What challenges did Shell face before AI implementation?

Shell needed to monitor and maintain over 10,000 pieces of critical equipment worldwide. Manual or reactive maintenance resulted in unplanned downtime, high costs, and operational inefficiencies. There was a need to process and analyze massive volumes of sensor data to detect equipment anomalies proactively. Ensuring environmental and human safety with timely detection and prevention of failures was a priority. Building an internal scalable AI platform was deemed too resource-intensive.

What was the impact of the AI solution?

More than 10,000 pieces of equipment now benefit from predictive maintenance. 20 billion sensor data rows processed per week across 3 million data streams. Over 15 million predictions generated daily, significantly reducing unplanned downtime. AI solution now available industry-wide through a commercial initiative.

Source

MicrosoftProductionEvidence: Low40/100

Shell scales global predictive maintenance across critical equipment

Use case typePredictive maintenanceUpdated Jun 13, 2026

Shell implemented an AI-driven predictive maintenance system across more than 10,000 pieces of equipment spanning its global operations. Leveraging Microsoft Azure, Azure Databricks, and C3 AI, Shell ingests over 20 billion rows of sensor data weekly, running more than 10,000 machine learning models in production. The solution detects anomalies, extends asset life, ensures environmental and human safety, and integrates with digital twin technology for comprehensive asset management. Shell's journey included significant technical and organizational challenges, ultimately creating a scalable, robust solution embedded in workflows. Shell has now made this solution commercially available via the Open AI Energy Initiative, in partnership with C3 AI, Baker Hughes, and Microsoft. The deployment demonstrates real cost reduction, improved equipment uptime, operational efficiency, and a positive environmental impact. Future plans involve extending predictive maintenance to renewables and broader use cases for AI within Shell's businesses.

Organization: Shell
Industry: Energy & Utilities
Location: Global
Published: May 2025

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Shell
Provider: Microsoft
Maturity: Production
Linked source: c3.ai blog

Leveraging Microsoft Azure, Azure Databricks, and C3 AI, Shell ingests over 20 billion rows of sensor data weekly, running more than 10,000 machine learning models in production

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Global Predictive Maintenance for Industrial Equipment
2Automated Anomaly Detection in Oil & Gas Assets
3AI-driven Equipment Health Monitoring

Shell needed to monitor and maintain over 10,000 pieces of critical equipment worldwide.
Manual or reactive maintenance resulted in unplanned downtime, high costs, and operational inefficiencies.
There was a need to process and analyze massive volumes of sensor data to detect equipment anomalies proactively.
Ensuring environmental and human safety with timely detection and prevention of failures was a priority.
Building an internal scalable AI platform was deemed too resource-intensive.

Deployed C3 AI running on Microsoft Azure as the scalable core platform.
Used Azure Databricks for processing large sensor data pipelines.
Developed and operationalized over 10,000 machine learning models for equipment anomaly detection.
Integrated AI predictions into digital twin systems for advanced asset management.
Embedded predictive maintenance capabilities into workflows company-wide, leveraging global community and governance.

Technologies

Microsoft Azure Azure Databricks C3 AI Azure Digital Twins

More than 10,000 pieces of equipment now benefit from predictive maintenance.
20 billion sensor data rows processed per week across 3 million data streams.
Over 15 million predictions generated daily, significantly reducing unplanned downtime.
AI solution now available industry-wide through a commercial initiative.

Architecture

Shell's solution leverages C3 AI running on Microsoft Azure, ingesting data from the Shell Sensor Intelligence Platform, which is based on Delta lake (Azure Databricks). Over 10,000 production ML models analyze data from global assets (including upstream and downstream sites), with anomaly detection predictions integrated into digital twin platforms for visualization and advanced asset management. The models, embedded in operational workflows, generate 15 million predictions per day and scale across Shell's entire asset ecosystem.

Sources & evidence1

Evidence: Low40/100Evidence strength