The hardest part of modernizing enterprise BI is NOT the dashboard
For my team, a whole lotta enterprise BI modernization discussions start with the visualization layer, mostly around: Which dashboard should be rebuilt? Should the reports use Direct Lake? Should the organization move to a Lakehouse? How should Power BI capacity be configured? Those are important questions, but there is another one that tends to surface later: Where should the business logic live? In a multi-source environment, the same KPI can be derived from SAP, operational systems, spreadsheets, or application databases. If each report implements the calculation independently, moving the data into a modern platform doesn't automatically create consistent reporting. A more maintainable pattern is to separate the responsibilities: Ingestion: bring source data into the platform with appropriate batch or event-based patterns. Transformation: clean, conform, and standardize data before it reaches reporting. Business layer: define reusable measures and business rules in the semantic model. Security: apply access rules at the appropriate data or semantic layer rather than duplicating them across individual reports. Visualization: keep reports focused on analysis and user experience rather than embedding large amounts of transformation logic. Microsoft Fabric, in that same environment, makes it feasible to bring several of these capabilities into one unified platform, but the architecture still needs clear boundaries between them. I have found this distinction especially important when upgrading reporting environments that have accumulated years of spreadsheet logic and report-specific calculations. For those working on enterprise BI modernization, where do you draw the line between data transformation logic and semantic-model/business logic? And what has worked best for keeping KPI definitions consistent across multiple Power BI reports?
Engineering an Azure Data Layer for Odoo Analytics Without Changing the ERP
One thing I have learned from working on ERP-driven data projects is that the ERP is rarely the place where you want to solve every analytics problem. We saw this firsthand in a manufacturing engagement where Odoo was being used across finance, inventory, procurement, and manufacturing, but reporting still involved substantial manual extraction and Excel-based consolidation. The interesting engineering question wasn't “How do we replace the ERP?” It was: “How do we build a reliable analytical layer around it without changing the transactional system?” That shaped the architecture. We used Odoo APIs and Azure Data Factory for ingestion, landing the data in Azure Data Lake Storage Gen2. From there, the data was organized into Bronze, Silver, and Gold layers: Bronze: Source-aligned Odoo data was retained with minimal transformation. Silver: Data was cleansed, standardized, and prepared across finance, inventory, procurement, and manufacturing domains. Gold: Analytics-ready data was prepared for downstream reporting and consumption. Azure Databricks with Spark handled transformation and KPI standardization, while Synapse/SQL layers provided structured analytical models for reporting. Power BI datasets consumed the prepared data, with Row-Level Security applied for role-based access. Why did this separation matter for the project? The important architectural boundary was: Odoo → transactional operations Azure data platform → ingestion, storage, transformation, standardization Power BI → analytical consumption That separation meant the reporting layer did not have to repeatedly extract and reshape data directly from the ERP. It also gave us a dedicated place to handle historical data and standardize information across business domains before it reached the reporting layer. What changed? The implementation automated 15+ Odoo-based reports and provided governed Power BI dashboards to 60+ business users. More importantly, teams moved away from repeated ERP extraction and spreadsheet consolidation toward a reusable analytical data layer. For our team, the bigger takeaway was that ERP modernization does not necessarily mean modifying the ERP. For teams building Azure data platforms around ERP systems, how are you drawing the boundary between API ingestion, historical storage, transformation, and analytical modeling?
In a Day (xIAD) Partner Program - Train the Trainer Events - Azure Data In a Day Workshops
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. October 16 - APAC - SGT November 13 - EMEA - CEST December 15 - AMERICAS - CST 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. October 16 - APAC - SGT November 13 - EMEA - CEST December 15 - AMERICAS - CST 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. October 16 - APAC - SGT November 13 - EMEA - CEST December 15 - AMERICAS - CST 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. October 16 - APAC - SGT November 13 - EMEA - CEST December 15 - AMERICAS - CST 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. November 6 - AMERICAS - CST November 20 - EMEA - CEST December 18 - APAC - SGT New Offer Type: Planning in a Day This is an intermediate-level, one‑day, hands‑on workshop for Business Analysts, providing foundational training to build and manage planning, forecasting, and budgeting solutions using Microsoft Fabric Planning. November 5 - AMERICAS - CST New Offer Type: SQL Modernization in a Day This is an intermediate-level, one‑day, hands‑on workshop for DBAs and Data professionals, providing foundational training to modernize SQL environments through assessment, migration, optimization, and AI-ready architectures. November 19 - AMERICAS - CST 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✨ 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 Partner Success Team ✅ Fabric Certification Recognition opportunities ✅ Testimonial Video nominations ✅ Fabric + SQL in the Wild Partner Photo Scavenger Hunt ✅ Cvent Event App guidance and Partner Community information 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 + SQL 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 + SQL 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 or a SQL Swag Kit 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 or 1 of 3 SQL Swag Kits. 📷 How It Works ✅ Complete any 5 photo challenges from the hunt ✅ Share all 5 photos in a single LinkedIn post or carousel ✅ Include: #FabConEurope #SQLConEurope #FabricSQLPartnerSweepstakes 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 + SQLCon story with the community! 🌍 See you in Barcelona. For more information about FabCon + SQLCon Europe, visit aka.ms/fabconeu #FabConEurope #SQLConEurope #FabricSQLPartnerSweepstakes
What would be the most useful restaurant KPIs to track in Power BI?
