How Azure uses AI to turn feedback into improved customer experiences
The Challenge: Synthesizing fragmented feedback signals, to improve Azure's experience quality at scale Customers experience products and services end to end, but product experiences are often structured around individual service. One team may only know its top issues, while another may see only its own slice of experience. That structure makes it difficult to identify cross-cutting friction across the broader product experience. The feedback signals themselves are also fragmented. Customers share feedback through in-product surveys, support cases, field conversations, and social channels. Most product teams can see only part of that picture, making it hard to distinguish isolated comments from meaningful trends, understand which issues were having the greatest impact, and avoid missing critical feedback. For the Azure team, the challenges of fragmentation were amplified by scale. The team processed roughly 10,000 to 15,000 customer feedback reports each month, and synthesizing that feedback required 80 to 100 hours of expert analysis. Additional effort was needed to translate findings into consistent engineering work items. As feedback volume grew, manual analysis became increasingly unsustainable creating delays in identifying and addressing customer priorities. Compounding the challenge was the absence of an effective feedback loop to measure the impact of quality improvements. Teams struggled to justify investments in quality over new features because the return on those investments was difficult to quantify. The absence of a closed-loop measurement system made it difficult to consistently assess the customer impact of quality improvements. The team needed a system that could operate across organizational boundaries and across the product development lifecycle: Identify the most critical customer issues across fragmented feedback channels and product areas. Convert those insights into actionable engineering work, help teams address issues effectively, and measure outcomes to close the loop. The solution needed to preserve team-specific context, maintain auditability, continuously improve through feedback, and keep human experts in control of decisions that require judgment. To address these challenges, Microsoft launched the Great Experiences Matter (GEM) initiative. GEM is designed to analyze feedback signals in aggregate, with access controls and privacy safeguards designed to limit exposure of customer-identifiable information while helping teams identify patterns across channels. The Solution: an agentic feedback-to-fix loop with humans in control GEM created an AI-enabled feedback-to-fix workflow that connects customer listening, engineering action, and impact measurement across the product development lifecycle. The workflow uses Microsoft Foundry, Azure Data Explorer, Microsoft Fabric, Azure DevOps, and a set of custom agents to transform large volumes of qualitative feedback into prioritized insights and actionable engineering work. Fig 1. GEM AI-enabled automation workflow The system closes the loop through two connected motions. Find. Agentic workflows remove noise and duplicate reports, assess relevance and actionability, classify feedback against known issues from UX research, cluster related issues, and surface likely root causes. GEM builds on years of deep end-to-end UX research that has identified systemic friction across customer journeys and product boundaries. By continuously triangulating GEM signals with ongoing research, we combine broad, scalable listening with deep human insight to inform a more cohesive Azure experience. The results are surfaced through global scorecards for cross-cutting Azure issues and vertical scorecards tailored to individual product teams. Fig 2. GEM Global scorecard of top issues with Azure, data has been fictionalized to protect intellectual property Fix. The workflow creates Azure DevOps work items with the customer context, likely reproduction steps, recommended next actions, and an auditable trace of the supporting analysis. To date, 42% of the identified issues have been addressed through engineering action. The team is also extending an AI-assisted engineering workflow, using GitHub Copilot cloud agent, that can generate proposed fixes for straightforward issues. Engineers remain responsible for reviewing, refining, approving, and shipping any changes. The architecture is designed for inspection rather than blind automation. The recommendations include an auditable evidence trail allowing reviewers to inspect the source feedback, classifications, supporting references, confidence indicators, and recommendation actions. Human expertise enters the system at several points. Researchers shape the issue taxonomies and qualitative grounding. Product teams define ownership boundaries, business priorities, domain-specific vocabulary, and trusted sources that guide agent analysis. Engineers review and act on resulting work items, while leaders use scorecards to inform investment decisions. This context is captured in configuration files that evolve alongside the products they support. Teams can add new issue categories, refine keywords, clarify ownership boundaries, or identify trusted research sources. The next analysis cycle automatically incorporates the updated context without requiring changes to the underlying agents. That design creates a "feedback loop for the feedback loop". Teams review the root-cause analyses and work-item quality, identify gaps, and refine their configurations. This enables teams to continuously embed domain expertise into the workflow, improving how feedback is interpreted and prioritized without requiring changes to the underlying infrastructure. Their input improves subsequent runs, helping the system become more precise while preserving local product knowledge. The agent also maintains access to reports from previous runs and uses tools such as Web IQ and MCP servers to assess whether previously identified issues are improving, still require attention, or can be confidently closed. Fig 3. Example of the vertical