AI is no longer a distant promise for utilities. It’s already helping grid operators streamline inspections, identify defects, and generate insights from asset data. These use cases offer real value, but common AI misconceptions often lead to AI being applied too narrowly and evaluated in isolation, without thinking through the larger system it needs to serve. An IEEE report states that up to 80% of AI projects fail to deliver value, and 87% never make it into production¹. Many of these failures are tied to insufficient, siloed, or ungoverned data, these don’t get fix by only focusing on better AI models.

That’s where progress can slow down. When AI is seen as the main objective rather than a component of a broader strategy, utilities can risk investing in tools that don’t scale, models built on poor-quality data, and insights that never reach frontline operations.

This article unpacks some of the most common AI misconceptions for utilities, and shows how shifting the mindset toward developing grid-wide intelligence vs just putting a magnifying glass on AI, can drive real, operational outcomes.

Once the POC works, are we ready to scale?

Most utilities start their AI journey with a proof of concept (POC). Pilot projects often succeed because they’re run in a bubble with controlled data, clear goals, and limited variability.

In the real world, scaling means not just the AI models, but the entire system: data quality, validation loops, human workflows, and the people. POCs don’t prove readiness, they prove potential. That’s an important difference.

That’s why it’s critical to partner with a team with proven operational experience at scale, because scaling requires more than good AI. It demands a holistic understanding of data, utility operations, and what it takes to make innovative technology work under real operational pressures and changing conditions.

Does more data mean better AI results?

Utilities sit on mountains of asset data, but volume isn’t the issue. Enough data is key, but quality matters far more than quantity. According to a study, 70% of AI projects fail to meet their goals in large part due to issues with data quality². To succeed, you want to know exactly what data you’re working with, how it’s been used, and whether it’s reliable. That requires version control, structured datasets, and a clear distinction between training, testing, and validation sets.

We have proven that in practice, more data isn’t always better. At eSmart Systems, we have reduced certain datasets from 500,000 to 100,000 images and achieved better outcomes by focusing on quality over quantity. By removing noise, correcting labels, and structuring data more effectively, the team achieved more accurate and explainable results. Uplifting data quality led to stronger model performance and greater trust in the results.

Should AI be the main goal?

AI isn’t the goal, better decisions are. High model accuracy means little if it doesn’t improve how you manage risk, respond to outages, or plan investments.

The real shift happens when utilities stop implementing AI as the destination and instead focus on building grid intelligence – structured, connected data that powers the entire operation. Inspections can be a key part of this shift, by capturing consistent, high-quality asset data that feeds a central digital model of the grid. With a central digital asset, operational teams can identify defects and inventory gaps in one platform, while planners prioritize investments based on condition, not assumptions. It also enables faster responses to weather events and ensures regulatory reports are grounded in real inspection data and not estimates.

Some major utilities (read more here) have made that leap, turning inspection programs into digital asset platforms that now drive maintenance, capital planning, and regulatory reporting.

Why look at the bigger picture?

Utilities face unprecedented challenges: load growth, renewable integration, aging infrastructure, and extreme weather. The old model: manual inspections, disconnected data, reactive maintenance – can’t keep up.

Grid intelligence platforms powered by AI, like Grid Vision, don’t just modernize inspections. They turn inspections into structured intelligence that supports every layer of grid operations. With visibility of asset condition down to component level, grid operators can act before problems escalate, avoiding outages, extending asset life, and allocating resources where they matter most. Focus on decisions, not just AI hype. Treat data as a strategic asset, not a byproduct. Building the intelligence layer your grid needs means investing in technology and processes that support risk mitigation at scale. AI plays a role, but shouldn’t be the only goal. Operational value is unlocked when data, insight, and action come together to drive smarter decisions for a stronger, more resilient grid.

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Source¹: https://ieeexplore.ieee.org/document/10572277/metrics#metrics Source²: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-data-leaders-technical-guide-to-scaling-gen-ai?