AI success stories

 
Making utility infrastructure more reliable with 

predictive intelligence


By replacing cycle-based inspections with AI-powered predictive maintenance, outages fell by 80%, delivering ~$4M in annual savings and enabling risk-based maintenance for 10M+ utility assets.
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Industry
Utilities
Region
United States
Solution
Predictive maintenance
About the client

An energy management leader that wanted predictive analysis  

Our client is a leading provider of utility infrastructure services, helping electric and telecommunications companies strengthen the reliability of critical networks. Operating across North America, the organization combines deep field expertise with engineering and data to help utilities inspect, maintain, and modernize the infrastructure communities depend on every day.

The measurable impact of predictive maintenance

~$4 million

Annual inspection cost savings
Reduced unnecessary inspections while directing maintenance resources to the assets that matter most.

80% 

Fewer outages from natural decay
Earlier identification of deteriorating utility poles improved infrastructure reliability and service continuity.

10+ million

Utility poles covered
A scalable predictive maintenance capability now supports one of the nation's largest utility infrastructure networks.

At scale

Predictive maintenance
A modern operating model that enables risk-based maintenance decisions across the utility network.
The business challenge

An inspection model that couldn't keep pace with scale

As utility infrastructure expanded across the United States, traditional inspection cycles struggled to keep up. Manual assessments and fixed schedules made it increasingly difficult to prioritize risk, maintain consistency across millions of assets, and optimize maintenance investments.

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10M+
Utility assets
A large, widely distributed infrastructure made manual inspections increasingly difficult to scale.
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Fixed inspection cycles

Assets were inspected on predetermined schedules instead of according to their condition, increasing unnecessary inspections while delaying attention to higher-risk assets.

 

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Limited asset visibility

Without a continuous view of asset health, teams struggled to identify emerging risks early and prioritize maintenance effectively. 

 

Where friction was building

As the utility network grew, the challenge extended beyond inspection itself. Critical information was trapped across disconnected data sources, manual workflows, and isolated decisions. Without a continuous flow of asset intelligence, maintenance teams struggled to identify risk early, prioritize interventions, and coordinate action across the network.
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Manual processes
Inspection findings relied heavily on manual collection and validation, slowing the movement of critical asset information across maintenance teams.
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Siloed data
Inspection records, operational history, and environmental conditions existed across disconnected systems, making it difficult to build a complete picture of infrastructure risk.
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Delayed risk detection
Emerging deterioration often became visible only after scheduled inspections, limiting opportunities for earlier intervention.

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Fragmented decision-making
Maintenance, planning, and field teams lacked a shared view of asset health, making it harder to consistently prioritize the highest-risk infrastructure.
The solution

A platform built in layers, engineered for scale

Delivering predictive maintenance across millions of utility assets required more than advanced analytics. Every layer of the platform was designed to help intelligence move seamlessly from raw data to day-to-day operational decisions.
01
Connected data
The journey began by bringing together inspection records, asset information, and operational data that had long existed in silos. With a connected view of infrastructure, every maintenance decision started from the same source of truth.

02
Predictive intelligence
Instead of waiting for deterioration to become visible, the platform learned to recognize early signs of risk. Maintenance teams could focus on the assets most likely to fail before disruptions occurred.

03
Scalable engineering
The solution was built to grow with the network. A cloud-native foundation ensured reliable performance as millions of utility assets, new data sources, and evolving business needs continued to expand.

04
Operational workflows
Risk insights became part of everyday maintenance operations, helping planners and field teams prioritize work with greater confidence and respond faster to emerging issues.
05
Continuous improvement
Every inspection made the platform smarter. As new data flowed in, predictions became more accurate, enabling maintenance strategies to evolve alongside the infrastructure they supported.
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One platform.
One view of asset risk.
One smarter way to maintain.

 

What it enabled


Instead of responding to deterioration after it appeared, the company set out to anticipate infrastructure risk before it disrupted operations. Together with Nagarro, it reimagined maintenance as a continuous, intelligence-led capability, helping teams focus on the assets that mattered most while making better use of time, budget, and field resources.

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A single view of infrastructure risk

Inspection, operational, and environmental data came together to create a shared understanding of asset health across the network.
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Maintenance driven by proactive risk assessment

Instead of inspecting every asset on a fixed schedule, teams could prioritize maintenance based on predicted risk and business impact.
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A capability built to evolve

With a modern technology foundation in place, the organization can continuously improve how it identifies risk and adapts to changing operating conditions.
The business outcome

Building a more resilient
utility network

The company redefined predictive maintenance across one of the nation's largest utility networks. AI-powered asset intelligence enabled risk-based maintenance, earlier intervention, and more efficient field operations—creating a scalable operating model that continuously improves with new data.

Annual inspection cost savings
$4 million 

Reduced unnecessary inspections while directing maintenance resources toward the highest-risk assets, lowering operational costs without compromising network reliability.

Fewer outages caused by natural decay 80%

Earlier identification of deteriorating assets enabled proactive interventions, significantly improving service continuity.
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Utility poles monitored
10 million+


A scalable predictive maintenance capability now supports one of the largest utility infrastructure networks in the United States.

From reactive to proactive
Smarter maintenance model

Inspection, maintenance, and operational teams now work from a shared view of asset risk, enabling faster decisions and a more resilient approach to infrastructure management..
The technology stack

Built on AI, predictive analytics,
and cloud-native engineering

A technology foundation designed to turn infrastructure data into faster, more accurate maintenance decisions—helping the business scale predictive maintenance with confidence.
Predictive analytics
Decay regression models, simulation, and curve-fitting techniques estimate remaining asset life, helping identify deterioration before failures occur.
Machine learning
Classification algorithms and clustering techniques identify high-risk assets, enabling maintenance teams to prioritize interventions based on predicted failure probability.
High-performance analytics
R (parallelized processing) accelerates large-scale predictive analytics, enabling enterprise-wide analysis across millions of utility assets.
Cloud-native infrastructure
Azure Kubernetes Service (AKS) provides the scalable, resilient foundation required to run predictive maintenance workloads reliably at enterprise scale.
Continuous delivery
Azure DevOps automates testing, deployment, and continuous improvement, ensuring the platform evolves as new data and business requirements emerge.
Containerized deployment
Azure Container Registry (ACR) securely manages containerized applications, enabling consistent deployment across development, testing, and production environments.
Business transformation

Built for predictive maintenance

The transformation extended beyond predictive maintenance. By bringing together data, analytics, and engineering, the company changed how infrastructure decisions are made, moving from routine inspection cycles to a more intelligent, risk-driven operating model that can scale with the business.
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Risk-based maintenance
Maintenance priorities are driven by predicted asset risk rather than fixed inspection schedules, helping teams intervene before failures occur.
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Higher infrastructure reliability
Earlier identification of high-risk assets reduces unplanned outages and improves the reliability of critical utility infrastructure.
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Smarter resource allocation
Field teams spend less time on low-risk inspections and more time addressing assets that need immediate attention.
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Continuously improving decisions
Every inspection strengthens future predictions, enabling maintenance strategies to become more accurate over time.
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Enterprise-wide visibility
A shared view of asset health helps planners, operations teams, and business leaders make faster, more informed decisions.
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Built for long-term scale
A modern technology foundation gives the business the flexibility to expand, adapt, and continuously improve as infrastructure and operational demands evolve.

Build a resilient
utility network.

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