakthrough wasn’t merely in faster answers but in offloading entire classes of repetitive, low-impact queries to the AI. Support engineers’ focus could shift to higher-level incidents and innovation rather than reviewing countless new tickets on old issues.
The AI assistant was engineered using Microsoft’s own open-source Semantic Kernel framework. Semantic Kernel supports generative AI and large language models (LLMs) without forcing developers into platform lock-in—making it easier for Microsoft Digital to experiment, iterate, and customize. Semantic Kernel’s modular architecture allows engineers fine-grained control over prompts, data sources, and plug-ins, all critical for an evolving ecosystem.
Crucially, the MyWorkspace AI assistant’s system prompt is tightly scoped. It knows only about MyWorkspace and responds only within that domain. This mitigates risks around out-of-domain answers, hallucination, or inappropriate content, increasing accuracy and user trust in high-stakes enterprise settings.
While such internal metrics should always be scrutinized and cross-referenced for external verifiability, they are echoed in industry reports on AI support automation, where reductions of similar magnitude are observed when contextually narrow, well-trained assistants manage highly repetitive queries.
This shift has a ripple effect: Tier 1 support volumes fall sharply, user onboarding accelerates, and new engineers or partners feel more empowered. The platform even recommends prompt starters (“Try asking about lab configuration!”), guiding novice users much like Microsoft Copilot does for end-user productivity. Each interaction feels less like logging a problem and more like consulting a knowledgeable colleague.
Here’s how it works. The AI interprets nuanced, often natural-language requests—such as “I’m troubleshooting sync issues between SharePoint Online and on-prem”—and creates a tailored lab environment that mirrors the reported scenario. This orchestration is powered by the Semantic Kernel SDK multi-agent framework, dividing responsibilities among specialist agents: one interprets support context, another configures labs, another ensures cost efficiency, and so on.
This means engineers no longer need to navigate a maze of configuration choices. Instead, they describe their challenge, and the system automatically provisions an optimized, scenario-specific environment. Not only does this reduce cognitive friction, but it also delivers cost savings—agents recommend ephemeral VMs, auto-pause idle resources, and select lower-cost configurations without sacrificing diagnostic fidelity.
“We have a lot more telemetry now, so users can provide feedback to our responses,” notes Joshua Deans. “And we can actually view where the model is giving incorrect or inappropriate information, and we can use that to make adjustments to the prompt provided to the model.”
Platform operators can observe patterns: which queries are trending, where friction still exists, and how user sentiment evolves with each model update. This continuous feedback enables a virtuous cycle: the AI gets smarter, support quality rises, and the underlying knowledge base reflects actual user needs, not just theoretical best practices.
Moreover, the assistant’s transparent feedback mechanisms—showing users how their input feeds improvements—help address legitimate concerns around bias, error, and inappropriate escalation. Every effort is made to keep the AI focused on what it knows best and ensure a human is never far away if escalation is truly needed.
As similar AI-powered systems are adopted industry-wide, organizations will face questions around generalizability, transparency, and continuous learning. But the blueprint is clear. AI can—and should—be more than just an automation tool. Properly scoped, thoughtfully trained, and seamlessly integrated, it is the new backbone of enterprise knowledge work.
Microsoft’s case study serves as both a proof-of-concept and a roadmap, suggesting that even the most daunting operational challenges can be deconstructed and rebuilt if teams are willing to learn from the data, start small, and let each solved ticket teach the system how to prevent the next.
In an IT world that prizes both agility and reliability, this evolution isn’t just welcome—it’s necessary. And the story of smarter labs and faster fixes at Microsoft is far from finished; it’s only accelerating, one intelligent conversation at a time.
Source: Microsoft Smarter labs, faster fixes: How we’re using AI to provision our virtual labs more effectively - Inside Track Blog
MyWorkspace, Microsoft’s internal platform for virtual lab provisioning, originally designed for diagnostics and testing, has evolved into a business-critical tool. As adoption soared, technical scaling—adding resources, storage, and compute power using Azure—proved relatively straightforward. The real challenge lay elsewhere: the avalanche of basic support requests each time groups of new users were onboarded. In an environment where complexity is a given, even simple onboarding or troubleshooting queries could quickly overwhelm Tier 1 support queues.These requests, ranging from “How do I start a lab?” to “What does this error mean?” rarely demanded deep technical insight but appeared in such volume that even well-staffed support teams struggled to keep up. According to Microsoft Digital’s Joshua Deans, much of the complexity wasn’t in solving new technical problems, but in rediscovering and repeating solutions to common, often previously solved, issues. The company even set up a dedicated Teams channel where repeated conversations reaffirmed the need for a more sustainable solution.