feedback agent reasoning through customer feedback to find new work items One demonstrated example surfaced customer reports that a networking tool lacked IPv6 validation and support. The workflow generated an engineering work item describing the issue, customer impact, likely reproduction path, and recommended actions. The networking team reproduced the issue, validated the finding, and added it to its backlog. The goal is not to remove people from the process. Agents assume much of the cognitive load associated with sorting, clustering, tracing, and drafting, allowing experts to focus on judgment, prioritization, and implementation. Teams remain accountable for what is fixed, what is funded, and what is allowed to ship. The Impact: measurable experience gains at Microsoft production scale GEM began with manual interventions and is now scaling through AI-enabled workflows. The combined approach has produced measurable results across the Azure Portal and individual product experiences: The workflow aggregates and analyzes approximately 10,000 to 15,000 feedback reports each month across in-product, support, and social channels. Automated analysis reduced manual synthesis time by over 95 percent, turning a process that required 110 to 160 hours each month into a workflow that runs in under 60 minutes. Between October 2025 and April 2026, Azure Portal feedback rates declined by 30%. During that same period, GEM helped teams identify and prioritize experience improvements, creating a clearer link between customer feedback, engineering action, and outcome measurement. Service-level outcomes also demonstrate how better signals can drive business impact. For example, improvements to VM Connect experiences reduced overall Core Compute support volume by 1-2% per month, resulting in proportionate cost savings. The Azure Growth team increased subscription conversion by 9.1 percent after prioritizing issues highlighted through GEM. The value goes beyond speed. Leaders gain a more consistent basis for prioritization, and engineering teams receive work that is already connected to customer evidence and impact signals. Most importantly, every completed cycle creates new learning. Teams can measure changes in customer feedback and support volumes following improvements, incorporate partner input into future analyses, and continuously refine both the system and the products it helps improve. Key learnings and transferable practices The GEM experience offers several lessons for teams building agentic systems around complex, qualitative business processes: Start with real problems, not AI - Value comes from understanding the genuine business needs and applying AI where it is demonstrably better than existing approaches. Applying AI without a clearly defined problem often adds complexity without delivering meaningful value. Design for the end-to-end workflow - Value comes from connecting insights to the broader business process, including grounding in prior knowledge, prioritization, engineering action, post-fix measurement and reporting. Standalone AI output creates limited value, while an integrated workflow drives outcomes. Design for human judgment and accountability - Agents can reduce toil and cognitive load, but researchers, product managers, engineers, and leaders remain responsible for validating insights and determining appropriate actions. Ground agents in the knowledge of the teams they serve - Shared models require local context. Editable configuration files allow teams to define ownership, business priorities, releases, examples, and trusted sources without modifying underlying agents. Build observability and feedback mechanisms into the agentic system itself - Making analysis inspectable through reasoning traces, source context, and recommendations enables experts to identify gaps, improve outputs, and build trust over time. Build feedback mechanisms directly into the flow of work, making it effortless for users to provide input on the system. Tailor outputs to the people making decisions - Executives need trends and investment signals. Researchers need evidence and themes. Engineers need reproducible, actionable work. Effective systems deliver the right information to the right audience. Start small and iterate quickly - The AI landscape continues to evolve rapidly. Begin with a well-defined problem, measure outcomes, learn from feedback, and iterate as capabilities mature. Looking forward GEM continues to scale across the Azure Portal ecosystem. In addition to the global scorecard, vertical scorecards are now live with seven teams, expanding to the top 20 portal extensions representing more than 80% of portal traffic and feedback, with longer-term plans to extend coverage across the entire ecosystem. The roadmap includes expanded feedback ingestion, streamlined work-item tracking, AI-assisted remediation workflows, stronger evaluation, and a self-improving architecture. Proposed fixes would remain subject to engineer review, approval, and standard release controls before deployment. GEM is also developing AI-assisted pre-release governance workflows for production code that help identify potential quality issues during development. We will share more about these pre-release workflows in a future post. New tools and models will continue to evolve, but the enduring principle remains the same: combine enterprise-scale automation with clear ownership, trusted grounding, and human control. For Microsoft, Customer Zero means deploying these systems in real production environments, learning from the complexities, and sharing those lessons broadly. GEM shows what becomes possible when AI does more than summarize feedback. It helps an organization listen, act, measure outcomes, and continuously learn at customer scale. Microsoft's Customer Zero blog series gives an insider view of how Microsoft builds and operates Microsoft using our trusted, enterprise-grade agentic platform. Learn best practices from our engineering teams through real-world lessons, architectural patterns, and operational strategies for building, operating, and scaling AI-powered systems across the organization.
