What challenges did Furnas face before AI implementation?

Significant penalties due to transmission asset unavailability Need for compliance with Brazil's National Electricity Agency (ANEEL) regulations Operational inefficiency in managing a vast infrastructure of 21 hydroelectric plants, transmission lines, and substations. High costs due to unscheduled maintenance or unforeseen asset failures.

What was the impact of the AI solution?

Reduced penalties and fines associated with transmission line unavailability Accelerated implementation of digital solutions in operational workflow Enhanced compliance with regulatory directives Improved operational efficiency across energy infrastructure

Source

MicrosoftLive source

Furnas fosters new predictive maintenance culture with AI and Azure

Use case typePredictive maintenanceUpdated Jun 13, 2026

Furnas, a major Brazilian energy company, embarked on an initiative to leverage AI in predictive maintenance using Azure technology. Partnering with Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) and Radix, the project aimed to reduce penalties linked to the unavailability of transmission assets. The objective was not only operational efficiency but also compliance with regulatory directives. AI models were developed and integrated into production-ready cycles, creating monetizable digital products. This approach enabled the company to accelerate operational efficiency while fostering innovation within the energy sector.

Organization: Furnas
Industry: Energy & Utilities
Location: Brazil
Published: November 2023

Reported outcomes

Strategic outcomes

Risk & complianceReduced penalties and improved complianceSpeed & agilityAccelerated digital solution deploymentNew product / capabilityDeveloped monetizable digital productsInnovation & cultureFostered innovation in energy operations

Primary read

Use case focus

Showing 2 of 2

1Predictive maintenance
2Operational efficiency

Significant penalties due to transmission asset unavailability
Need for compliance with Brazil's National Electricity Agency (ANEEL) regulations
Operational inefficiency in managing a vast infrastructure of 21 hydroelectric plants, transmission lines, and substations.
High costs due to unscheduled maintenance or unforeseen asset failures.

Utilized Azure AI for predictive maintenance and modeling
Partnered with PUC-Rio for AI model development
Radix implemented architecture and data integration processes
Delivered production-ready AI models in monetizable digital cycles

Technologies

Azure AI

Reduced penalties and fines associated with transmission line unavailability
Accelerated implementation of digital solutions in operational workflow
Enhanced compliance with regulatory directives
Improved operational efficiency across energy infrastructure

Architecture

AI models were built by PUC-Rio, focusing on asset unavailability penalties, and implemented by Radix using Azure architecture. The project delivered monetizable digital products via interconnected workflows and robust architecture.

Implementation partners1

Radix

Sources & evidence1

Live sourceStill referenced

The case's original source is still reachable.