The AI-native utility in 2035

This is the second article in a series about how energy providers can thrive in an AI-powered future.


As AI ushers in a profound transformation of the global economy, the energy sector finds itself in the midst of generational change—managing a historic, AI-fueled demand surge while having to modernize legacy infrastructure and technologies. A decentralized, multidirectional “network of networks”—what Guidehouse dubbed the Energy Cloud more than a decade ago—is already an emergent reality. And AI, even as it exerts tremendous external pressure on utilities, is becoming a powerful driver of internal change.

Indeed, AI should be thought of as the operating system that will enable utilities to realize the full promise of the Energy Cloud, embedding intelligence into every layer of the world’s largest machine—and its operators. 

What will these interconnected, AI-enabled energy networks look like 10 years from now? A clear vision of that future is coming into view. In it, AI is ubiquitous, pervading every facet of society and industry. In this reality—one that is already emerging in data center corridors, distributed energy resource (DER)–saturated markets, and policy-volatile regions—AI moves beyond proofs of concept and pilots and enables operating models in which value accrues less from infrastructure than from intelligent dynamic platforms. Decentralized energy networks are no longer a strategic moat for industry participants. They are a web of connected infrastructure and stakeholders that can be orchestrated to achieve outsized returns: financial, geopolitical, societal, and environmental. 

In the ubiquitous-AI future, AI is integral, not peripheral. It is native to core operations and strategy. In the AI-native utility, intelligence is not layered on top of operations; it is embedded within them. The AI-native utility is not a passive sellers of electrons, but rather an orchestrator of resilience, transparency, and adaptive energy ecosystems. Hyperscalers with behind-the-meter (BTM) generation and storage resources aren’t just utility customers; they’re critical collaborators.  

For AI-native utilities, new business models emerge that empower them to play several broader roles in the economy and in society as a whole: 

1. Guardians of resilience: AI-native utilities become the critical line of defense against outages, cyberattacks, and climate shocks. 
2. Intelligent orchestrators: Like the internet, the grid functions as an ambient and invisible presence—reliable, clean, adaptive, and predictive—serving as a dynamic and always-on resource for a wide array of systems and stakeholders. 
3. Ecosystem stewards: Utility employees evolve from system operators to AI ecosystem architects and stewards of ethical energy, curating how AI interacts with society’s most critical infrastructure.  

The most significant impacts of ubiquitous AI throughout the energy ecosystem will come in the form of speed, adaptability, and coordination at every operational level and across every utility silo. Not only will utilities and their unregulated competitors enjoy a new level of flexibility and automation, but their enterprise, industrial, and residential customers will too, as will regulators. 

The flexibility of the AI-native utility—of a grid that thinks, adapts, and builds trust and transparency—will enable individuals, communities, and businesses not just to withstand disruptions but to thrive in the face of them, as the following scenario demonstrates. 

The path to a ubiquitous-AI future

How can utilities attain this vision of 2035? It starts with moving beyond AI pilots and targeted integrations to ubiquitous AI-enablement across the utility operational landscape. To do that, utility executives will need to accelerate existing innovation initiatives across five dimensions: 

1. Workforce transformation 
Utilities are already grappling with aging workforces, skills gaps, and cultural inertia. Management teams will need to reimagine roles, training, and organizational design. This includes the integration of digital coworkers, upskilling of existing workforces, attracting next-gen talent with AI-native tools, and human-in-the-loop design.

2. Data management and AI governance 
Legacy operational silos have made data management a challenge many utilities have already begun to tackle, but in the AI-native utility, acquisition, cleansing, storage, and sharing of data—both internally and externally—become foundational. AI amplifies both the value of data but also the risk associated with poor governance. Clear ownership, lineage, and quality controls must all be in place, or AI models will make poor recommendations and erode confidence, stalling progress.  

In order for utilities to assume a more ambitious orchestration role, enterprise data governance and cyber-resilience integration should be thought of as grid modernization investments, not just IT initiatives. AI models should be auditable and subject to sophisticated cross-silo governance. In effect, data must be treated as a regulated asset. In a world of accelerating policy shifts, utilities must embed explainability, safety, and equity into AI.  

3. Customer empowerment 
Through smart leveraging of GenAI and behavioral modeling, utilities can build trust and loyalty through adaptive customer engagement. The AI-enabled utility will no longer simply serve ratepayers but will instead partner with real-time energy system participants. By transforming customer engagement, utilities can replace static segmentation with proactive energy coaching, demand shaping, and service adaptability attuned to household needs and community goals. The result is not just higher satisfaction, but demand flexibility at scale.

4. Capital planning 
In the AI-native utility, infrastructure is evaluated not by its physical footprint, but by its ability to sense, learn, predict, and automate. Utilities face a glut of infrastructure investment today that will stretch and potentially break existing manually intensive capital planning processes. Software-first capital planning can improve asset utilization, introduce new valuation frameworks, and align investment with strategic agility. 

5. Orchestration 
The potential for utilities to step in as mediators, orchestrators, and enablers of ubiquitous-AI-enabled energy systems is profound. Already legally obligated to provide efficient and reliable service, utilities have the power to expand their societal and economic role, leveraging distributed grid intelligence to integrate flexible resources like demand response and virtual power plants in an optimized fashion.

AI can help utilities more accurately balance supply and demand at the distribution feeder level. It can also manage load shifting across disparate pools of assets, such as DER clusters, and support greater control over power quality, reliability, and management of the generation mix—all in real time.  

Although there is no one-size-fits-all approach to operationalizing those five priorities, Guidehouse has developed an AI acceleration framework that can serve as a roadmap. It outlines a phased approach, starting with data readiness and early-stage use cases and progressing to deeper AI integration. 

As with every significant technological innovation, discerning hype from meaningful change can be challenging, but the eventual impact of AI on enterprise and industry will be immense. Utilities that approach the changing landscape opportunistically, looking beyond current constraints, will be strategically positioned to capitalize on both the known and unknown contours of an AI-powered future.