In a Day (xIAD) Partner Events Program - Train the Trainer Events (DIAD/FAIAD/RTIAD/CDIAD/SQLAIAD)
We invite you to attend an upcoming Train the Trainer session for Microsoft Partners to learn more about the Microsoft In a Day (XIAD) Partner Events Programand how to lead workshops that empower customers to use and adopt Microsoft products. Our Train the Trainer events are designed to provide you with the knowledge and tools necessary to deliver successful Microsoft In a Day (XIAD) sessions. *Please note, participation is restricted to individuals representing a Microsoft Partner organization. You must register using your corporate email address that is associated with your Partner ID. Personal emails will not be approved. ✨ Why Attend? Expert Guidance: Learn from experienced trainers and get your questions answered. Comprehensive Resources: Access all the content and support you need to succeed. Learn more at https://aka.ms/attendTTT 📅 Upcoming Events: Dashboard in a Day This is a one-day, hands-on workshop for business analysts, covering the breadth of Power BI capabilities. September 10 - Americas - Central Time September 18 - APAC - SGT September 25 - EMEA - CEST Fabric Analyst in a Day This is an intermediate-level training designed for Power BI Data Analysts who have at least one year of experience on Power BI but are new to Microsoft Fabric. September 10 - Americas - Central Time September 18 - APAC - SGT September 25 - EMEA - CEST Real-Time Intelligence in a Day This is an intermediate-level training designed for Power BI developers looking to extract insights and visualize streaming and time sensitive data. September 10 - Americas - Central Time September 18 - APAC - SGT September 25 - EMEA - CEST Chat with your Data in a Day This is an intermediate-level training designed for Power BI data analysts and developers to help get their models chat-ready and unlock instant insights using natural language. September 10 - Americas - Central Time September 18 - APAC - SGT September 25 - EMA - CEST New Offer Type: SQL AI App in a Day This is an intermediate-level, one‑day, hands‑on workshop showing SQL Developers and DBAs how to build the database backbone for modern AI apps using Azure SQL. Coming soon! Partner with us! Are you a Microsoft Partner interested in the opportunity to join the program and deliver Microsoft In a Day (XIAD) events? 🔍 Learn more about the program and review partner eligibility criteria: https://aka.ms/xiadpartneropportunity. 📧 Contact the XIAD Program team: xiadevents@microsoft.com 📤 Submit requests to deliver events: https://aka.ms/xIAD/PartnerEvents🚀 Join the FY27 Fabric Engineering Connection Kickoff Call
We're excited to kick off FY27 with the first Fabric Engineering Connection call of the year! Join Tamer Farag, Global Partner Ecosystem Lead for Microsoft Fabric & SQL, as he shares the latest strategy, priorities, and opportunities for partners in the year ahead. What we'll cover ✅ Fabric Partner Community updates and upcoming changes ✅ Analytics on Azure Specialization updates ✅ Frontier Accelerate program highlights ✅ Partner enablement plans and resources ✅ Fabric Feature Partner program updates ✅ FabCon and SQLCon insights and opportunities ✅ Open Q&A with the community 🎁 Bonus: Attend live for a chance to win Fabric swag during the call! Event Details Americas & EMEA 📅 Wednesday, September 9, 2026 🕗 8:00 AM – 9:00 AM PT APAC 📅 Thursday, September 10, 2026 🕐 1:00 PM – 2:00 PM UTC (Wednesday, September 9, 5:00 PM – 6:00 PM PT) How to Join To participate, join the Fabric Partner Community Teams channel: 🔗 https://aka.ms/JoinFabricPartnerCommunity Whether you're a long-time community member or just beginning your Microsoft Fabric journey, this is a great opportunity to hear directly from the team, get aligned on FY27 priorities, and connect with fellow partners around the world. We look forward to seeing you there!✨ FabCon + SQLCon Europe 2026: Partner Know Before You Go Guide is Now Live!
