Predicting utility risk before failure

A US utility infrastructure services provider used AI-powered predictive maintenance to shift from fixed inspection cycles to risk-based maintenance across 10M+ utility assets, reducing outages from natural decay by 80% and delivering approximately $4 million in annual inspection cost savings.

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Industry

Utilities

Region

United States

Solution

Predictive maintenance

About the client

A utility infrastructure leader supporting critical networks

A leading utility infrastructure services provider, the organization supports electric and telecommunications companies across North America. Behind the networks communities rely on every day is the ongoing work of inspecting, maintaining, and modernizing critical infrastructure. Combining field expertise with engineering and data, the organization helps utilities strengthen the reliability of these networks.

~$4 million

Annual inspection cost savings

Fewer unnecessary inspections helped reduce costs while focusing maintenance resources on higher-risk assets.

80%

Fewer outages from natural decay

Earlier identification of deteriorating utility poles enabled more proactive maintenance and improved service continuity.

10+ million

Utility poles covered

Predictive maintenance capabilities now support more than 10 million utility poles across the network.

At scale

Risk-based predictive maintenance

Maintenance teams can prioritize interventions based on predicted asset risk rather than fixed inspection schedules.

The business challenge

An inspection model that couldn't keep pace with scale

As utility infrastructure expanded across the United States, fixed inspection cycles became increasingly difficult to scale. Manual assessments made it harder to prioritize risk consistently across millions of assets and direct maintenance resources where they were needed most.

10M+

Utility poles at scale

A large, widely distributed infrastructure made manual inspection increasingly difficult to scale.

Fixed inspection cycles

Assets were inspected on predetermined schedules rather than according to condition or predicted risk, leading to unnecessary inspections while higher-risk assets could require greater attention.

Limited asset visibility

Without a continuous view of asset health, teams had limited ability 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.

Manual processes

Inspection findings relied heavily on manual collection and validation, slowing the movement of critical asset information across maintenance teams.

Siloed data

Inspection records, operational history, and environmental conditions existed across disconnected systems, making it difficult to build a complete picture of infrastructure risk.

Delayed risk detection

Emerging deterioration often became visible only after scheduled inspections, limiting opportunities for earlier intervention.

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.

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.

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.

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.

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.

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.

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.

A single view of infrastructure risk

Inspection, operational, and environmental data came together to create a shared understanding of asset health across the network.

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.

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.

Annual inspection cost savings
$4 million

Reducing unnecessary inspections and prioritizing higher-risk assets delivered approximately $4 million in annual inspection cost savings.

Fewer outages caused by natural decay 80%

Earlier identification of deteriorating utility poles enabled proactive intervention, reducing outages caused by natural decay.
.

Utility poles monitored
10 million+

Predictive maintenance capabilities now support more than 10 million utility poles across the network.

From reactive to proactive
Smarter maintenance model

Maintenance teams can prioritize interventions based on predicted asset risk rather than relying solely on fixed inspection schedules.

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.

Risk-based maintenance

Maintenance priorities are driven by predicted asset risk rather than fixed inspection schedules, helping teams intervene before failures occur.

Higher infrastructure reliability

Earlier identification of high-risk assets reduces unplanned outages and improves the reliability of critical utility infrastructure.

Smarter resource allocation

Field teams spend less time on low-risk inspections and more time addressing assets that need immediate attention.

Continuously improving decisions

Every inspection strengthens future predictions, enabling maintenance strategies to become more accurate over time.

Enterprise-wide visibility

A shared view of asset health helps planners, operations teams, and business leaders make faster, more informed decisions.

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.

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