Enable Predictive Asset Maintenance with AI-Powered Solutions for Utilities

Reduce outages and enhance grid reliability with precision and ease  

For utilities worldwide, the job of asset maintenance has never been more demanding. Research indicates that roughly one-third of U.S. transmission equipment and nearly half of distribution assets are within five years of the end of their useful life. At the same time, power outages are becoming more frequent and severe, driven by deteriorating equipment and increasingly volatile weather. Leaders responsible for transmission and distribution, grid modernization and digital transformation recognize that time-based inspections and largely reactive maintenance can no longer keep pace with stakeholder expectations for resilience, safety and cost control. 

Many utilities have invested heavily in imagery and inspection programs. The related data often sits in silos, as it is procured by different teams, stored in various formats and underused for enterprise-scale predictive maintenance. Meanwhile, artificial intelligence (AI) has matured to the point where computer vision, machine learning and advanced remote sensing analytics can automatically detect asset defects, enrich asset records and forecast failures at a speed and scale impossible with manual methods alone.  

The opportunity now involves connecting these dots: organizing and centralizing imagery, applying AI models to turn pixels into asset intelligence and embedding predictive insights into day-to-day maintenance and capital planning. Utilities can now stand-up AI-enabled predictive maintenance programs that reduce outages and improve restoration times, enhance grid reliability and deliver measurable value across the full asset lifecycle. This enables utilities to stay ahead of the asset management curve with a more proactive approach that doesn’t risk potential service disruption, increased outages or lower-quality service.  

The Predictive Maintenance and AI Challenge 

For many utilities, the core problem in predictive maintenance is a lack of a reliable, up-to-date view of assets. Inspection cycles are often driven by regulatory minimums, such as 10-year intervals for major infrastructure, rather than actual asset health or risk. Large-scale condition assessments are expensive, can run into the millions of dollars and are not always capitalizable, making it challenging to secure sustained funding. As a result, utilities tend to prioritize urgent issues and compliance-driven inspections, leaving little room for systematic, forward-looking programs that identify emerging risks before they become failures. 

At the same time, utilities do not fully to maximize the data they capture. Different groups, including vegetation management, engineering, planning, environmental and operations, often procure aerial, drone, LiDAR, thermal and street-level imagery independently. Each acquisition uses unique formats, resolutions and metadata tailored to its specific purpose with little regard for reuse across the organization. The result is significant “latent value” trapped in unorganized repositories: images that could support asset health analytics remain disconnected from the systems and teams that need them most. 

This fragmented data environment is one of the main reasons AI initiatives struggle to gain traction. Building reliable AI models depends on large volumes of consistent, well-labeled data. When imagery differs significantly in quality, angles and metadata, and when asset identifiers are incomplete or inconsistent, data preparation becomes a bottleneck that makes model development more complicated. Teams are forced to design models solely to manage data discrepancies, rather than focusing on extracting high-value insights from assets. Without clear data standards and governance, proofs of concept remain limited to narrow scenarios and cannot be scaled across circuits, asset classes, or business units. 

For operations, utilities still rely heavily on manual processes that don’t scale. Inspectors must review every image from drone flights or aerial campaigns to identify potential defects, such as corrosion, damaged crossarms, cracked insulators, missing components or oil leaks. For a single pole with multiple high-resolution images. Reviewing multiple high-resolution images for just one poles takes several minutes. When multiplied by hundreds of poles per circuit and thousands of poles across the system, this effort quickly overwhelms available staff. Even after identifying issues, the process of translating findings into work orders, prioritizing them, and routing them to the right crews or subject-matter experts can be slow and inconsistent.  

For CIOs, CTOs, grid modernization leaders and senior T&D executives, these factors combine into a common set of challenges: an incomplete understanding of asset health, fragmented data, barriers to scalable AI deployment and manual workflows that cannot keep up. 

How AI‑powered Imagery Management Improves Predictive Maintenance

Turn Imagery into an Enterprise Asset 

The first step toward AI-enabled predictive maintenance is transforming scattered image files into a managed enterprise asset. Utilities typically start with a cross-functional assessment of existing imagery and inspection data: what has been captured, who uses it, where it is stored and how it is connected to asset records. From this baseline, an image management strategy can be defined that sets standards that cover preferred formats, resolutions, georeferencing practices, metadata requirements, retention policies and access controls. 

Utilities can then centralize imagery in a secure, cloud-based remote sensing platform. Aerial, drone, LiDAR, thermal and ground images are ingested, filtered for privacy (e.g., PII blurring), cataloged and linked to asset IDs and locations. Easy-to-use interfaces and integrations with work management systems then allow engineers, planners and inspectors to search by asset, circuit, location or time, and to view historical imagery alongside other asset data. This unified view provides the backbone for advanced analytics and AI. 