Barcelona is just around the corner, and we're excited to welcome our partner community to the first-ever co-located FabCon + SQLCon Europe experience! To help you make the most of your time onsite, the Partner Know Before You Go Guide is now available. This guide brings together everything partners need to know before arriving, including exclusive partner programming, networking opportunities, important deadlines, and event resources. What You'll Find Inside ✅ Partner Day agenda and key sessions ✅ Partner Happy Hour details ✅ Partner Elevator Pitch Challenge information ✅ Executive Meetings with Fabric & SQL Leadership Team ✅ 1:1 Meetings with the Partner Success Team ✅ AMA Session with the Fabric Partner Success Team ✅ Fabric Certification Recognition opportunities ✅ Testimonial Video nominations ✅ Fabric in the Wild Partner Photo Scavenger Hunt ✅ Cvent Event App guidance and Partner Community information Important Partner Deadlines 📅 September 13, 2026 (11:59 PM PT) Submit your Partner Elevator Pitch entry Request an Executive Meeting with Fabric Leadership Request a 1:1 Meeting with the Partner Success Team Nominate yourself or your customer for a testimonial video opportunity Event Details 📍 Centre de Convencions Internacional de Barcelona (CCIB) 📅 September 28 - October 1, 2026 🌍 Barcelona, Spain Whether you're attending Partner Day, meeting with Microsoft leaders, showcasing your expertise, or connecting with peers from around the world, we've built partner-exclusive experiences to help you learn, connect, and accelerate your Microsoft Fabric business. 📘 Download the Partner Know Before You Go Guide below. ⬇️ We can't wait to see you in Barcelona! #FabConEurope #SQLConEurope #MicrosoftFabric #MicrosoftPartners #DataAndAI #FabCon2026 #PartnerCommunity2026 FabCon + SQLCon Europe Fabric in the Wild: Photo Scavenger Hunt
📸 Fabric in the Wild: Photo Scavenger Hunt at FabCon + SQLCon Europe Heading to FabCon + SQLCon Europe in Barcelona? Get ready to explore, connect, and have some fun with the Fabric community! We're excited to launch the Fabric in the Wild: Photo Scavenger Hunt, an exclusive experience for Microsoft partners attending the event. Whether you're networking at Partner Day, taking in the keynotes, meeting fellow partners, or discovering Barcelona, you'll have the opportunity to capture your adventure and win some exclusive Fabric swag. 🏆 Win Fabric Kicks Complete the challenge by sharing photos from any five scavenger hunt moments and you'll be entered for a chance to win 1 of 3 pairs of Fabric Kicks. 📷 How It Works ✅ Complete any 5 photo challenges from the hunt ✅ Share all 5 photos in a single LinkedIn post or carousel ✅ Include: #FabConEurope #MicrosoftPartner #FabricInTheWildSweepstakes ✅ Tag @Stephanie Chimeziri That's it. You're in! The scavenger hunt is all about celebrating the people, experiences, and energy that make the Microsoft Fabric partner community special. From Partner Day and keynotes to new connections and iconic Barcelona landmarks, we can't wait to see Fabric in the wild through your lens. Drop your photos, showcase your creativity, and share your FabCon story with the community! 🌍 See you in Barcelona. For more information about FabCon + SQLCon Europe, visit aka.ms/fabconeu #MicrosoftFabric #FabConEurope #SQLConEurope #MicrosoftPartner #FabricCommunity #DataAnalytics #FabricInTheWildSweepstakes #PartnerDay #MicrosoftPartnersGLSU Excel add-in fails to load after Office update from Version 2607 to 2608
Our Excel add-in, Process Runner GLSU , fails to load after Microsoft Office updates from Version 2607 to Version 2608. This started August 17, 2026 and is actively growing in scope. Error messages: Excel: Cannot run the macro 'onLoad' VBA: System Error &H80004005 (-2147467259). Unspecified error VBA: Compile error in hidden module: ThisWorkbook Has anyone encountered similar issue with their own custom add-on. If yes, any pointers to fix it. We are seeking help on priority. Thanks, Vrushali Pawale, Sr. Manager, Insightsoftware.
