Detects fault issues from data and flags them in real time.
Use cases
6
Examples
6
Industries
5
Timeline
4 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
4 cases documented across 37 months (Jul 23 – Jul 26), peaking at 2 in October 2023.
AI Use Cases Hub
1 earlier case before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
3Innovativeness3/5Differentiated3/5 - Differentiated. It describes an IoT telemetry and provisioning architecture using Azure IoT Hub and Event Hubs with anomaly detection/diagnostics, but does not show unusual AI/RAG orchestration beyond standard patterns.
Telefónica, a leading telecom provider in Spain, implemented an advanced IoT platform (HAC) using Microsoft Azure IoT Hub and Azure Event Hubs. The platform centralizes and automates the management of home internet connectivity for over 4.5 million households, with plans to scale to 20 million. By leveraging Azure's device provisioning and event ingestion capabilities, Telefónica improved operational efficiency, predictive maintenance, and customer service. The architecture integrates device telemetry for anomaly detection, enables in-depth diagnostics (including customer self-service), and supports rapid integration of new features. The project included close collaboration with Microsoft experts, ensuring strong data privacy, cybersecurity, and virtually unlimited scalability. As a result, Telefónica drastically cut development times, reduced manual processes, and delivered consistently better connectivity experiences to customers.
4Innovativeness4/5Advanced4/5 - Advanced. Evidence shows real-time predictive maintenance and outage management for grid operations via DERMS on Azure, with Azure IoT edge analytics and Azure OpenAI/Copilot embedded into control-center workflows plus OT/IT cybersecurity with Defender for IoT.
Schneider Electric, in collaboration with Microsoft and Pacific Gas & Electric (PG&E), is transforming the energy and utilities industry by integrating AI, cloud analytics, and IoT solutions.This large-scale initiative focuses on deploying predictive maintenance, real-time outage management, and distributed energy resource management systems (DERMS) using the Microsoft Azure cloud platform.The partnership enables Schneider Electric and PG&E to optimize grid operations, enhance asset performance, increase workforce agility, and manage distributed energy resources for cleaner and more resilient energy delivery.Azure OpenAI Service and Copilot technologies are being used to support control center operations, accelerate decision-making, and improve reliability for utilities customers in North America and globally.Cybersecurity is addressed through Microsoft Defender for IoT, providing holistic protection of critical energy infrastructure across OT and IT.This approach supports broader sustainability and decarbonization goals, delivering new operational efficiencies and cleaner, more reliable energy to millions of customers.
3Innovativeness3/5Differentiated3/5 - Differentiated. Implemented agentless OT/IoT monitoring with centralized Azure Sentinel detection and automated firewall isolation to bridge IT/OT visibility and reduce ransomware/unauthorized access risk across global facilities.
COFCO International, a global agriculture supply chain company, implemented Microsoft Defender for IoT and Azure Sentinel to strengthen its cybersecurity framework, especially for its operational technologies (OT). Facing challenges such as isolated systems and lack of visibility into threats, the organization adopted a holistic approach to gain centralized control of OT/IoT ecosystems globally. The deployment included agentless monitoring, vulnerability assessments, and real-time threat identification with automated responses, effectively bridging the security gap between their IT and OT teams. Enhanced visibility enabled COFCO to mitigate risks like ransomware and unauthorized connections, improve operational processes, and set a foundation for standardizing security across their facilities worldwide.
2Innovativeness2/5Incremental2/5 - Incremental. Migrated to a cloud observability platform to improve real-time fault detection and dashboards, but the described innovation is primarily a monitoring/cloud shift without unusual AI architecture or integration.
Renewi, a European waste management company operating in six countries, modernized its IT systems by deploying Elastic Observability hosted on Microsoft Azure. The previous in-house monitoring software lacked extensibility, robust support, and security. By migrating to Elastic Cloud, Renewi enhanced the efficiency and visibility of its middleware and essential business applications like Salesforce CRM. Non-technical teams now benefit from user-friendly dashboards, which facilitate real-time fault detection and quick resolutions. Additionally, the cloud-based solution reduces energy consumption, aligns Renewi's operations with their sustainability goals, and transforms their IT infrastructure into a predictable operating expense.
3Innovativeness3/5Differentiated3/5 - Differentiated. Use of transfer learning with Amazon Rekognition Custom Labels to build a highly accurate custom damage classification model with significantly fewer annotated images is a differentiated applied innovation.
Autonet, part of Progrits AB, operates in the Auto Insurance industry and aimed to automate the slow and manual car insurance claims damage assessment process.They used Amazon Rekognition Custom Labels to build a highly accurate model for damage level classification with significantly fewer annotated images.The model was integrated into a workflow allowing customers to upload photos of their car damage to get quick and accurate assessments.The assessment results help direct claims either to SMART repair specialists for minor damage or to traditional body shops for more severe damage.The partner repair shops are integrated into the workflow for case handling and final approval of repair category.
4Innovativeness4/5Advanced4/5 - Advanced. The case describes a multi-system security operations transformation combining XDR coverage (365 Defender) with centralized AI-driven SIEM/SOAR (Azure Sentinel) and incident-response automation/feedback loops, indicating more advanced orchestration than basic AI adoption.
VR Group, a Finnish railway company, faced escalating cybersecurity threats after migrating to a multi-cloud IT infrastructure. Recognizing the limitations of traditional security methods, they required a modern, automated approach to defend critical infrastructure and maintain operational resilience. They adopted Microsoft 365 Defender (XDR) to secure identities, endpoints, and applications while providing extended detection and response. Additionally, they implemented Azure Sentinel, a cloud-native SIEM and SOAR solution using AI for security event analysis and automation of incident responses.Accenture Security, a key consulting partner, supervised the migration and collaborated closely on change management, including employee training and alert response processes.These solutions enabled comprehensive visibility into VR Group’s environment, drastically reducing manual reviews and false positives through automated alerts, and helped cultivate enhanced cybersecurity awareness among staff. Training programs were institutionalized to ensure security operations effectiveness and foster continuous learning across teams. The centralized alerting and automation made it possible for VR Group to respond quickly to emerging threats, driving improved operational security and reliability for public rail services in Finland.By leveraging advanced Microsoft security technologies, VR Group modernized its security posture, decreased operational costs, and established a scalable approach for digital infrastructure protection.
How many fault detection use cases are documented?
The AI Use Case Hub documents 6 real fault detection deployments across 5 industries, with 6 detailed company examples you can browse.
Which industries adopt fault detection the most?
Fault detection is most common in Energy & Utilities (33%), Agriculture (17%) and Tech & Comms (17%).
Which countries lead in fault detection?
China leads documented fault detection deployments, followed by Netherlands and Spain.
What technologies are used for fault detection?
Teams most often build fault detection with Microsoft Defender for IoT, Azure Sentinel and Azure.
What AI capabilities power fault detection?
Across the documented deployments, the most common capability patterns are Sustainability (33%), Copilot (17%) and Vision (17%).
What results do companies report from fault detection?
Across the 6 deployments reporting outcomes, companies most often cite speed & agility (83%), risk & compliance (67%) and new product / capability (67%).