Turning Bottlenecks into Breakthroughs: The AI Intervention
Instead of treating repetitive support tickets as a necessary cost of scale, Microsoft Digital spotted a more strategic opportunity. By leveraging artificial intelligence—not as a basic chatbot but as a purpose-built, domain-specific assistant—the company reimagined how onboarding, troubleshooting, and operational questions could be answered instantly, with context and accuracy.The team built a generative AI assistant for MyWorkspace, trained not only on official documentation but also internal knowledge bases and, crucially, a trove of real support conversations between Tier 1 staff and users. This allowed the assistant to present not just static, scripted responses, but context-aware answers, adapting to the nuances of each user’s needs.
As Vikram Dadwal, principal software engineering manager at Microsoft Digital, emphasized, the real breakthrough wasn’t merely in faster answers but in offloading entire classes of repetitive, low-impact queries to the AI. Support engineers’ focus could shift to higher-level incidents and innovation rather than reviewing countless new tickets on old issues.
Infrastructure: Building on Azure and Semantic Kernel
Any AI assistant in a mission-critical, high-scale operation needs robust infrastructure behind it. MyWorkspace is built natively on Azure, with instant scalability across tens of thousands of virtual machines, responding in real-time to shifting demand. This elasticity is essential not just for labs, but for the AI layer as well.The AI assistant was engineered using Microsoft’s own open-source Semantic Kernel framework. Semantic Kernel supports generative AI and large language models (LLMs) without forcing developers into platform lock-in—making it easier for Microsoft Digital to experiment, iterate, and customize. Semantic Kernel’s modular architecture allows engineers fine-grained control over prompts, data sources, and plug-ins, all critical for an evolving ecosystem.
Crucially, the MyWorkspace AI assistant’s system prompt is tightly scoped. It knows only about MyWorkspace and responds only within that domain. This mitigates risks around out-of-domain answers, hallucination, or inappropriate content, increasing accuracy and user trust in high-stakes enterprise settings.
Quantifying the Gains: From 20 Minutes to 30 Seconds
One of the most compelling measures of success is the dramatic reduction in support resolution time. “On average, we measured these interactions at around 20 minutes from ticket submission to problem resolution,” explains Nathan Prentice, senior product manager at Microsoft Digital. “Now compare that with a 30-second AI interaction for resolving the same class of issues—that’s a 98% reduction in resolution time, a number we’ve validated with our support team and continue to track.”While such internal metrics should always be scrutinized and cross-referenced for external verifiability, they are echoed in industry reports on AI support automation, where reductions of similar magnitude are observed when contextually narrow, well-trained assistants manage highly repetitive queries.
Shifting Paradigms: From Tickets to Conversations
Traditional IT support models operate on a ticketing paradigm—problems are submitted, routed, and resolved, sometimes over days. With the new MyWorkspace assistant, unresolved questions are handled conversationally, in real-time, without requiring escalation. Repetitive onboarding issues are resolved instantly, users learn how to ask better questions, and friction gives way to fluency.This shift has a ripple effect: Tier 1 support volumes fall sharply, user onboarding accelerates, and new engineers or partners feel more empowered. The platform even recommends prompt starters (“Try asking about lab configuration!”), guiding novice users much like Microsoft Copilot does for end-user productivity. Each interaction feels less like logging a problem and more like consulting a knowledgeable colleague.
Beyond Passive Support: Active, Intelligent Provisioning
Traditional virtual lab provisioning often assumes users know exactly what environment they need—a risky assumption in complex hybrid or cloud-first settings. Microsoft Digital responds with a next-generation feature: orchestrated, multi-agent AI-driven provisioning.Here’s how it works. The AI interprets nuanced, often natural-language requests—such as “I’m troubleshooting sync issues between SharePoint Online and on-prem”—and creates a tailored lab environment that mirrors the reported scenario. This orchestration is powered by the Semantic Kernel SDK multi-agent framework, dividing responsibilities among specialist agents: one interprets support context, another configures labs, another ensures cost efficiency, and so on.
This means engineers no longer need to navigate a maze of configuration choices. Instead, they describe their challenge, and the system automatically provisions an optimized, scenario-specific environment. Not only does this reduce cognitive friction, but it also delivers cost savings—agents recommend ephemeral VMs, auto-pause idle resources, and select lower-cost configurations without sacrificing diagnostic fidelity.
The Feedback Loop: Continuous Learning and Improvement
A critical component of effective enterprise AI is the ability to learn. The MyWorkspace assistant collects telemetry on every interaction. Users rate responses with a simple thumbs up or down, and outlier cases—where the model gives irrelevant or incorrect answers—are quickly flagged and fed back into the improvement cycle. Engineers review low-rated threads to refine prompts or retrain the underlying models, sharpening performance for future queries.“We have a lot more telemetry now, so users can provide feedback to our responses,” notes Joshua Deans. “And we can actually view where the model is giving incorrect or inappropriate information, and we can use that to make adjustments to the prompt provided to the model.”
Platform operators can observe patterns: which queries are trending, where friction still exists, and how user sentiment evolves with each model update. This continuous feedback enables a virtuous cycle: the AI gets smarter, support quality rises, and the underlying knowledge base reflects actual user needs, not just theoretical best practices.