Advanced Remote Sensing for Automated Detection 

Once utilities organize and link imagery to assets, advanced remote sensing analytics can be applied to automate much of the condition detection work. Techniques such as orthorectification, 3D reconstruction, change detection and spectral analysis enable the system to highlight structural issues and anomalies without requiring humans to scrutinize every pixel. For example, remote sensing can flag leaning structures, vegetation encroachment, missing components, discoloration patterns associated with corrosion and thermal signatures.  

These analytics provide a first layer of automated triage, reducing the volume of imagery that requires manual review and focusing attention on the most likely areas of concern. When combined with asset criticality and location-specific risk factors (such as wildfire exposure or storm-prone regions), remote sensing outputs help utilities prioritize inspections and follow-up work where they have the highest reliability and safety impact. 

Computer Vision and Machine Learning at Utility Scale 

Computer vision extends these capabilities by teaching AI models to recognize specific asset conditions directly from images. In a typical deployment, models are trained on labeled images to detect and classify defects such as damaged or cracked insulators, frayed conductors, missing equipment or structural degradation. Instead of scanning every image manually, engineers can review only those instances the model has flagged, with tags indicating the issue and asset component.  

The automation can be layered. A first level focuses on anomaly detection, which simply tells users where something looks unusual. A second level classifies the anomaly, mapping it to known condition categories and associated maintenance practices. A third level applies severity scoring, assigning a risk level based on defect type, location, asset criticality and environmental context. At this stage, the system can automatically generate work orders or inspection tasks in existing enterprise asset management (EAM) or work management systems, complete with structured descriptions and recommended next steps. 

Machine learning models can then incorporate historical failure data, maintenance history and environmental factors to estimate the remaining useful lifespan and the probability of failure for asset populations. Utilities move from reactive repairs and rigid cycles to condition-based and risk-based maintenance strategies, optimizing the timing and scope of work. Predictive insights also support more targeted capital planning, highlighting where replacement or reinforcement will deliver the greatest risk reduction per dollar invested.  

Forecasting Predictive and Preventative Maintenance 

When computer vision, remote sensing analytics and machine learning are combined, utilities can build robust, repeatable processes to forecast and plan for predictive and preventive maintenance. For instance, drone or aerial imagery collected over representative circuits can be analyzed automatically to identify and classify known defect types across hundreds of poles. Detections are stored in a centralized platform, linked to asset IDs and made available through intuitive interfaces for engineering review. 

Over time, repeated image captures and detections provide a time series of condition data for each asset. Machine learning models can analyze how quickly specific defects progress under different environmental conditions, loading profiles or maintenance histories, enabling utilities to forecast when issues are likely to reach critical thresholds. Maintenance teams can then schedule interventions proactively, coordinating outages, crews and materials to minimize customer impact. This also supports regulatory engagement by providing objective evidence for risk-based maintenance and investment strategies.  

Governance and Change Management  

For utilities looking to deploy successful AI applications, change management and governance matters. They should proceed in phases aligned with clear decision points and business outcomes. The journey typically begins with an assessment of existing data and a tightly scoped proof of concept focused on a few high-priority circuits or asset classes. This proof-of-concept validates data sufficiency and tests remote sensing and computer vision models. It measures key metrics such as reductions in manual review time, improvements in detection accuracy, and the quality of prioritization.  

Next, a pilot phase expands the scope to include multiple business functions, such as maintenance, vegetation management, planning and operations. The scope should also include deeper integration with core systems such as GIS, EAM and outage management. During this phase, utilities refine data standards, user workflows, roles and responsibilities, as well as model monitoring and retraining processes. Finally, lessons learned from the pilot inform the design of a full-scale program with defined governance, performance targets and continuous improvement mechanisms, ensuring AI becomes an embedded capability rather than a one-off project. 

Benefits of AI‑powered Predictive Asset Maintenance

AI-enabled predictive maintenance delivers multidimensional value when it is thoughtfully integrated into utility operations, planning and digital transformation efforts. By turning imagery and asset data into a strategic enterprise resource, utilities can better manage risk, stretch limited capital and operations and management budgets and demonstrate tangible improvements in reliability and resilience. 

Operational Reliability: Predictive maintenance minimizes unplanned outages by identifying potential equipment failures before they occur, ensuring consistent service delivery. 

Cost Efficiency: By shifting from reactive to predictive maintenance, utilities can reduce emergency repair costs, extend asset life and optimize workforce deployment. 

Data-Driven Decision Making: AI models analyze imagery and asset data to provide actionable insights, enabling smarter asset management and capital planning. 

Regulatory Compliance and Safety: Proactive maintenance helps utilities meet regulatory standards and safety requirements by ensuring equipment operates within prescribed parameters. 

Sustainability Goals: Efficient asset management reduces waste and supports environmental sustainability by minimizing unnecessary maintenance inspections, material consumption and asset replacements.