AI in Energy: Why Progress Depends on People and Structure

When you talk with energy executives today, there’s no longer a debate about whether artificial intelligence belongs in operations. The question has shifted to how to make it work.

Natalia Klafke, Executive Vice President of Energy & Sustainability at Radix, has spent over a decade helping energy companies modernize complex operations. At Impact 2025, she noted a clear change in the air:

“The event is way bigger this year. The maturity of discussions is getting more interesting. We see concrete achievements and way bolder goals.”

That maturity, however, comes with a reality check — progress is uneven. Many companies have pilots, dashboards, and proofs of concept, but few have built the foundation for sustained impact.

Building Before Scaling

Energy companies are eager to adopt AI, but the speed of implementation often outpaces the strength of their foundations. The organizations seeing meaningful results are those that have invested time in preparing their systems and teams before they deploy new tools.

Strong data infrastructure is the starting point. Without it, no amount of modeling or automation can deliver consistent value. AI systems depend on data that is clean, connected, and contextual. Companies that treat data as a shared operational resource — not a departmental by-product — create a base for improvement that scales across sites and functions.

Equally important is strategy. Every digital initiative should serve a defined operational goal: lowering energy intensity, improving throughput, or reducing unplanned shutdowns. When the goal is clear, data teams and operations can work toward the same target instead of running parallel experiments.

Alignment: The Hidden Bottleneck

The toughest problems in industrial AI rarely appear in the code. They appear in how the organization is structured.

Energy companies operate across layers of safety, engineering, IT, and business management. Each has its own priorities, processes, and pace. When these groups move in different directions, even a technically sound project can stall.

Natalia Klafke has seen this repeatedly. A new analytics tool can be deployed in days, but aligning departments around its purpose and use can take months. The misalignment is costly — it delays adoption, erodes trust, and fragments data systems that were meant to integrate them.

Organizations that handle this well establish cross-functional teams from the beginning. They define a shared set of metrics, give decision-making authority to mixed teams, and make alignment a continuous process rather than a one-time meeting.

Adoption as the Only Proof of Value

The measure of success in industrial digitalization isn’t technical performance — it’s adoption.

In plants and refineries, where processes run 24/7, tools that are ignored or underused might as well not exist. High adoption signals that the people closest to the process — operators, engineers, technicians — see real value in the system. Low adoption, even with perfect code, shows that the solution doesn’t fit the workflow.

For this reason, the most effective leaders track adoption like a production metric. They ask: How many users rely on the system daily? How many facilities have rolled it out? Are the insights influencing real decisions? When those numbers rise, the rest — productivity, efficiency, safety — tends to follow.

Change Management as a Core Function

Technology changes quickly; people and culture take longer. Treating human adaptation as an afterthought is a common cause of failure in industrial AI programs.

Energy companies face a dual challenge: a wave of retirements among experienced staff and the influx of younger, digitally native professionals. Each group has strengths — one brings deep process knowledge, the other fluency with data tools — but bridging them requires intention.

Companies that succeed view change management as operational work, not internal communications. They dedicate time to training, mentoring, and co-designing workflows that balance automation with human oversight. The result is not just better system use, but higher morale and safer operations.

From Human Alignment to System Resilience

Once organizations understand how to align people and processes, the next challenge is maintaining that alignment in the face of constant variability.

This is where the renewable energy sector offers valuable lessons. Renewable operations are dynamic by nature — subject to shifting weather, variable generation, and evolving grid demands. They show, in real time, how fragile even the best digital systems can become without context and adaptability.

Data Abundance and Context in Renewables

In the renewable energy sector, the conversation shifts from scarcity to overload. Turbines, inverters, and grid systems stream continuous data, but without context, it quickly becomes noise.

Forecasting renewable generation now involves more volatility than ever — weather behavior, load shifts, and the rising power demand from AI data centers all interact in unpredictable ways. Models built for yesterday’s stability are struggling to keep up with today’s variability.

Leading operators are adapting by recalibrating models more frequently, combining AI forecasts with human domain expertise, and focusing on resilience rather than prediction alone. The emphasis is shifting from accuracy in a snapshot to adaptability over time.

The same principle applies beyond renewables: resilient systems require resilient organizations. When teams are equipped to question data, adjust assumptions, and collaborate across disciplines, digital tools can remain effective even as conditions evolve.

AI as a New Energy Load

This adaptability will soon matter even more, as AI itself becomes a major factor in the energy equation.

The same technologies being used to optimize operations are driving a surge in electricity demand. Large-scale computing centers that power modern AI now consume vast amounts of energy — sometimes comparable to industrial plants.

This growing interdependence between the energy and technology sectors is forcing a reassessment of what “efficiency” means. Improving algorithmic performance or data center cooling isn’t just a technical issue; it’s now part of global energy planning. Digital efficiency and energy efficiency can no longer be managed in isolation.

No Way Back

AI’s role in the energy sector is no longer experimental. It’s part of the operating fabric — guiding maintenance, predicting failures, balancing grids, and informing strategy. The question is no longer whether it should be used, but how well it is governed.

The companies moving forward understand that technology alone doesn’t change outcomes. Structure, clarity, and trust do. AI’s potential in energy will be realized not through faster adoption of tools, but through more deliberate alignment of people and systems.

As Natalia Klafke put it: “Technology isn’t the challenge. Agreement is.”