This is part one of a two-part look at frameworks for understanding the risks and opportunities of AI-driven power line inspections. Continue reading with part two. This article was based on the concepts and frameworks presented by Faisal Hoque in “Two frameworks for balancing AI innovation and risk,” published in the Harvard Business Review.
The power grid is one of the most vital infrastructures in modern society. But failures in grid assets result in annual costs of $150 billion, or around 0.15% of global GDP. This number continues to rise as demand on the grid grows.
Despite this, traditional inspection methods — such as manual checks, helicopters, and ground crews — remain slow, costly, and highly susceptible to human error. In fact, these methods fail to detect up to 90% of critical defects.
With climate challenges, aging infrastructure, and growing regulatory demands, grid operators can no longer afford to rely on reactive maintenance. A shift toward preventive strategies, powered by artificial intelligence-driven insights, is essential for ensuring resilience, efficiency, and long-term grid stability.
The way forward is clear: drone inspections combined with AI-powered image analysis. This method provides the fastest, most cost-effective, sustainable, and, above all, precise way to monitor, assess, and maintain transmission and distribution grids.
Certain transmission and distribution operators are embracing AI-driven maintenance, with great success. E.ON Sweden, for example, has been leveraging this approach for years, while others have adopted it for specific use cases, such as post-storm assessments. However, the industry as a whole remains slow to adapt.
While the benefits of AI are numerous, many operators hesitate due to concerns about technology readiness. Others dive in without a clear strategy, leading to fragmented, small-scale implementations that struggle to scale, ultimately hindering AI from reaching its full potential.
This cautious approach to change could become a major challenge for power grids over the next 20 years, as demand is expected to double, renewables dominate the energy mix, and the grid rapidly ages. A new approach is essential.
As Faisal Hoque, writing in the Harvard Business Review, put it: “Bridging the gap between aspiration and achievement requires a systematic approach to AI transformation, one that primes organizations to think through the biggest questions this technology raises without losing sight of its day-to-day impact. The stakes could not be higher. Organizations that fail to adapt will become the Polaroids and Blockbusters of the AI age.”
Two frameworks
The world cannot afford for power grids to become the Polaroids or Blockbusters of the AI age; they must continue delivering reliable, affordable, and sustainable energy. To bridge this gap, a structured, balanced approach to AI adoption is necessary. Two complementary frameworks can help guide grid operators: the OPEN and CARE frameworks.
The OPEN framework (Outline, Partner, Experiment, Navigate) equips grid operators with a systematic four-step process to integrate AI into their workflows, enabling a smoother transition from proofs of concept to large-scale implementation.
Meanwhile, the CARE framework (Catastrophize, Assess, Regulate, Exit) provides a ‘sanity check’ to map and manage AI-related risks while aligning adoption with existing organizational guardrails.
To achieve meaningful progress toward scaled AI deployments across the grid, energy stakeholders must first ask: What is preventing adoption, given that the technology is already live, tested, and delivering value to industry peers?
AI adoption is not just a technology or innovation project — it requires cross-departmental alignment and effort. A dual mindset of acceleration and risk mitigation is key to unlocking the efficiency, cost, and precision gains that AI can deliver.
A power line case study
Traditional power line inspection methods — manual checks, helicopters, and ground patrols — are slow, costly, and often fail to detect defects. AI-powered asset analytics fundamentally transform this process by integrating advanced technologies to enhance efficiency, accuracy, and decision-making. Arkion uses the following process:
- Data collection: Drones or helicopters capture high-resolution images, thermal images, and 3D LiDAR data of the power grid assets and lines.
- AI and computer vision analysis: Machine learning models analyze images and 3D data to detect defects such as corrosion, missing components, and vegetation encroachment. AI can process thousands of images in minutes, identifying patterns that would take human inspectors days to review.
- Human verification: AI insights are augmented and cleaned by expert validation. Human inspectors review flagged anomalies, ensuring that critical decisions are based on both automated insights and experienced judgment.
- Actionable insights and integration: The analyzed data integrates into asset management and work order systems, enabling predictive maintenance, risk mitigation, and optimized investment planning.
There are two main benefits of using AI-powered inspections. First, they’re cost-effective. Case studies from the industry suggest that they achieve an average cost reduction per defect found of 85%. And they also allow for more precision. AI-driven analysis detects up to five times more defects than manual inspections; on many more problematic or hard-to-inspect stretches, it discovers up to eight times more defects.
Despite these advantages, resistance persists among key stakeholders, from maintenance and vegetation teams to executives. This is where structured frameworks like OPEN and CARE help align different departments within grid owner organizations to accelerate AI adoption.
Emma Gavala is the chief of staff at Arkion, an AI-powered asset intelligence platform, training computer vision and AI models for global transmission and distribution operators since 2019. A version of this story was published by Arkion. The opinions represented in this contributed article are solely those of the author, and do not reflect the views of Latitude Media or any of its staff.
