The AI in energy market is estimated to be valued at USD 15.45 Bn in 2024 and is expected to reach USD 46.92 Bn by 2031, growing at a compound annual growth rate (CAGR) of 17.2% from 2024 to 2031. The AI in Energy market is expected to witness strong growth over the forecast period owing to the increasing focus on digital transformation across the energy sector.

Market Driver - Increasing Demand for Energy Efficiency and Optimization

The global energy demand has grown significantly in the last few decades owing to rapid industrialization and urbanization across major economies worldwide. At the same time, there has been a growing awareness regarding environmental sustainability and mitigating the impacts of climate change. Energy providers and regulatory authorities are under increasing compulsion to transition towards more efficient and cleaner sources of energy production.

AI promises to play a pivotal role in improving overall energy efficiency through advanced optimization of supply and demand. By monitoring equipment operations in real time and detecting anomalies, utilities can avert breakdowns and outages. This enhances reliability of services.

Numerous companies across the energy value chain have already started implementing AI. Industrial consumers are leveraging AI to reduce energy footprint of their facilities through continuous optimization and automated control of diverse equipment/machinery based on evolving needs.

Overall, the capability of AI to massively improve energy efficiency through better optimization holds huge potential to transform global energy systems.

Market Driver - Advancements in AI Technologies Enhancing Predictive Capabilities

Rapid developments are occurring in the fields of deep learning, computer vision, natural language processing and other AI sub domains in recent times. This is expanding the frontiers of applications across a wide array of domains including AI in energy sector.

AI capabilities around predictive maintenance and forecasting are particularly promising for optimizing asset performance as well as demand/supply planning. Advanced deep learning models trained on IoT sensor data from oil rigs, pipelines, power plants etc. can now identify subtle patterns to predict equipment failures even before any symptoms arise. This helps plan servicing in advance avoiding unexpected breakdowns and outages. AI is also revolutionizing predictive analytics for weather-dependent renewable assets.

Natural language processing capabilities of AI are finding applications as well. Chatbots and voice assistants are being deployed for automated customer support, taking off routine queries. This frees up live agents to focus on more complex issues, proving to be an important driver for the AI in energy market.

Market Challenge - High Implementation Costs of AI Solutions

One of the major challenges that AI in energy market faces is the high implementation costs associated with it. Deploying advanced AI technologies requires significant investments in data infrastructure to collect, store, manage, and process large volumes data on a continuous basis. It also needs highly skilled talent pool of AI engineers, data scientists and domain experts to develop customized AI solutions for various energy value chains.

Maintaining and upgrading these complex AI systems over time requires dedicated budgets and resources. For many energy companies, particularly utilities operating on thin margins, making such large upfront investments without clear mid-term return on investments can be difficult.

Furthermore, integrating AI technologies with existing IT systems of most energy firms requires revamping outdated infrastructure as well, adding to implementation expenditures. High costs thus remain a major impediment for the widespread roll-out of AI in energy market.

Market Opportunity - Growing Adoption of Renewable Energy Sources

The growth in adoption of renewable energy sources such as solar and wind energy provides a huge opportunity for AI to play a transformative role. As the share of intermittent renewable energy on the overall grid increases, maintaining grid stability and reliability becomes more challenging for operators.

Advanced AI and machine learning algorithms can help optimize renewable energy production and integration into the grid in real time. This helps operators plan and balance the grid better.

AI in energy applications is also being used for predictive maintenance of renewable assets, improving their operational efficiency. With more countries and regions committing to higher renewable targets to reduce carbon emissions, the energy landscape is expected to increasingly transition towards renewables. This growing shift opens up a massive potential for AI vendors in AI in energy market to develop and supply solutions that help maximize the value delivered from renewable resources.

Partnerships and collaborations: In 2018, IBM partnered with an ocean energy company Minesto to develop an AI assistant to monitor and optimize their tidal energy conversions systems.

Acquisitions: In 2020, Shell acquired Silicon Valley-based AI startup Savannah Simulation to accelerate their use of AI/ML for optimization of LNG facilities. The acquisition strengthened Shell's digital capabilities for asset performance management.

Development of customized AI solutions: Microsoft has developed solutions like the Distributed Energy Resource Management System (DERMS) to help utilities better integrate renewables using machine learning.

Partnerships with research organizations: Successful companies in the AI in energy market partner with national research labs and academia to tap into new research. For instance, in 2021, the US Department of Energy launched three new Energy Labs of the Future with ExxonMobil, NREL, LANL, focusing on AI for optimized operations and grid stability.

