What challenges did Axpo face before AI implementation?

Decentralized and slow access to operational and maintenance data for over 90,000 grid assets. Difficulty making real-time operational decisions and identifying problems quickly. Manual inspection processes required taking lines out of service, limiting efficiency and impacting safety. Complicated, costly BI dashboard that was not mobile-ready or easy to scale organization-wide. Need for improved communication and coordination during incidents.

What was the impact of the AI solution?

Reduced asset information search times by up to 99%. Increased number of assets inspected per day with drones versus manual climbing (from 5–10 to 20–50). Enabled near real-time identification of problems and geographic fault visualization. Improved safety by reducing the need for manual inspections and line shutdowns. Created foundations for predictive maintenance and offered service to other grid operators.

Source

MicrosoftProductionEvidence: Medium65/100

Axpo centralizes grid asset management for Swiss power grid operations

Use case typeWind farm maintenanceUpdated Jun 13, 2026

Axpo, Switzerland’s leading renewable energy producer, undertook a digital transformation to enhance power grid operational efficiency. Facing challenges with decentralized, slow asset information retrieval over a 2,400 km high-voltage grid with 90,000+ assets, Axpo developed the web-based 'Insights' platform. Leveraging Microsoft Azure Cognitive Search, Azure Maps, Power BI, Azure IoT Edge/Hub, and Azure Data Factory, the platform provides real-time asset data, AI-based drone image analysis, SCADA integration, and incident management, enabling faster and safer asset monitoring and predictive maintenance. Key integrations enabled engineers and maintenance teams to visualize assets geographically, rapidly locate faults, and streamline both manual and drone inspection processes. The Incident Management Module improved crew response and communication. Results include a 99% reduction in asset information search times, safer line inspections via drones, and stepwise progress toward predictive maintenance, supporting grid reliability for Switzerland and Liechtenstein.

Organization: Axpo
Industry: Energy & Utilities
Location: Switzerland
Published: July 2025

Reported outcomes

−99%

timeTime & speed

Strategic outcomes

New product / capabilityCreated centralized grid asset management platformBetter decisions & insightEnabled near real-time fault identificationRisk & complianceImproved safety of inspectionsNew product / capabilityEstablished predictive maintenance foundation

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 99% decrease

microsoft.comJul 8, 2025Customer storyInferred claimMedium evidence strength

Reduced asset information search times by up to 99%.

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

Customer: Axpo
Provider: Microsoft
Maturity: Production
Linked source: microsoft.com

Axpo, Switzerland’s leading renewable energy producer, undertook a digital transformation to enhance power grid operational efficiency

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Centralized Asset Management for Power Grid
2Real-Time Grid Monitoring and Visualization
3Drone-Based Automated Line Inspections

Decentralized and slow access to operational and maintenance data for over 90,000 grid assets.
Difficulty making real-time operational decisions and identifying problems quickly.
Manual inspection processes required taking lines out of service, limiting efficiency and impacting safety.
Complicated, costly BI dashboard that was not mobile-ready or easy to scale organization-wide.
Need for improved communication and coordination during incidents.

Created 'Insights'—a centralized, web-based platform combining all grid asset and operational data with geographical visualization.
Used Azure Data Factory to aggregate on-prem and IoT data into SQL data warehouse.
Enabled fast data search and mapping with Azure Cognitive Search and Azure Maps.
Integrated live SCADA data, incident management, and drone-based inspection images analyzed by AI (Grid Vision by eSmart Systems).
Power BI introduced for advanced analytics and mobile-ready BI.

Technologies

Azure Cognitive Search Azure Maps Power BI Azure IoT Edge Azure IoT Hub Azure Data Factory

Reduced asset information search times by up to 99%.
Increased number of assets inspected per day with drones versus manual climbing (from 5–10 to 20–50).
Enabled near real-time identification of problems and geographic fault visualization.
Improved safety by reducing the need for manual inspections and line shutdowns.
Created foundations for predictive maintenance and offered service to other grid operators.

Architecture

Data from on-premise systems and IoT sensors is aggregated with Azure Data Factory and stored in a SQL data warehouse; Azure Cognitive Search provides querying functionality on the dataset, with Azure Maps handling spatial visualization. Azure IoT Edge and Hub route live data feeds, and Power BI is used for reporting and analytics. The platform integrates AI-based image analysis from drone inspection software and includes an Azure-based incident management module, combining real-time monitoring with historic records for maintenance and operations.

Sources & evidence1

Evidence: Medium65/100Evidence strength