Engineering the Future: How AI Could Solve Energy’s Workforce Woes

The energy industry is at a critical juncture, grappling with both surging demand and a growing talent shortage that threatens progress. As utilities adapt to the needs of an evolving grid, the lack of skilled professionals in key roles, especially engineering, is reaching crisis levels. Can artificial intelligence (AI) be the solution?

A 2019 survey of 17,000 energy professionals highlighted an alarming trend: nearly 60% of respondents indicated that their companies have been affected by skill shortages, with engineering disciplines particularly hard-hit. Sixty-two percent of respondents pointed to engineering as the area most affected by talent shortages. Fast-forward to 2024 and the boom in AI is surely compounding this challenge given the rapid growth of data centers, which are expected to account for up to 44% of new demand in energy and infrastructure between now and 2028. For an industry already grappling with talent gaps, this surge in demand could deepen the talent shortage, creating even more pressure on an overstretched workforce.

Ironically, the very technology contributing to this surge in demand for more skilled workers may hold the key to addressing this crisis. Already, AI has made significant strides across various industries. For instance, 25% of all code at Google is now written by AI, and AI adoption in legal firms has skyrocketed, with 79% of lawyers now using AI—up from just 19% from as recently as 2023. In 2024, 72% of organizations report that they have integrated AI into at least one business function.

Transmission and distribution (T&D) engineering is one of the most diverse and complex areas of the energy sector. T&D engineers face the unique challenge of managing a wide range of deliverables—each in its own utility-specific format—across different workflows and vendors. At first glance, this level of diversity might suggest that no single AI-driven automation solution could effectively streamline such a huge variety of tasks.

Recent breakthroughs in the reasoning capabilities of AI, particularly demonstrated by the release of the new O1 model in ChatGPT, suggest a promising new possibility. Enhanced reasoning is enabling the rise of AI Agents—task-specific AI tools capable of taking real-world actions rather than merely responding to prompts—which could address this challenge. As AI continues to evolve, many experts predict that 2025 will be recognized as the "year of AI agents." Meta CEO Mark Zuckerberg recently observed that there may soon be more AI agents than humans in the workforce, underscoring the growing potential for AI to perform highly specific and specialized tasks.

It should be feasible to build task-specific agents that partially or fully automate a variety of vendor-specific or utility-specific workflows in T&D engineering —ranging from the creation of stringing chart drawings to the generation of Bills of Materials (BoMs). Depending on the mission-criticality of the task being performed, such agents would incorporate a ‘human-in-the-

loop' framework, where humans oversee the most mission-critical tasks, ensuring safety and accuracy while leveraging AI to handle repetitive or time-consuming work.

Gridfusion, a Silicon Valley-based startup, is already piloting this approach. With seasoned T&D engineers collaborating with AI experts from UC Berkeley, Gridfusion has developed a framework that allows AI agents to automate specific T&D workflows. Early results from pilot programs indicate significant improvements in efficiency and accuracy.

The rise of AI may also impact the traditional business model of engineering consultants, especially those who rely on billable hours. In industries like law, where AI adoption is already widespread, firms are shifting from an hourly billing model to flat fees. According to Clio’s 2024 Legal Trends Report, law firms are now charging 34% more of their cases on a flat-fee basis compared to 2016, as AI reduces the time required for many tasks.

In the T&D sector, engineering firms could follow a similar trajectory. By embracing AI to automate repetitive engineering tasks, firms may reduce the need for large teams dedicated to manual deliverables, thus shifting their pricing models toward value-based fees rather than hourly rates even as the existing workforce is retained to deliver a higher volume of work without creating pressures that compromise quality or schedule.

It has become a popular analogy in Silicon Valley to liken AI to electricity—suggesting that much like electricity, intelligence could one day evolve into a universal utility, seamlessly integrated into every aspect of our lives. Google CEO Sundar Pichai has gone even further, declaring that AI is “more profound than electricity.” It is hence fitting that AI itself be applied to smoothen its own ascension by addressing the shortage of skilled professionals capable of designing the electric grid on a scale sufficient to power the world’s next great utility—intelligence.