Uses sensor and operational data to forecast equipment failures before they occur. It helps reduce unplanned downtime, maintenance costs, and disruption to operations.
Use cases
150
Examples
60
Industries
9
Timeline
17 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
96 cases documented across 37 months (Jul 23 – Jul 26), peaking at 20 in May 2025.
AI Use Cases Hub
33 earlier cases 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.
3.3Innovativeness3.3/5Differentiated3.3/5 - Differentiated. This is a solid applied data-and-AI operations platform: predictive maintenance, real-time analytics, and GenAI log interpretation are combined in a production transit setting. It is more advanced than a simple dashboard or chatbot, but it does not show the unusual multi-agent or frontier architecture needed for a 4+ score.
Delhi Metro Rail Corporation (DMRC) built a Data Center of Excellence on Google Cloud to move from reactive maintenance to predictive maintenance across its metro network.The solution uses Vertex AI and Gemini in Vertex AI to identify early fault patterns and interpret depot maintenance logs, while BigQuery and Dataflow unify data and support real-time operational analytics.DMRC monitors 300+ standardized KPIs and aims to reduce downtime for passengers while improving maintenance planning and decision-making.
3Innovativeness3/5Differentiated3/5 - Differentiated. A solid industrial ML deployment with centralized data engineering and predictive models, but it is a fairly established pattern versus the more advanced multimodal or agentic AWS cases in recent calibration examples.
Georgia-Pacific, owned by Koch Industries, produces paper and tissue parent rolls at manufacturing facilities across North America. The company needed to reduce tears and breaks in converting lines, cut unplanned downtime, and predict equipment failure 60–90 days in advance.To address the challenge, Georgia-Pacific built an AWS-based advanced analytics solution centered on Amazon S3, Amazon EMR, and Amazon SageMaker. Real-time machine data was streamed into a central S3 data lake, transformed with EMR, and used to train ML models that recommend optimum machine speeds and detect risk of failure.The solution also helped Georgia-Pacific consolidate disparate production data and expert knowledge into a centralized analytics approach that more experienced operators and central support teams could use to improve production decisions.
2.7Innovativeness2.7/5Differentiated2.7/5 - Differentiated. This is a substantial but fairly standard cloud IoT modernization: Paytm replaced a legacy fleet-management setup with AWS IoT services and custom device management features, similar in sophistication to other production IoT migration cases rather than a novel AI architecture.
Paytm (One97 Communications) uses AWS IoT services to manage and secure its fleet of IoT payment-processing devices.The company migrated from a legacy cloud provider to AWS in a few months and built scalable device authorization, registration, communication, monitoring, and remote management capabilities.The solution supports over-the-air and broadcast updates, improves visibility into device health, increases availability, and reduces fraud in merchant payment workflows.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. This is a scaled but fairly standard industrial ML deployment on managed AWS services; the novelty is in breadth and operational scale rather than a new model or architecture. Compared with recent SageMaker cases, it is closer to incremental platformization than to advanced generative or agentic systems.
ENGIE Digital built the Robin Analytics and Agathe predictive maintenance platforms for thermal power plants and B2B customer equipment.The platforms use AWS services including Amazon SageMaker, Amazon S3, AWS Glue, and Amazon Athena to develop, train, and deploy predictive maintenance models at scale.The architecture supports a large number of assets and models while emphasizing scalability, security boundaries, and controlled costs.
3Innovativeness3/5Differentiated3/5 - Differentiated. Innovative migration to a scalable, event-driven serverless analytics platform enabling near real-time predictive maintenance with collaborative AI model development.
Lufthansa Technik, a global aircraft technical services provider, rebuilt its AVIATAR analytics platform to deliver scalable, cost-efficient, real-time event-driven architecture for predictive maintenance and technical operations.The migration from a self-managed platform to Google Cloud serverless managed services enabled on-demand scaling, reduced infrastructure costs by 50%, and improved stability.Google Kubernetes Engine, Cloud Run, AI Platform, and Notebooks enable real-time ETL processing, data modeling, and collaborative machine learning model development.The new platform supports faster development of analytic use cases, better insights delivery in minutes, and stronger cross-team collaboration across the engineering and data science teams.
3Innovativeness3/5Differentiated3/5 - Differentiated. Differentiated applied innovation by integrating IoT sensor data and AWS AI services for predictive maintenance at a large automotive manufacturing company, improving operational excellence.
