As artificial intelligence rapidly transforms industries, LinkedIn projects “AI engineer” will be the fastest-growing job in 2025. Across sectors, executives increasingly recognize that operating model innovation and workforce transformation are critical to unlocking AI’s strategic potential.
For utilities, the stakes are high. While pursuing ambitious goals like autonomous edge agents and adaptive grid orchestration, they must also tackle immediate priorities: reliability, resiliency, and rising power demand. Achieving these goals requires more than technological innovation — it demands building organizational capabilities that deliver immediate impact while establishing a foundation for future strategic transformation. The same utilities that are powering AI’s energy needs are also adopting the technology to reshape business models, transform operations, and find better ways to structure teams and scale opportunities.
The era of power sector AI experimentation is evolving into one of structured, scalable approaches. Utilities are no longer merely dabbling in artificial intelligence; they are shifting toward embedding it into their organizational DNA. Centralized leadership, agile operating models, and specialized roles are becoming the cornerstones of efforts to drive measurable impact.
While it would be premature to completely reinvent the power sector through AI-driven operating models, significant evolutionary shifts are underway. Research and implementation reveal not only high-value use cases and potential benefits, but also clear emerging trends in how utilities are strategically reshaping their operational models to harness AI’s transformative potential.
The rise of the Chief AI Officer and cross-functional delivery pods
The C-suite is evolving, with new roles emerging to drive AI innovation. The rise of the Chief AI Officer, or CAIO, reflects a growing recognition among some utilities that artificial intelligence requires not only technical expertise but also strategic oversight at the executive level. Many major U.S. investor-owned utilities already have CAIO, or else are planning for the role.
Whether established as a standalone position or combined with other responsibilities, the CAIO’s mandate typically includes setting a clear AI vision, creating governance frameworks for responsible AI use, and developing robust workforce enablement strategies. Centralized leadership enables utilities to avoid fragmented experimentation, and instead ensure AI initiatives are consistent and aligned with overarching corporate objectives.
Sam Altman famously predicted the potential for aritificial intelligence to enable a one-person, billion-dollar company. But the operational reality for utilities demands a more nuanced approach. Most companies in this sector will require sophisticated, cross-functional teams to strategically deploy and extract comprehensive value from AI technologies.
Beyond the strategic oversight of CAIOs, utilities are increasingly adopting cross-functional project teams, often referred to as “AI pods” or “AI squads,” to accelerate AI deployment. These models draw inspiration from leading tech companies like Amazon, Microsoft, and Spotify, where small, autonomous groups work collaboratively to rapidly develop and iterate products.
In the power sector, pods focus on rapid prototyping, iterative problem-solving, and building minimum viable products. The experimental nature requires that these pods move with agility, and help utilities refine solutions through continuous feedback.
With nearly a third of generative AI projects expected to fail, some utilities see rapid prototyping as critical to success. By combining a CAIO with the pod model, they are moving away from siloed, monolithic initiatives to more dynamic, scalable approaches that align with strategic goals.
Defining AI roles and building multi-disciplinary teams
Meanwhile, utilities are increasingly defining and investing in specialized aritficial intelligence roles to bridge the gap between technical capability and operational impact.
Power companies are actively planning for AI-focused positions spanning technical, strategic, and user-centered perspectives. Roles such as AI product managers and solutions leads ensure alignment between business objectives and AI initiatives, while AI business analysts translate complex requirements into actionable roadmaps. Looking ahead, the sector may see the rise of new roles — think AI ethicists, trainers, safety engineers, human-AI workflow architects, compliance officers, and more.
This strategic alignment marks a shift from isolated pilot projects to integrated, enterprise-level AI deployment. Utilities are building internal artificial intelligence teams to take control of proprietary data and develop tailored solutions for specific operational needs. This approach creates customized AI applications aligned with organizational goals while building on investments in data science capabilities. For instance, select utilities have scaled their data science teams from as few as five members to over 100 within a few years. The growing availability of open-source libraries has further accelerated product development, although the quality and availability of training data remain critical priorities.
Others are partnering with external vendors, focusing on hiring professionals who can integrate off-the-shelf tools into business workflows rather than developing proprietary platforms. This hybrid approach is emerging as the most common strategy, balancing speed, innovation, and resource efficiency.
