Enhancing operational efficiency in the energy sector with artificial intelligence

AI applications in the energy sector

Across the energy sector, AI can help ensure operational efficiency by improving safety and reliability and providing more accurate insight and strategies. Along with several other benefits, such as: 

• Predictive maintenance: AI-driven analysis of real-time and historical data forecasts maintenance needs, as opposed to traditional calendar-based approaches. This proactive approach reduces costs and improves reliability.
• Fleet management: AI helps enhance operations across fleets by providing more accurate and detailed insights about real-time operations, enabling more efficient asset utilization, including maintenance and outage planning.
• Parts life extension: Machine learning can analyze customer-specific usage patterns aiming to extend the lifespan of components, reducing waste and costs.
• Material development: Developing new materials can be expensive and time-consuming, but AI can help accelerate the process. By utilizing AI’s understanding of physics and data for quicker quantitative analysis, we can create more sustainable materials faster and cost-effectively.
• Enhanced decision-making: Generative AI can offer assistance to engineers by helping to examine and analyze multi-modal data sources and factors to spot trends and help provide tailored solutions across fleets.

Overcoming the hurdles of AI integration

While AI’s potential is immense, integrating it into operational workflows can present challenges. Many of these hurdles can be grouped into three main buckets: data quality and volume, operator engagement, and trust and transparency. 

AI models rely on vast, high-quality datasets that represent asset operations. Sufficient and clean data is essential so that when a model learns a particular process or how an asset operates, it has the ability to see a snapshot of the operation. Without sufficient and clean data, your model can be incomplete and inaccurate. 

Equally important is operator engagement, as even the most advanced AI models often require human interaction to deliver accurate outcomes. This makes interface design crucial, helping operators understand and trust the model’s outputs while allowing them to interact with and influence the models when needed. By focusing on human-machine interaction, organizations can bridge the gap between AI models and humans, allowing for more improved reliability and adaptability.

Effective operator engagement relies on trust and transparency in AI, which is inherently difficult because it requires placing confidence in a system that operates in ways that are not always transparent to humans. Educational training, providing clean data, and partnering with companies like GE Vernova with decades of experience in AI can help bridge the gap in trust, helping to ensure the successful integration and better adoption of AI systems.

Although integrating AI can present several hurdles, it’s important to remember that there are several continuously increasing benefits as AI’s adoption grows and expands its capabilities in the energy sector.

The future of operational efficiency with AI

Adopting AI is no longer optional for businesses in the energy industry—it’s essential. From reducing operational costs to improving environmental impact, AI can offer transformative benefits. 

GE Vernova’s proven commitment to AI innovation is highlighted in its ongoing investments in advanced analytics and R&D partnerships. GE Vernova is deploying cognitive AI technologies across products and services, like large language models (LLMs) or multi-modal models, unlocking their potential. Additionally, GE Vernova is investing in bringing the extraordinary impact of AI to the physical world with Embodied AI, creating tools and systems that are more safe, reliable, and capable of navigating the complexity and unpredictability of the world today. 

By utilizing AI, GE Vernova delivers unparalleled value to customers, helping to enhance safety, reliability, and sustainability across the energy sector.

Ready to learn more about how AI can help enhance your operational efficiency? In our recent webinar, GE Vernova’s experts shared how to embrace AI with a forward-thinking and more collaborative approach to unlock its full potential.

1) McKinsey & Company. "The State of AI in 2023: Generative AI’s Breakout Year." Accessed January 30, 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-AIs-breakout-year.