Rewiring Energy with AI: From Prediction to Optimization

AI in energy: Attracting global attention

The global energy market is grappling with mounting challenges. Rapid economic growth continues to drive a sharp rise in energy demand, while intensifying climate concerns are pressing the sector to accelerate the shift toward cleaner and more sustainable energy sources. As a result, energy service providers must rethink their strategies: optimizing existing production assets while simultaneously investing in innovation to identify renewable and sustainable alternatives. This dual challenge can be daunting, yet AI is emerging as a powerful enabler to help address these complexities.

Market signals show strong confidence in AI’s potential to reshape the energy sector. The market for AI applications in energy is expanding rapidly, with projections of an average annual growth rate of 36.9%, from roughly $8.9 billion in 2024 to more than $58 billion by 2030 [1]. At the enterprise level, AI adoption is gaining significant traction as energy companies seek to enhance operational efficiency, improve productivity, and meet environmental objectives through use cases such as demand forecasting and predictive maintenance. A survey by IBM indicates that 74% of energy and utility companies have implemented or are actively exploring AI applications [2]. Together, these growth projections and adoption rates underscore the scale of investment and the strong belief in AI’s ability to deliver value across the energy value chain.

AI use cases in energy: Examples from real-world success stories

Cementing plays a critical role in ensuring well integrity, safety, and zonal isolation, protecting both production sites and surrounding areas from contamination. Cementing failures can have severe consequences, including sustained casing pressure, threats to well structural integrity, and potential environmental contamination. Despite this importance, cementing remains highly challenging. Industry research estimates that up to 15% of primary cementing jobs fail [3], often due to incorrect thickening time predictions and suboptimal slurry design.

To address these issues, global energy companies are increasingly adopting AI as a viable solution. One leading oilfield services provider specializing in drilling, cementing, and reservoir optimization partnered with FPT to deploy an AI-powered cementing prediction system. The solution leveraged machine learning models, including FLAML and XGBoost, to predict cement thickening time and the time required to reach ultrasonic cement analyzer (UCA) strength thresholds of 100 psi, 500 psi, and 1,000 psi.

The impact of this AI-driven approach was significant. Prediction accuracy improved from approximately 60% to 79% across key metrics, while median error was reduced to 19%. More accurate thickening time predictions ensured that the cement remained pumpable during placement and developed sufficient strength when required, mitigating risks related to premature setting or excessive delays. Improved UCA milestone predictions further enhanced well integrity by validating cement strength within defined timeframes, supporting long-term zonal isolation.

Beyond technical performance, the AI solution also delivered notable operational efficiencies. By optimizing more than 17,000 laboratory tests per month that would otherwise be needed for manual cement validation, the company substantially reduced experimentation costs and effort. With an average runtime of just 1.6 seconds per prediction, the AI model significantly accelerated the cement design workflow.

AI is increasingly critical in optimizing oil well design and construction. During the planning stage, AI models analyze large volumes of historical drilling data, geological information, and seismic interpretations to recommend optimal designs, such as well trajectories and drilling parameters. Beyond planning, AI can also be applied during the oil well construction phase to analyze real-time data from rig sensors, providing predictive insights that enable engineers to dynamically adjust drilling parameters and optimize operations.

Global energy companies are already realizing tangible benefits from this approach. BP, for example, has been actively leveraging AI to accelerate drilling operations by applying advanced analytics to its extensive seismic datasets in the Gulf of Mexico. Using AI, BP was able to complete seismic data analysis in just 8–12 weeks, compared with up to 12 months using traditional manual analysis methods [4].

Other energy service providers report similar results:

• Devon Energy deployed machine learning to monitor oil rigs across the United States, achieving a 25% improvement in the productive life of its oil and gas wells [4].
• A multinational energy company partnered with FPT to implement an AI-powered well construction solution, achieving a 25% reduction in well construction costs and an 80% decrease in engineering effort.

Machine learning algorithms can learn the normal operating behavior of each asset and flag deviations that indicate corrosion, detecting early warning signs long before leaks occur. Trained on massive datasets, AI is superior at identifying subtle patterns and anomalies that are often invisible to the human eye, enabling earlier and more accurate detection of potential leak sources than traditional manual inspections. This proactive capability allows operators to intervene promptly, preventing resource waste, costly repairs, and minimizing environmental risks.

A practical example is FPT’s Flezi Nergy Dronin™, an AI-powered automation solution for pipeline inspection. The platform leverages drone imagery and advanced analytics to accurately and rapidly detect corrosion across oil and gas assets, including pipelines, flanges, and valves. Compared with traditional manual pipeline inspection, Flezi Nergy Dronin™ is 20% faster at predicting corrosion while achieving an accuracy rate of up to 95%.

Realizing the AI potential with speed and scale

As 2026 approaches, AI adoption has clearly moved beyond the experimental phase and is now under growing pressure to deliver tangible business value. A survey of 3,700 executives shows that 61% of business leaders are facing greater pressure than last year to demonstrate a clear return on investment from their AI initiatives [5]. For global organizations in general, and energy companies in particular, this means they must define a clear AI vision and execute it quickly and at scale.

However, achieving this is far from straightforward. The complexity of AI implementation, especially for energy-specific use cases, demands both advanced technical capabilities and deep domain expertise. As a result, partnering with an experienced technology provider has become a practical way to accelerate AI outcomes. An effective partner must not only excel in AI and data engineering, but also understand energy operations and production processes.

With more than 25 years of experience delivering innovative technology solutions, FPT has established itself as a trusted partner for energy companies worldwide, working with leading organizations such as Halliburton, RWE, and E.ON. To further support the sector, FPT offers Flezi Nergy, a dedicated suite of AI solutions for energy and utilities. Flezi Nergy focuses on high-impact use cases, including:

• Drilling and cementing optimization
• Safety incident prediction
• Well production optimization

Flezi Nergy is part of FPT’s flagship AI-first platform, FleziPT, which is designed to deliver AI solutions at speed and scale. By streamlining workflows with AI tools and accelerating deployment with FPT’s AI-augmented workforce, FleziPT can reduce development time by up to 60% and cut rework by more than 50%. This allows energy companies to move more quickly from AI ambition to measurable business results.

Conclusion

From cementing prediction and well construction optimization to intelligent, drone-powered maintenance, AI is already reshaping how energy is produced, protected, and optimized, turning complexity into a source of efficiency, resilience, and sustainability. Real-world results—higher prediction accuracy, lower construction costs, extended asset life, and faster inspections—prove that AI in energy has moved well beyond theory into measurable business impact. Yet unlocking this potential at scale demands more than isolated pilots; it requires unified platforms, robust data foundations, and partners who understand both algorithms and the realities of the field. As AI races from prediction to end-to-end optimization, the critical question for every energy leader is no longer whether to act, but how quickly and boldly they are prepared to move.