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YouTube Live - https://youtube.com/live/tqFCxUx0yFA?feature=share

Session Description: Enterprises are increasingly leveraging artificial intelligence (AI) to transform decision-making, streamline operations, and enhance competitiveness. This session explores how the integration of SAP’s AI-enabled platforms with Google Cloud’s advanced machine learning capabilities is redefining enterprise analytics and accelerating digital transformation.

Through real-world case studies and benchmarks, we will showcase how organizations adopting this combined SAP-Google Cloud stack have achieved up to 22% reductions in operational costs and a 14.5% boost in productivity. Attendees will gain insights into how SAP solutions S/4HANA, SuccessFactors, and Analytics Cloud seamlessly integrate with Google Cloud services such as Vertex AI, BigQuery ML, and Looker to deliver predictive insights, intelligent automation, and scalable analytics.

Key discussion points include predictive maintenance and supply chain optimization using SAP-GCP IoT data convergence, financial analytics with enhanced accuracy and throughput, and ML-driven inventory forecasting that improves turnover and reduces safety stock.

The session will provide practical guidance on data governance, phased AI adoption, and change management strategies to ensure successful implementation. Data leaders, enterprise architects, and transformation officers will leave with actionable approaches to harness hybrid cloud AI ecosystems that enable cross-functional collaboration, accelerate time-to-insight, and future-proof enterprise analytics.

Real-Time Analytics for Electrical Distribution: A Microsoft Fabric Case Study

YouTube Live Link: https://youtube.com/live/KL4cAQUqLvU?feature=share

The electrical distribution industry faces complex challenges balancing inventory availability with cash flow optimization, particularly for contractors requiring on-site access with deferred payment capabilities. This case study demonstrates how Microsoft Fabric's unified analytics platform transformed a large electrical distributor network's operations through integrated real-time data processing, automated insights, and intelligent forecasting.

The implementation leveraged Fabric's Lakehouse architecture to consolidate data from mobile applications, inventory systems, and billing platforms, creating a comprehensive view of supplier-customer interactions. Real-time analytics pipelines processed consumption patterns, payment cycles, and inventory movements, enabling automated replenishment decisions and dynamic pricing strategies. Power BI integration provided stakeholders with actionable dashboards monitoring key performance indicators across multiple regions. Results demonstrated significant operational improvements: inventory accuracy increased by over 30%, cash flow optimization scores rose by approximately 40%, and operational efficiency metrics improved by nearly 25% compared to traditional models. Automated replenishment algorithms reduced stockout incidents by 50%, while billing error rates declined by 60%, substantially improving financial reconciliation processes.

The technical architecture utilized Fabric's data engineering capabilities for ETL processes, machine learning models for demand forecasting, and real-time intelligence for immediate decision-making. Integration challenges with legacy systems were addressed through Fabric's flexible connectivity options and comprehensive data governance features.

This case study illustrates how Microsoft Fabric's end-to-end analytics platform can revolutionize traditional industrial supply chains, demonstrating the platform's capability to deliver measurable business value through unified data management and intelligent automation.

Session Title: Transforming Education Outcomes with Fabric AI Session

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Description: Higher education is undergoing a data-driven transformation as institutions strive to improve retention, equity, and graduation rates. This session explores how AI-powered predictive analytics—built on the Microsoft Fabric ecosystem—enables scalable, secure, and impactful early intervention strategies for student success. Drawing on a study of 91 institutions, the session presents concrete results: AI models integrating structured and behavioral data improved early risk detection by 7.7%, while ensemble methods reached an AUC of 0.802. Real-world implementations—such as at Arizona State University and the University of Kentucky—demonstrated up to 12% gains in retention and significant equity gap reductions. Attendees will learn how Microsoft Fabric components—including OneLake, Synapse Real-Time Analytics, Power BI, and integrated ML tooling—support unified data pipelines, real-time alerting, and ethical model governance. Topics will include how API-first architectures reduced data silos by 65%, how field-level encryption boosted adoption, and how fairness-aware modeling cut bias gaps to under 2%. This session provides a practical roadmap for deploying predictive analytics with Microsoft Fabric’s unified platform. Emphasis will be placed on architectural scalability, secure data collaboration, and measurable ROI, with leading institutions reporting up to 7x returns on analytics investments. Be Ready to Engage: Bring a notepad – this is a content-rich session! Submit your questions beforehand via Comments. Ask questions live via comments or join the stage with camera/mic access – just reach out to us in advance if you’d like to participate on screen.

