AI in energy is about more than just adopting new technologies
Scaling AI is about reimagining the enterprise and meeting the energy trilemma head on, embedding intelligence across the value chain to secure supply, decarbonize and control costs. Our findings provide guidance for navigating that future.
Data and AI are pivotal in addressing the energy sector’s challenges—rising demand, grid reliability, and renewable integration. These technologies provide insights that drive operational efficiency and smarter decision-making, helping to balance supply and demand dynamically. By predicting maintenance needs and optimizing resource allocation, data and AI help build more resilient, sustainable energy systems. As a result, they empower the sector to adapt to evolving needs and environmental goals, paving the way toward a cleaner future.
At-a-glance insights:
The industry is preparing for an AI future, with early successes
Experimentation is a critical investment area
92% believe that organizations that embrace AI will develop a competitive edge over those who do not
96% are investing in future-focused projects without the expectation of immediate returns
AI adoption in energy has moved beyond pilot projects. Despite differences between regulated and unregulated entities (as well as the specific nuances of individual sub-sectors), the challenges and opportunities around AI tend to be broadly consistent. Companies across the energy value chain are converging on common AI use cases in areas like operational efficiency, asset optimization, safety, sustainability and predictive maintenance.
Data-driven decision making tops the list of AI benefits
How you can realize value from an AI transformation journey
To address these challenges, KPMG introduces the three phases of AI value — a framework designed to guide energy companies through the AI adoption journey. This phased approach provides a structured roadmap, helping the energy industry prioritize investments, align initiatives with business goals, and position themselves effectively in the age of AI in energy. Click on each of the phases below to find out more.
Enable
The Enable phase establishes the foundations for AI adoption and focuses on creating awareness, experimentation and alignment to help ensure the organization is prepared for broader AI integration. This includes developing an AI strategy, increasing AI literacy, learning from initial implementations and more.
Embed
The Embed phase integrates AI into end-to-end value streams and transforms ways of working across the enterprise. A senior leader, supported by a capable transformation office, oversees enterprise-wide change.
Evolve
The Evolve phase transforms enterprises so they can anticipate market disruptions, forming new business models and ecosystems to solve larger, industry-wide problems. Energy companies move beyond internal optimization and become orchestrators of interconnected ecosystems, seamlessly integrating producers, grid operators, industrial consumers, governments and sustainability initiatives.
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