Insights, By Technology: Machine Learning Leads Due to its Adaptability

In terms of technology, machine learning contributes 35.6% share of the AI in energy market in 2024. This is due to its ability to continuously learn and improve from data without being explicitly programmed.

All machine learning models require large amounts of data to learn from. The energy sector has abundant data from various sources like smart meters, weather stations, sensors etc. which makes machine learning highly applicable for tasks like predicting future energy demand and consumption based on historical data, classifying equipment health, optimizing power grid reliability etc.

Its self-learning ability allows machine learning models to continuously improve over time as they are exposed to new data. This advantage has led to machine learning dominating other techniques in capturing the highest share in AI in energy market.

Insights, By Application: Safety & Security Drives the Application of AI in Energy Market

In terms of application, safety & security contributes 27.5% share of AI in energy market owing to the critical needs for risk mitigation and protection of critical energy infrastructure and workforce. AI-based safety and security applications are invaluable in monitoring energy facilities, detecting anomalies, predicting failures and ensuring personnel safety.

With growing cybersecurity threats to energy infrastructure, AI is playing a vital role in strengthening cyber defenses through activities like anomaly-based intrusion detection, malware analysis, and predictive threat modeling. Its ability to analyze huge volumes of data at scale makes AI particularly suitable for this application compared to traditional solutions.

The criticality of safety and security applications has led this segment to account for the largest share of the AI in energy market.

Insights, By Deployment Mode: Cloud Adoption Spreads AI in Energy Market

In terms of deployment mode, cloud-based deployment contributes the highest share of the AI in energy market owing to advantages like scalability, accessibility and reduced maintenance costs compared to on-premises solutions. Deploying AI applications on the cloud removes the need for energy companies to build and maintain their own on-premises infrastructure. This provides significant savings while freeing up internal resources.

Cloud platforms also simplify data scientists' work by offering serverless computing and auto-scaling capabilities to handle large and diverse workloads efficiently. Features likePay-as-you-go billing, globally distributed data centers, and streamlined upgrades further support fast iteration and experimentation needed for AI/ML model development.

Cloud architecture overcomes challenges of data storage, processing and model training faced by many energy firms due to restricted local computing power and data centers. Major cloud providers like AWS, GCP, and Azure have made considerable investments in AI-specific services, tooling, and frameworks which attract developers and foster innovation. These benefits have spurred energy companies to predominantly adopt cloud-based AI systems.

• AI Deployment Rate by Country: High adoption of AI in energy market in China, India, and Singapore, with over 50% deployment rate in AI in energy applications.
• Projected Growth of Asia Pacific: AI in energy market in the region is expected CAGR of 17.4% from 2024 to 2034, driven by energy efficiency initiatives.
• SLB's Lumi Data Platform: A cutting-edge AI platform aimed at improving decision-making and data management across energy applications.
• Vultr’s Cloud Solutions: Tailored solutions for AI in energy regulation compliance, enhancing flexibility, and scalability.

The major players operating in the AI in energy market include IBM, Siemens AG, Schneider Electric, General Electric (GE), Microsoft Corporation, ABB Group, AppOrchid Inc, Alpiq AG, ATOS SE, Zen Robotics Ltd, SmartCloud Inc., and Hazama Ando Corporation.

• In September 2024, SLB (formerly Schlumberger) introduced the Lumi™ data and AI platform at its Digital Forum in Monaco. This platform is designed to enhance data management across the energy sector by integrating advanced artificial intelligence capabilities, including generative AI, into workflows spanning the entire energy value chain.
• In July 2024, Spain initiated a €2.3 billion funding package to expedite its transition to clean energy, with a significant focus on green industries and renewable hydrogen. This initiative, managed by the Institute for Energy Diversification and Savings (IDAE) and funded through European grants from the Recovery, Transformation, and Resilience Plan (PRTR), underscores Spain's commitment to its ecological transition.
• In August 2024, Vultr introduced its industry cloud solution, offering specialized cloud computing services tailored to the regulatory and operational needs of various sectors, including retail, manufacturing, healthcare, media, telecommunications, and finance.
• In March 2024, IBM launched a new AI-driven energy management platform aimed at optimizing power distribution, enhancing efficiency by 20%.

• By Technology
  • Machine Learning
  • Neural Networks
  • Natural Language Processing (NLP)
  • Computer Vision
• By Application
  • Safety & Security
  • Demand Forecasting
  • Renewables Management
  • Infrastructure
  • Robotics
• By Deployment Mode
  • Cloud-Based
  • On-Premises
• By End-use Industry
  • Power Generation
  • Oil & Gas
  • Utilities
  • Renewable Energy