The Future of Digital Twins & GenAI in The Energy Sector: From Overpromise to Powerhouse

Digital twins are virtual replicas of physical systems that encompass resources, data, and workflows.

Not that long ago, the term was met with skepticism. The phrase “digital twin” conjured images of the holographic ship in Star Wars, becoming a buzzword that promised space-age transformation but fell flat on delivery.

Part of the problem is that, in the early 2010s, digital twins were often portrayed as plug-and-play solutions for real-time insights and operational optimization.

Building digital twins that can provide truly accurate insights requires deep domain expertise, high-quality data, and integration across disparate systems. In other words, the complexity of deploying and maintaining digital twins was underestimated at first.

But that was then.

Today, digital twins are living up to their original promise. Many of the technical challenges have been overcome, and they can sync with real-time data and historians, and even automatically update in near real time. Time-tested digital twins are now delivering such capabilities as early failure detection and grid optimization.

No longer a buzzword, digital twins have become essential for asset-intensive industries, with applications including:

Over the last several years, as digital twins have become commonplace and their value has been realized, their capabilities have also evolved.

Some use cases, such as predictive maintenance using machine learning, 3D modeling and simulation, IoT sensor integration, and basic asset monitoring, while not wholly new capabilities, have become more scalable and accessible. This is thanks to cloud computing and better data pipelines.

But there are also plenty of truly new features.

Platforms like AWS SageMaker now allow drag-and-drop model creation, democratizing access to advanced analytics. And, with integrations to LLM models, digital twin-based software can now provide both prescriptive and predictive maintenance recommendations.

New digital twin features include:

Despite these advances, many companies still rely on spreadsheets or software without prebuilt models. It's still true today that digital twins require deep domain expertise, high-quality data, and integration across systems to deliver accurate results. Without advanced digital twins, maintenance strategies are still at risk of being siloed and reactive.

Contrary to recent AI hype, digital twins have been leveraging AI for over two decades, including GE Vernova’s SmartSignal. SmartSignal’s digital twins are powered by machine learning, a subset of AI, for early failure detection. The combination of digital twins powered by machine learning allows for dynamic thresholds to detect anomalies in critical equipment.

Since 2024, generative AI (GenAI) has dominated headlines, and today it is often the centerpiece of boardroom strategy decks. While GenAI promises major cost savings, digital twins have quietly matured into a foundational technology. According to business consulting firm McKinsey & Company, the two technologies could have a symbiotic relationship:

“Gen AI can structure inputs and synthesize outputs of digital twins, and digital twins can provide a robust test-and-learn environment for gen AI. By combining these technologies, organizations could produce synergies that reduce costs, accelerate deployment, and provide substantially more value than either could deliver on its own.”

This could be especially promising in the energy sector, where digital twins may be the foundation GenAI needs to live up to its hype and realize the cost savings promised.

Even without digital twins, GenAI is helping energy companies with:

But without digital twins, however, GenAI lacks the contextual fidelity needed to make decisions based on actual system behavior. GenAI alone lacks a real-time, analytics-based understanding of assets and systems. It’s great at reasoning over data and language, but not at simulating or mirroring the behavior of turbines, grids, or substations.

Digital twins with analytics provide the “ground truth”—a real-time, dynamic model of physical assets — and this enables:

GenAI without digital twins is like having a brilliant analyst with no access to live data.

Digital twins without GenAI are like having rich data but no intuitive interface or reasoning layer.

Together, they can simulate, predict, and prescribe solutions to some of the most complex challenges in energy, manufacturing, and infrastructure.

Today, several challenges still affect energy companies’ ability to realize the benefits of digital twins:

To help overcome these challenges, GE Vernova is your partner in achieving digital transformation goals.

To reduce the cost and time of development and maintenance, leverage GE Vernova’s Industrial Managed Services (IMS). With IMS, you gain:

Additionally, to deliver dependable early failure detection, the SmartSignal digital twin catalogue (pre-built digital twin models embedded with deep domain expertise in the algorithms) covers more than 370+ common industrial assets. These analytics leverage a wealth of asset class intelligence including known failure modes, instrumentation inputs, operating context, engineering specifications, and GE Vernova subject matter expertise.

Looking ahead, digital twins are evolving into context-aware systems. With embedded AI models, they can now operate in near real-time across vast energy networks. SmartSignal, for instance, is adding prescriptive recommendations using natural language processing.

Energy companies that adopt advanced digital twin software with AI are not just making a technological shift; they’re making a strategic one.

Companies that embrace smart digital twin technology, backed by deep domain expertise, will be better equipped to navigate complexity, anticipate change, and lead in the energy transition.