Aaron Dhaliwal, CEO of avua, an AI hiring platform.
As the CEO of an AI-powered talent platform supporting clients across North America, Europe and the Middle East, I’ve witnessed artificial intelligence move from promise to practice across every major industry. Yet I think few sectors embody this transformation as vividly as energy.
AI is now the engine driving the global energy transition. It can predict grid demand, optimize renewables, improve safety and drive down emissions. But amid these breakthroughs, a quieter development is underway—one that is redefining how energy companies find, evaluate and develop the people who make it all possible.
My company operates across six major sectors, and I’ve noticed energy—spanning renewables, utilities, oil and gas and emerging clean tech—is among the fastest-evolving. The workforce powering this transformation looks markedly different from that of even five years ago. The most sought-after professionals are not only engineers and project managers; they are data scientists, AI specialists and digital innovators fluent in both energy systems and advanced analytics.
A new breed of energy engineer is needed.
The modern grid is no longer a static network—it is a dynamic, data-driven ecosystem. Artificial intelligence is enabling utilities to forecast demand, detect faults and integrate renewables with unprecedented precision. This evolution demands a new kind of engineer: one fluent in both energy infrastructure and machine intelligence.
I’ve noticed utilities are increasingly hiring professionals capable of navigating predictive maintenance platforms, digital twins and AI-powered optimization tools. These hybrid experts need to bridge the gap between traditional power engineering and data-driven decision making.
For candidates, showcasing expertise in smart grid analytics, real-time monitoring and digital asset management has become the new baseline.
New intelligence is expanding the types of roles available in renewables planning.
Renewable energy projects are now planned and operated with the precision of algorithmic insight. AI models can simulate solar performance, forecast wind variability and optimize storage, turning vast datasets into actionable intelligence.
This shift is expanding the industry’s talent map. New positions such as renewable data strategist, AI project planner and digital operations lead are emerging as core roles within energy firms. Success in these roles often requires not only technical proficiency but the ability to interpret complex data and turn it into clear operational strategies.
I’ve noticed that forward-looking employers are rewriting job descriptions to reflect this reality. They are prioritizing AI literacy, digital communication and systems thinking as essential competencies for the next generation of clean energy professionals.
Field technicians and maintenance supervisors must balance technology and the human touch.
AI has revolutionized asset management. Turbines, substations and pipelines can now transmit millions of data points daily, enabling predictive maintenance that helps prevent failures before they occur. But technology alone is not enough—the insight must be translated into decisive human action.
Field technicians and maintenance supervisors must evolve into digitally empowered problem-solvers. Those who can interpret sensor data and integrate AI insights into their decision making are fast becoming valuable assets.
I think adaptability has overtaken tenure as the strongest predictor of success. The ability to learn new digital tools quickly—and apply them in operational environments—has become a hallmark of high-performing technical teams.
Smarter markets require smarter traders.
Energy trading is undergoing its own AI-driven transformation. Machine learning now underpins everything from price forecasting to risk assessment, and I’ve found this has redefined what employers look for in their trading teams.
The modern trader needs to be as comfortable analyzing neural network outputs as reading market reports. Energy firms are recruiting professionals who combine quantitative acumen, data fluency and strategic insight—a blend that can drive faster and more informed decisions in increasingly automated markets.
For professionals, fluency in AI analytics, market modeling and algorithmic trading systems is rapidly becoming the new currency of relevance.
Digital operations and globalization are giving way to new virtual roles.
The digitalization of the energy sector has blurred the boundaries of geography and role structure. AI-assisted control rooms, remote monitoring centers and virtual operations hubs are enabling energy companies to manage assets globally, 24/7, with unprecedented visibility and efficiency.
As a result, new categories of work are emerging. Roles such as virtual asset manager, remote systems operator and digital integration lead now sit at the intersection of technology, operations and strategy.
These positions require a blend of technical fluency and communication skills—professionals who can collaborate across borders, interpret AI insights and maintain operational continuity in increasingly digital ecosystems.
Employees will face new challenges.
I’ve had a front-row seat to how AI is reshaping the energy workforce, both its incredible potential and its growing pains. One of the more immediate challenges I’ve seen is the displacement and redefinition of roles that once relied heavily on manual or experiential knowledge.
In upstream operations, for example, AI-driven predictive maintenance systems have reduced the need for on-site inspection teams, while automated monitoring tools in renewables have streamlined asset management to the point where a handful of engineers can oversee what used to require dozens. These efficiencies are powerful, but they’ve also left some experienced technicians struggling to find where they fit in an increasingly data-centric environment.
Beyond job cuts, the subtler challenge lies in the shift of skill expectations. The sector has long valued hands-on operational expertise, but many of today’s energy employers are prioritizing data literacy, programming fluency and comfort with algorithmic systems.
For some mid-career professionals, that’s a steep learning curve, especially in traditional oil, gas and utilities—where digital transformation arrived late. The risk isn’t just technological redundancy; it’s cultural friction. Teams that have spent decades optimizing physical systems are now being asked to trust and interpret digital ones, and that trust doesn’t develop overnight.
I suggest companies focus on reskilling rather than replacing—ensuring AI becomes an amplifier for human expertise, not a substitute for it.
It’s time to build the workforce that powers the future.
Artificial intelligence is not just transforming how we produce and distribute energy—it is fundamentally redefining who does that work and how they are hired, trained and retained.
The companies that invest in the right talent could define the next decade of progress, resilience and sustainability in the global energy economy.
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