Toyota Motors North America addressed the challenge of modernizing predictive maintenance to detect equipment anomalies early, avoid unplanned outages, and improve productivity.They implemented an IoT-based predictive maintenance system that collects real-time sensor data and applies AWS AI services for anomaly detection and asset health visibility.Specifically, they leveraged AWS IoT SiteWise and Amazon Lookout for Equipment to gain insights and make data-driven maintenance decisions, resulting in reduced unplanned equipment downtime and enhanced productivity.
2Innovativeness2/5Incremental2/5 - Incremental. Implementation of established AWS predictive maintenance AI and IoT services to optimize manufacturing equipment uptime and costs.
Commonwealth Bank of Australia faces challenges with equipment failure and degradation causing unplanned downtime and high maintenance costs in manufacturing operations.They implemented a predictive maintenance solution using AWS technologies to optimize equipment performance and extend asset lifespan by predicting failures before they occur.
3Innovativeness3/5Differentiated3/5 - Differentiated. Use of AWS IoT services to rebuild a highly scalable IoT platform for elevators with a significant impact in reducing operational issues and enabling new AI-driven innovations, demonstrating differentiated applied innovation.
KONE, a global leader in elevator and escalator industry, built a robust and scalable IoT platform using AWS IoT Core and related services to connect and monitor a growing fleet of smart devices.The challenge was to improve operational efficiency, reduce maintenance callouts, and support a large device base with highly available cloud architecture.The solution involved migrating to AWS IoT Core, AWS IoT Device Management, AWS IoT TwinMaker, Amazon Simple Storage Service, and AWS Professional Services to enable near-real-time monitoring, provisioning, and predictive maintenance.The impact included a 40% reduction in customer-reported issues and entrapment incidents, improved provisioning success near 100%, device fleet scale increasing from 100K to 500K, and foundation for AI and digital twin innovations.
3.4Innovativeness3.4/5Differentiated3.4/5 - Differentiated. This is a differentiated but not breakthrough rail-maintenance analytics deployment. Compared with recent Alibaba Cloud modernization cases and common enterprise RAG-style assistants, it adds a trusted data layer, vector search, and a domain chatbot for safety-critical operations, but the architecture remains a focused pilot rather than an advanced multi-agent system.
SMRT, Singapore’s leading public transportation provider, is running a pilot project using Oracle Cloud Infrastructure (OCI) Enterprise AI and Oracle Autonomous AI Database to improve rail maintenance.STRIDES Technologies developed JARVIS, an intelligent analytics platform that unifies maintenance, train performance, sensor, and asset lifecycle data from multiple standalone systems into a single trusted source of truth.The platform uses machine learning through a generative AI chatbot interface and vector search to support predictive maintenance, faster fault resolution, and more informed operational decisions.
4Innovativeness4/5Advanced4/5 - Advanced. TwinThread's Predictive Operations Center uses a multi-component real-time architecture (IoT sensors, Azure ML, SQL, App Service, Kubernetes) with anomaly detection and actionable insights to reduce downtime.
Toray Plastics (America) Inc. faced challenges with manufacturing line instability and costly downtime caused by film breaks, which impacted production quality and resulted in waste.The company needed a solution to reduce downtime and improve overall manufacturing efficiency.
How many predictive maintenance use cases are documented?
The AI Use Case Hub documents 150 real predictive maintenance deployments across 9 industries, with 60 detailed company examples you can browse.
Which industries adopt predictive maintenance the most?
Predictive maintenance is most common in Manufacturing (63%), Energy & Utilities (12%) and Automotive (12%).
Which countries lead in predictive maintenance?
Germany leads documented predictive maintenance deployments, followed by United States and Global.
What technologies are used for predictive maintenance?
Teams most often build predictive maintenance with Azure AI, Azure IoT and AI.
What AI capabilities power predictive maintenance?
Across the documented deployments, the most common capability patterns are Sustainability (15%), Vision (12%) and Agent (12%).
What results do companies report from predictive maintenance?
Across the 150 deployments reporting outcomes, companies most often cite new product / capability (75%), speed & agility (54%) and customer experience & trust (42%). Where impact is quantified, the strongest evidence is in time & speed: a median −26.2% across 14 reported metrics.
