Unexpected equipment failures create significant operational and financial impact. According to NIST research, preventable maintenance issues cost U.S. manufacturers an estimated $119.1 billion annually through lost production, downtime, and quality-related losses.
The challenge is not the lack of data. Critical assets already generate signals through sensors, SCADA systems, maintenance platforms, ERP systems, and OEM performance data. The challenge is turning these disconnected signals into timely insights.
Join the experts at Kanerika, Microsoft, and Esri for an insightful session, to explore how AI-powered asset intelligence helps enterprises analyze asset behavior, detect early degradation, estimate Remaining Useful Life (RUL), and make proactive maintenance decisions.
Asset-intensive industries rely on equipment reliability to keep operations running. However, traditional maintenance approaches often miss early signs of degradation hidden across operational data, maintenance records, and engineering systems.
AI-powered asset intelligence connects asset data, OEM benchmarks, and historical insights to detect risks earlier, predict equipment health, and improve maintenance decisions before failures occur.
See how an AI-powered digital twin combines OEM data, real-time signals, maintenance history, and asset context to understand equipment health. The demo will show how AI identifies degradation patterns, predicts Remaining Useful Life (RUL), and recommends proactive maintenance actions.
AI-powered asset intelligence requires connected data, predictive models, and operational context.
Microsoft Fabric provides the unified data foundation to bring together asset telemetry, maintenance records, enterprise systems, and analytics workloads.
Esri ArcGIS adds location intelligence by incorporating geographic and environmental context into asset analysis.
Together, these capabilities help organizations create AI-driven insights that explain not only what is happening with an asset, but also what is likely to happen next.
Sharad Kumar | Director – Digital Transformation at Kanerika
Sharad leads digital transformation at Kanerika, driving enterprise data strategy across AI, ML, big data, governance, and advanced analytics. He manages multi-million dollar portfolios and advises C-suite executives across industries.
Pardeep Singla | Partner Solution Architect at Microsoft
Pardeep Singla is a Digital Cloud Solution Architect at Microsoft with expertise in Microsoft Fabric. He helps organizations design and implement modern cloud and data solutions that drive operational outcomes.
Sarah Battersby | Principal Product Manager at Esri
Sarah is a Principal Product Manager at Esri, leading ArcGIS GeoAnalytics products that bring spatial analysis to big data and cloud workflows. With a PhD in Geography and a background as a Principal Research Scientist at Tableau, she’s dedicated her career to making geospatial data easier and more trustworthy for everyone to use.
Designed for leaders responsible for asset reliability, operations, and digital transformation, this webinar explores how AI can help predict failures, improve maintenance planning, and optimize critical asset performance.
Ideal for CTOs, CDOs, operations leaders, maintenance teams, industrial AI teams, and data leaders.
Discover how AI-powered digital twins can help your organization predict failures earlier, improve asset reliability, and make maintenance decisions with greater confidence.
Join Kanerika, Microsoft, and Esri to explore the future of AI-driven asset intelligence.
