The potential of artificial intelligence in the energy and utilities sector is enormous, ranging from smarter forecasting and grid optimization to proactive maintenance and better customer experiences.

McKinsey & Company estimates that predictive AI, including analytics and machine learning, could add between $11 and $17.7 trillion to global economic activity annually. Generative AI is expected to unlock another $2.6 to $4.4 trillion.

Companies are rushing to grab the opportunity, but too many are building on unstable ground. According to MIT Technology Review Insights, 82% of senior executives globally say scaling AI and GenAI use cases to drive business value is a top priority.

Here’s the catch: most of those same leaders admit they’re worried about the quality of data feeding their models. And with good reason. As one CIO put it: “AI just doesn’t work without data governance. It’s garbage in, garbage out on steroids.”

In the energy and utilities sector, 60% of executives identify data governance and security as their primary challenge when preparing for AI.

The message is clear: the promise of AI is massive, but so is the risk of getting it wrong. AI isn’t plug and play. It’s a high-value capability that demands a strong foundation. If AI is a skyscraper, data governance is the bedrock. Without clear ownership, quality controls, and traceability, even the most advanced models will fail to deliver.

For energy and utility providers, the cost of error is high. Inaccurate forecasting, particularly in load management, can lead to millions in losses from overcommitments, imbalance penalties, or missed efficiency gains. Studies show that a 1% improvement in load forecast accuracy can save a mid-sized utility up to $1.6 million annually.

Without data governance, analytics teams typically spend 20 to 30% of their time just cleaning and validating data before models are even operational. In some forecasting pilot cases, weather inputs were mislabeled, inconsistent across systems, or missing entirely. In a real-time grid environment, that’s not just inefficient; it’s a liability.

Strong data governance builds trust. Executives worry, and rightfully so, that AI and predictive models might “hallucinate” results if trained on poor-quality data. Clear data policies, ownership, and validation routines are essential to mitigate bias, reduce risk, and ensure AI-driven insights are both accurate and defensible.

In other words, it’s not just about smarter algorithms; it’s about better data.

Data governance isn’t just about setting policies and rules. It’s a strategic framework that underpins every aspect of data management, from architecture and integration to quality, privacy, metadata, lineage, and analytics. It ensures that data is not only secure and compliant, but also usable, reliable, and valuable.

And it delivers results. Organizations that address governance challenges deploy AI three times faster and with 60% higher success rates, according to McKinsey. Those with mature governance capabilities report analytics ROI up to 40% higher than their peers because they spend less time cleaning up data and more time acting on it.

Even without AI, data governance has always been key to unlocking business growth, helping organizations:

• Strengthen analytics and decision-making with trusted data
• Boost operational efficiency and cross-functional confidence
• Reduce regulatory and reputational risk through traceability
• Turn data into a scalable, revenue-driving strategic asset

Done right, governance isn’t a burden. It’s a business enabler.

Data governance isn’t a destination. It’s a journey that transforms raw data into confident decisions and long-term business value.

But getting started can be daunting. Governance cuts across the entire organization, involving people, processes, and technology. Without a clear path, it often feels too complex to act on and too abstract to prioritize.

The good news is that it doesn’t require a massive overhaul to make progress. With the right guidance, organizations can achieve meaningful, measurable results quickly. Many utilities are starting with a high-impact use case, such as load forecasting or customer contactability, and applying governance principles there first.

These focused efforts improve performance and provide tangible proof of value. From there, governance can scale, shifting from an isolated initiative to an embedded capability that drives smarter operations across the business.

• Invest in data governance, not just AI pilots. This isn’t a choice between AI or governance. They go hand in hand.
• Seek expert guidance. Look for partners with proven frameworks, certifications, and hands-on experience.
• Start small and smart. Focus on a high-impact use case to demonstrate value early.
• Scale with confidence. Track and communicate early wins to build momentum.

As experts caution, a lack of trust in data shouldn’t stall innovation. It should ignite efforts to build strong governance.

Because at the end of the day:

AI runs on data. Data runs on governance. And governance is a journey best taken with guidance.

We combine decades of hands-on experience in Data & AI, governance, and advanced analytics with a practical, field-tested approach. Recognized in Gartner’s Market Guide for our impact on the Energy & Utilities sector, we bring certified expertise and proven methodologies that turn strategy into action.

Our Data Governance Subway Toolkit helps organizations launch, accelerate, and scale their governance journey with purpose and impact. Every path is unique, and we’re here to help guide yours.

Let’s keep the conversation going.

Every journey starts with a first step. If you’re exploring how to build a stronger data foundation to scale AI with confidence, let’s talk!

Carolina Charrie

Business Development Manager