Trust, UI, and the Human Factor
Achieving broad adoption of AI support in a demanding enterprise also requires attention to design and trust. The MyWorkspace assistant leverages familiar layouts and adaptive cards that mirror internal tools like Microsoft 365 Copilot. This yields not only consistency and recognition for users but also reinforces the assistant’s credibility.Moreover, the assistant’s transparent feedback mechanisms—showing users how their input feeds improvements—help address legitimate concerns around bias, error, and inappropriate escalation. Every effort is made to keep the AI focused on what it knows best and ensure a human is never far away if escalation is truly needed.
Architectural Strengths and Industry Impact
Microsoft’s AI-driven lab support model is distinguished by several strengths:- Narrow Domain Focus: By confining the assistant’s expertise to MyWorkspace, Microsoft avoids common AI pitfalls like hallucination or irrelevant recommendations.
- Open, Modular Infrastructure: Building on open-source Semantic Kernel with Azure backbone provides flexibility to future-proof the solution, integrate with evolving LLMs, and adapt quickly to changing business needs.
- Multi-agent Orchestration: Intelligent provisioning leverages multi-agent coordination, pushing automation beyond simple response to active enablement of complex environments.
- Continuous Learning: User feedback is systematically gathered and acted upon, driving iterative improvement.
- Scalable Without Headcount: By offloading routine issues, the system empowers Tier 1 and 2 teams to focus on critical challenges, innovation, or deep troubleshooting.
- Familiar User Experience: UI aligned with Copilot and adaptive cards minimizes user resistance.
Potential Risks and Considerations
Despite dramatic gains, several risks and caution points are worth noting:- Model Drift and Knowledge Staleness: If the AI’s core dataset (support logs, FAQs, documentation) isn’t kept up-to-date, the assistant risks providing outdated or misleading guidance—especially in fast-moving technical domains.
- Over-reliance on Automation: There’s a danger that users may bypass critical escalation for truly novel or complex issues if trust in the AI is too high, potentially delaying critical resolutions.
- Feedback Noise: Telemetry-based feedback can be gamed or reflect outlier frustrations rather than representative issues. Over-tuning to negative signals may lead the AI to become overly conservative or repetitive.
- Bias and Blind Spots: Even domain-specific generative models can develop unintentional biases or internal blind spots, particularly if historical support data is itself incomplete or skewed.
- Security and Data Privacy: Automatic provisioning and context-aware replies require strict access controls and careful handling of potentially sensitive data, particularly in environments open to external partners or vendors.
- Vendor Lock-in Mitigation: While Semantic Kernel is open-source, reliance on Azure and Microsoft-specific integrations may introduce some degree of cloud lock-in, a consideration for organizations seeking multi-cloud or hybrid strategy.
Lessons for the Industry: Practical Takeaways
Microsoft’s MyWorkspace journey offers lessons for other enterprises looking to infuse AI into internal support and lab provisioning:- Start with Narrow Use Cases: Rather than boiling the ocean, organizations should begin with targeted domains (e.g., a specific internal tool or environment) and leverage existing support data to bootstrap training.
- Invest in Modular AI Infrastructure: Open frameworks like Semantic Kernel allow for adaptation and integration without being boxed into proprietary solutions.
- Design for Trust and Continuity: Aligning assistant UI and interactions with familiar platforms (such as Copilot) speeds adoption.
- Build Feedback Loops from Day One: Instrument everything, from user satisfaction to scenario coverage, and close the feedback loop with engineering and product teams.
- Launch Early, Iterate Relentlessly: Accept imperfections, collect data, and continuously improve. AI assistants don’t need to be perfect at launch, but they must learn quickly.
- Think Beyond Ticketing: Envision a future where AI not only solves problems reactively, but proactively anticipates and neutralizes friction before it arises.
Looking Ahead: Proactive, Anticipatory, Always-On Support
The MyWorkspace experience underscores a critical shift: AI is not simply a tool for cost-cutting, but a means to reframe complexity as strategic advantage. By embedding intelligence directly into support operations, Microsoft is not only making labs faster and fixes smarter, but is setting a new standard for scale, adaptability, and user satisfaction in enterprise IT.As similar AI-powered systems are adopted industry-wide, organizations will face questions around generalizability, transparency, and continuous learning. But the blueprint is clear. AI can—and should—be more than just an automation tool. Properly scoped, thoughtfully trained, and seamlessly integrated, it is the new backbone of enterprise knowledge work.
Microsoft’s case study serves as both a proof-of-concept and a roadmap, suggesting that even the most daunting operational challenges can be deconstructed and rebuilt if teams are willing to learn from the data, start small, and let each solved ticket teach the system how to prevent the next.
In an IT world that prizes both agility and reliability, this evolution isn’t just welcome—it’s necessary. And the story of smarter labs and faster fixes at Microsoft is far from finished; it’s only accelerating, one intelligent conversation at a time.
Source: Microsoft Smarter labs, faster fixes: How we’re using AI to provision our virtual labs more effectively - Inside Track Blog