I'm looking at the problem from the perspective of a restaurant that has sales, menu-item, customer, and ordering data but doesn't necessarily have a dedicated data analyst. Beyond total sales, I'm curious about metrics such as average order value, food cost, item popularity, peak ordering times, table turnover, and menu performance. For restaurants experimenting with AI, which metrics do you think are most useful for generating practical recommendations rather than just producing another dashboard?
Turning Nonprofit Data Into Action: A Technical Guide to Power BI
Nonprofits generate data constantly — but it’s often scattered across Excel files, CRMs, survey tools, and program systems. Power BI provides a unified analytics layer that connects, cleans, models, and visualizes this data in a secure, scalable environment. It is part of Microsoft’s broader analytics stack designed to turn raw data into actionable insight. This guide dives deeper into the technical foundations required to build nonprofit‑ready analytics solutions. Power BI Architecture for Nonprofits Power BI consists of three core components: 1. Power BI Desktop Power BI Desktop is the primary authoring tool used to create data models and interactive reports. These reports are then published to the Power BI Service for sharing and collaboration. Used for: Data modeling (building semantic models) DAX calculations (measures and calculated columns) Visual design (interactive reports) Data transformation using Power Query 👉 Power BI Desktop overview 2. Power BI Service (Cloud) The Power BI Service is the cloud-based platform for collaboration, distribution, and consumption of analytics, enabling teams and stakeholders to securely access and interact with data insights Used for: Publishing reports from Power BI Desktop Sharing dashboards and reports Scheduled data refresh Row-Level Security (RLS) Workspaces, apps, and governance 👉 Get Started Creating in the Power BI Service - Power BI | Microsoft Learn 3. Power BI Gateway (Optional) The gateway ensures secure data transfer between on‑premises systems and Power BI cloud services. Used when connecting to on‑premises data sources (SQL Server, local Excel files, etc.). Acts as a secure bridge between cloud services and on‑premises data Enables scheduled data refresh and live (DirectQuery) connections Keeps sensitive data within your local network while enabling cloud-based analytics 👉 On-premises data gateway - Power BI | Microsoft Learn Core Data Preparation: Power Query Power Query is the data preparation and ETL (Extract, Transform, Load) engine behind Power BI. Capabilities: Connects to multiple data sources Cleans and reshapes data Creates repeatable transformation pipelines Supports hundreds of data transformations 👉 Power Query overview Power Query standardizes data and automates preparation workflows, reducing manual effort. Advanced Nonprofit Use Cases 1. Multi‑Program Outcome Dashboards Nonprofits often manage multiple programs with different metrics. Power BI enables unified reporting through: Star schema modeling Fact tables (attendance, outcomes, surveys) Dimension tables (participants, programs, locations) 👉 Star schema guidance: Understand star schema and the importance for Power BI - Power BI | Microsoft Learn 2. Donor Analytics With Predictive Insights DAX (Data Analysis Expressions) is a formula language used to create dynamic calculations and measures inside Power BI data models. Using DAX, AI visuals, and advanced analytics features, nonprofits can: Identify donor churn Analyze giving trends and likelihood Segment donors by behavior Calculate and track lifetime value 👉 DAX reference 3. Volunteer Workforce Analytics Track and optimize: Volunteer hours by skill Retention trends Training completion and effectiveness Event staffing gaps and workforce allocation 👉 Power BI HR analytics sample (workforce trends & attrition): Human Resources sample – Power BI 4. Grant Reporting Automation Power BI enables nonprofits to automate and standardize grant reporting processes, reducing manual effort and improving accuracy. Capabilities: Automatically refresh data using scheduled refresh in the Power BI Service Standardize reporting through reusable dashboards and report templates Automated export and distribution 👉 Configure scheduled refresh – Power BI 👉 Export and automate report delivery with Power Automate How to Build a Nonprofit Impact Dashboard (Advanced) Step 1: Build a Data Model Import data (Excel, SharePoint, SQL) Create core tables: FactOutcomes DimParticipant DimProgram DimDate Define one‑to‑many relationships between dimension and fact tables This follows a star schema approach, which improves performance, scalability, and reporting clarity. 👉 Power BI data modeling guidance Step 2: Create DAX Measures Example measures: Total Participants = COUNT(DimParticipant[ParticipantID]) Total Outcomes = SUM(FactOutcomes[OutcomeValue]) Outcome Rate = DIVIDE( [Total Outcomes], [Total Participants], 0 ) Active Participants = CALCULATE( DISTINCTCOUNT(DimParticipant[ParticipantID]), FactOutcomes[Status] = "Active" ) 👉 DAX reference Step 3: Build Visuals Use report visuals to present key metrics and patterns, then pin selected visuals to dashboards in the Power BI Service for executive-level monitoring. Cards → KPIs (total participants, outcomes) Line charts → trends over time Maps → geographic impact Matrix tables → structured reporting for funders 👉 Intro to dashboards for Power BI designers - Power BI | Microsoft Learn and Overview of Power BI reports - Power BI | Microsoft Learn Step 4: Publish and Automate Publish to Power BI Service Configure scheduled refresh Apply Row-Level Security Share dashboards with leadership, funders, and board members 👉 Configure scheduled refresh - Power BI | Microsoft Learn and Tutorial: Get Started Creating in the Power BI Service - Power BI | Microsoft Learn Final Thought Power BI gives nonprofits a modern, enterprise-grade analytics platform without requiring a full data engineering team. By combining: Structured data modeling Automated data preparation Strong governance practices nonprofits can transform fragmented data into clear, actionable insight that drives mission impact.How Azure uses AI to turn feedback into improved customer experience
Authors: eshaanbhattad, jenniferjhan, lakshminarasimha, bharadwajr 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.🚀 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!GLSU 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.