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Session Title: AI-Powered Customer Service: Human-AI Synergy Driving 287% ROI

YouTube Live Link: https://youtube.com/live/11zFiGxVCWA?feature=share

Session Description: At the intersection of customer experience and AI innovation, this session explores how leading enterprises are transforming customer service operations through intelligent automation and strategic human-AI collaboration. Drawing from insights across 219 global companies and industry examples like Rocket Mortgage, we’ll examine how conversational AI, emotion-aware systems, and AI copilots are reshaping service models cutting handling time by 41.3%, improving first-contact resolution by 36.8%, and boosting customer satisfaction by 22.7%. Attendees will discover how AI copilots trained on massive datasets analyze thousands of customer interactions in real time, enabling human agents to focus on empathy and context-sensitive resolution achieving over 90% accuracy in sentiment interpretation. You'll learn how Microsoft Fabric’s architecture can underpin scalable AI frameworks that deliver millisecond-level intent classification, reduce reporting overhead by 65%, and resolve complex issues 58.1% faster. This session offers evidence-backed strategies to drive agent satisfaction (up by over 33%), achieve 7.3-month payback periods, and realize cost savings exceeding $6.4M annually. We’ll also detail implementation patterns for AI-assisted workflows, predictive documentation engines, and real-time customer insight systems with an adoption rate of over 81.4% among agents. Join to gain a clear roadmap for designing transformative, AI-augmented customer service ecosystems with measurable ROI.

Be Ready to Engage: Bring a notepad – this is a content-rich session! Submit your questions beforehand via Comments. Ask questions live via comments or join the stage with camera/mic access – just reach out to us in advance if you’d like to participate on screen.

Fabric-Powered Retail AI: From Data Lakes to $Billion ROI & Personalization

Join Session Here: https://www.youtube.com/live/8_33kHuC6ho?feature=shared

The retail industry is experiencing an unprecedented AI revolution, with academic research publications exploding from just 12 articles in 2000 to over 847 articles in 2023—a staggering 21.3% compound annual growth rate that signals the transformative power of intelligent systems built on modern data platforms. This presentation reveals how leading retailers are leveraging Microsoft Fabric's unified analytics platform to process 150+ customer attributes simultaneously (versus 10-15 in traditional systems), achieving hyperpersonalization that drives 20-30% conversion rate increases and 34% higher click-through rates through context-aware recommendations. Deep learning-based recommendation systems now constitute 68% of retail AI research, with transformer-based architectures like BERT4Rec delivering 23-31% performance improvements when powered by scalable data lakehouse architectures. Drawing from comprehensive analysis across 29 industries and 112 countries, we'll explore why only 10% of companies achieve significant AI ROI—and how Microsoft Fabric's integrated data platform accelerates success for the winners. The presentation will showcase real-world case studies demonstrating inventory optimization with 30-50% forecast accuracy improvements through generative AI models processing 20-50 feature variables in Fabric's lakehouse, operational excellence with logistics firms achieving 28% average productivity gains through real-time analytics, innovation acceleration with 37% average increase in patent applications post-AI adoption, and customer experience enhancement with 75% prediction accuracy enabling real-time personalization across unified data estates. However, success requires more than technology. Research reveals that 70% of AI success stems from organizational factors, with AI leaders investing 73% more in human-AI collaboration training compared to laggards (23%). We'll decode the five critical organizational learning practices that distinguish AI winners from losers, and how Fabric's collaborative workspace enables these practices at scale. This data-driven session combines cutting-edge research insights with practical Microsoft Fabric implementation strategies, addressing both the $billion opportunities and the ethical considerations reshaping retail's future through unified analytics platforms.

Unlock the power of Microsoft Fabric to build secure, AI-native payment systems that both automate integrations and detect fraud in real time. This session showcases how LLMs automate API spec ingestion, transforming chaotic third-party documentation into executable Fabric orchestrations. Concurrently, Python-based ML models process billions of transactions to establish behavioral baselines, velocity thresholds, and device fingerprints—enabling fraud detection from the very first transaction. A graph analytics layer links device metadata, geolocation, and account behavior to catch coordinated fraud attempts beyond traditional models.

Built using Fabric’s compute fabrics, Dataflows, and containerized microservices, this architecture delivers sub-second latency and seamless scalability. It integrates with CI/CD pipelines for audit-ready compliance and real-time streaming into Fabric dashboards for monitoring and alerts. Ideal for data engineers, platform architects, and AI developers using Microsoft Fabric, this talk provides a clear, practical framework for embedding AI at the core of your data-driven applications.