Office Scripts does not refresh Power BI Query live connections in Excel Online
Hello everyone, I've got the following problem and am nearly going insane because it's causing so much problems 🥲 Summary The Office Scripts method workbook.refreshAllDataConnections() no longer refreshes a live connection to a Power BI Semantic Model in Excel Online. Environment Excel Online Office Scripts Power Automate ("Recurrence" + "Run Office Script") Live connection to a Power BI Semantic Model (Analyze in Excel) Expected behavior Calling workbook.refreshAllDataConnections(); should trigger the same refresh as manually selecting Data → Refresh All in Excel Online. Actual behavior The script completes successfully without any error, but the workbook does not requery the Power BI Semantic Model. Additional observations The same workbook worked correctly for approximately two years. The issue started around mid-July 2026. Manual Data → Refresh All in Excel Online refreshes successfully. Refreshing in Excel Desktop also works correctly. The problem occurs both: when running the Office Script manually from the Automate tab in Excel Online when running the same Office Script from Power Automate ("Run Office Script") This suggests the issue is specific to the Office Scripts implementation of refreshAllDataConnections() rather than the workbook, authentication, or the Power BI connection itself. Steps to reproduce Create an Excel workbook with a live connection to a Power BI Semantic Model. Confirm that manual Data → Refresh All refreshes the data successfully. Create an Office Script containing only: function main(workbook: ExcelScript.Workbook) { // Refresh all data connections workbook.refreshAllDataConnections(); } Run the script. Result The script finishes successfully but no refresh is performed. Expected result The workbook should requery the Power BI Semantic Model exactly as when using the manual Refresh All button in Excel Online.Storage Accounts - Networking
Hi All, Seems like a basic issue, however, I cannot seem to resolve the issue. In a nutshell, a number of storage accounts (and other resources) were created with the Public Network Access set as below: I would like to change them all to them all to Enabled from selected virtual networks and IP addresses or even Disabled. However, when I change to Enabled from selected virtual networks and IP addresses, connectivity from, for example, Power Bi to the Storage Account fails. I have added the VPN IP's my local IP etc. But all continue to fail connection or authentication. Once it is changed back to Enabled for All networks everything works, i.e. Power Bi can access the Azure Blob Storage and refresh successfully. I have also enabled 'Allow Azure services on the trusted services list to access this storage account'. But PBI fails to have access to the data. data Source Credentials error, whether using Key, Service Principal etc, it fails. As soon as I switch it back to Enable From All Networks, it authenticates straight away. One more idea I had was to add ALL of the Resource Instances, as this would white list more Azure services, although PBI should be covered by enabling 'Allow Azure services on the trusted services list to access this storage account'. I thought I might give it a try. Also, I created an NSG and used the ServiceTags file to create an inbound rule to allow Power BI from UK South. Also, I have created a Private Endpoint. This should all have worked but still can’t set it to restricted networks. I must be missing something fundamental or there is something fundamentally off with this tenant. When any of the two restrictive options are selected, do they also block various Microsoft services? Any help would be gratefully appreciated.