Human-centric design and knowledge sharing
Establishing technological trust has long been essential for utilities, built on dependable and transparent systems. This principle now extends to AI deployments, with human-centric design playing a key role. Through journey mapping, prototyping, and iterative testing, utilities are ensuring that AI solutions are not only technically robust but also practical and easy to adopt. Design teams are developing intuitive user interfaces and clear decision pathways while exploring concepts like explainable AI (XAI) to demystify outputs and build operator confidence in AI-driven decisions.
According to recent research, 95% of energy professionals expect artificial intelligence to increase demand for human skills — a finding that runs contrary to popular fears of automation replacing jobs. This shift is already evident within utilities, where AI tools are being designed with a focus on usability, transparency, and explainability.
This need for accessibility is underscored by workforce demographics. According to the Bureau of Labor Statistics, the country’s utility sector employs nearly 1.4 million individuals, with 47% aged 45 or older. As this significant portion of the workforce nears retirement, AI deployments are focused on bridging generational gaps and ensuring that technologies are both scalable and approachable for all employees.
Studies from the Brookings Institution and other research bodies highlight that AI’s most immediate and tangible benefit lies in enhancing personal productivity—enabling workers to accomplish more in less time. As productivity-focused AI tools gain traction within power companies, knowledge-sharing and enablement programs are emerging as key to adoption.
Roles such as AI enablement specialists are pivotal in democratizing access to AI tools. These professionals lead the charge in developing training programs, including reverse mentorship programs where digitally-native employees’ partner with experienced utility operators, fostering internal AI communities, and equipping employees with the skills needed to effectively leverage AI in their workflows.
Workshops, bootcamps, and enablement toolkits—often created in collaboration with technology partners like Microsoft, Nvidia, and AWS—are key to building a culture of AI fluency within utilities. These technology providers offer toolkits tailored to industry-specific use cases, alongside specialized workshops and certifications for enabling artificial intelligence. Such initiatives are empowering employees across departments to gain proficiency with AI tools, fostering a more innovative and collaborative ecosystem.
Technology integration and future outlook
Artificial intelligence is driving utilities’ digital transformation by enabling real-time data integration across information technology, operational technology, and enterprise systems. Power companies are adopting flexible cloud technologies that process operational data securely while enabling advanced AI training and analytics. This technological evolution enables AI systems to process diverse, multi-modal data streams and generate actionable insights. But robust governance and cybersecurity frameworks are essential to both sensitive operational data and ensure compliance with industry regulations.
These advancements are elevating the importance of core technical functions within power companies. Teams responsible for IT/OT integration are increasingly central to AI adoption, driving growth and investment in technical departments. As utilities embed AI into their operating models, the ability to manage integrated data systems has become a cornerstone of sustainable AI-driven transformation.
Despite progress, utilities still face significant challenges. Talent shortages remain one of the most pressing issues, with skilled AI professionals in high demand across industries. This challenge is particularly acute in the utility sector, where AI engineers who combine deep technological expertise with comprehensive power systems knowledge represent an exceptionally rare talent pool.
Historically, utilities have adapted to emerging technologies by tweaking operations — introducing new departments, roles, or heads of innovation, digital transformation, and service offerings. To support these shifts, some have adopted managed services, hybrid models, or service agreements to enable new ways of working. But amid these changes, the core organizational structures of power companies have largely remained intact. Defined enterprise, support, and operational functions — ranging from power delivery to customer services — have persisted.
AI could fundamentally change this dynamic. However, the pace of change remains uncertain. In leaner and less regulated sectors, some companies are already reimagining their organizational structures, processes, and cultures around a symbiotic relationship between humans and AI. This vision includes fluid, project-based teams that form and dissolve around specific goals, with AI acting as a connector. In this future model, management roles evolve into AI coordination functions, focusing on harmonizing human workflows with AI outputs to drive seamless collaboration.
In our research, Utility AI Insights, we mapped archetypes of AI adoption across North American utilities. These archetypes were defined by two key dimensions: AI’s strategic centrality, and its operational deployment.
One year later, the framework remains relevant, revealing distinct patterns of AI integration. Utilities are increasingly restructuring their organizational capabilities, developing strategic competencies, and constructing robust resource pipelines with a deliberate, long-term approach to unlocking AI’s transformative potential.
David Groarke is managing director for Indigo Advisory Group, and collaborated with Latitude Intelligence on the 2024 Utility AI Insights report. The opinions represented in this contributed article are solely those of the author, and do not reflect the views of Latitude Media or any of its staff.
