AI in energy works in pilots. Now it needs to work in operations.

The limits of isolated AI in energy

Across the energy and utilities sector, billions have been invested in artificial intelligence, yet most organizations are still struggling to move beyond pilots. The issue isn’t ambition, or even the technology itself; it’s that many AI initiatives were never designed to operate inside the realities of live grid environments, complex asset ecosystems and tightly regulated operations.

This has led to a growing sense of “pilot fatigue.” Promising use cases demonstrate value in isolation but stall when faced with the challenge of scaling across fragmented OT and IT landscapes. In a sector where reliability, safety and auditability are non-negotiable, isolated AI simply isn’t enough.

The conversation is now shifting from experimentation to industrialization. The question is no longer whether AI can add value, but how to embed it into the core operational fabric of the enterprise in a way that is trusted, governed and built to last.

The future is an AI-orchestrated, human-led enterprise

The next phase of AI in energy will not be defined by better models, but by how effectively those models are embedded with operational systems.

What’s emerging is a new paradigm: the AI-orchestrated, human-led enterprise. In this model, AI is not a layer sitting on top of the business; it is woven into the infrastructure that runs it. Systems continuously sense, decide and act across generation, grids, pipelines and customer operations, with humans providing oversight, judgment and accountability where it matters most.

A critical enabler of this shift is the evolution from digital twins to what we describe as ‘digital triplets.’

Traditional digital twins mirror physical assets. Digital triplets extend beyond this concept by integrating three dimensions: the physical asset, a real-time data representation and an AI-driven decision layer. This third layer introduces agentic capabilities: AI systems that don’t just analyze conditions but recommend and, in some cases, initiate actions within defined operational and regulatory guardrails.

For example, instead of simply flagging a potential outage risk, a digital triplet can simulate response scenarios, recommend optimal interventions and aid in triggering coordinated workflows across field crews and control systems. This closes the loop between insight and execution, something most AI deployments today fail to achieve.

Embedding AI into operations changes where human expertise is applied. As AI systems take on coordination-heavy, data-intensive tasks such as outage triage, field scheduling and demand forecasting, human operators are free to focus on safety-critical decisions, exception handling and strategic system oversight.

The result is not just greater efficiency, but an ability to achieve meaningful outcomes, including a fundamentally more resilient and adaptive energy system, capable of managing increasing complexity with confidence.

How we build what's next: From vision to value

Reaching this future state requires a coordinated shift in operating models, governance, and accountability. Learning from what is working for others, progress depends on a set of deliberate, production-focused actions:

1. Anchor AI to critical business outcomes: Every AI initiative needs to be built with the understanding of how it will be made operational, as well as its impacts on reliability, cost-to-serve and regulatory requirements. Leading organizations have moved away from technology-first approaches and focus on solving specific, high-value problems in journeys like forecasting, dispatch and asset management.
2. Build on a foundation of trusted data: Leaders in this area elevate data quality and governance to a critical corporate priority. The only way to deliver trusted actions and insights from AI is to build on a unified OT and IT data fabric that is governed, explainable and perpetually maintained.
3. Embed responsible AI by design: In our sector, safety, security and compliance are non-negotiable. Responsible AI, cybersecurity for both IT and OT and data provenance cannot be afterthoughts. For every AI model or agent deployed in a critical environment, these guardrails must be engineered in from the outset.
4. Operate AI for enterprise scale: To move from pilot to production, leaders are adopting modern operating models. This means leveraging MLOps and LLMOps capabilities and considering AI-first managed services to ensure that models and agents remain observable, secure and continuously improving in live environments.
5. Engage in meaningful change management: Embracing the benefits of human-led AI transformation requires proactive workforce engagement and training, data literacy and an innovation culture to empower employees to adopt and effectively integrate AI into daily decision-making and operations.

Leading the transition with confidence

The journey from isolated AI pilots to embedded operational infrastructure is the defining challenge for our industry today. It is more than just technology; it demands deep integration depth across both OT and IT systems and must include accountability for the entire lifecycle, from executive advisory through to the delivery of daily run at scale live operations.

Organizations that make this pragmatic, production-focused shift will be best positioned to leverage enterprise AI to manage increasing system complexity and build resilience at scale.