Artificial Intelligence in Energy: What You Should Know

The application of artificial intelligence (AI) in energy is revolutionizing the sector, bringing transformative changes. According to an article by BCG, “Companies that fully embrace AI could see profit improvements of 30% to 70% of EBIT over the next five years.”

This guide explores how AI for energy is reshaping operations—from predictive maintenance and grid optimization to trading intelligence and sustainability. You’ll discover the types of AI, the energy data analytics needed for implementation, key challenges and real-world examples of energy companies already seeing results. Whether you’re just starting your AI for energy journey or scaling enterprise-wide initiatives, this guide offers a roadmap to unlock efficiency, innovation and competitive advantage.

What is AI?

AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think, learn and make decisions. At its core, AI enables computers and systems to perform tasks that typically require human intelligence, such as:

Understanding language: A great example of AI that understands language in the energy sector is Enverus AI. This AI agent is designed specifically for energy workflows and uses natural language understanding to assist professionals across land, trading, finance, operations and planning.
Recognizing patterns (like in images or data): An example of this in the energy space is recognizing pressure patterns during hydraulic fracturing and providing real-time recommendations to optimize the fracking process.
Solving problems and making decisions: An example of this is using AI for predictive and preventative maintenance. With generative AI analyzing real-time sensor data, it can identify optimal patterns and adjust operations without human intervention.
Learning from experience using algorithms and data: A powerful use case in AI for renewable energy is addressing challenges with its primary sources of wind and solar. AI significantly improves the accuracy of energy production forecasts by analyzing real-time meteorological data, satellite imagery, and historical patterns.

Types of AI

Narrow AI: Designed for a specific task (e.g., voice assistants, recommendation systems).
AGI: A theoretical form of AI that could perform any intellectual task a human can
Generative AI: A subset of AI that creates new content—like text, images, music or code—based on patterns it has
Agentic AI: AI systems that exhibit autonomous, goal-driven. These systems are designed to act independently, make decisions and complete complex tasks with minimal human supervision.

Unlike traditional AI, which often requires explicit instructions or operates within fixed rules, agentic AI systems:

Plan, reason and act in dynamic environments
Use large language models (LLMs) to interpret context
Interact with external tools (APIs, databases, sensors)
Learn and adapt over time through feedback and reinforcement learning

How is agentic AI different from generative AI?

Generative AI creates content—text, images, code—based on patterns it has learned. Agentic AI goes further by:

Using that content to take action
Managing multi-step workflows

Coordinating with other agents to achieve broader goals For example:

A generative AI might suggest the best time to make a
An agentic AI for renewable energy could coordinate distributed energy resources by balancing supply and demand, shift loads and forecast renewable output, ultimately enabling grid stability and efficiency in energy delivery.

What types of data are needed for AI implementation?

To implement AI effectively, the types of data you need depend on the specific use case, but generally fall into the following categories:

Structured data
This is organized and easily searchable, often found in databases or spreadsheets.

- Customer data: demographics, purchase history, preferences

- Sales and financial data: revenue, margins, forecasts

- Operational data: inventory levels, logistics, staffing

Use case: predictive analytics, segmentation, churn modeling

Unstructured data
This is more complex and includes formats like text, images, audio and video.

- Text: emails, reviews, social media posts, support tickets

- Images and video: product photos, surveillance footage, marketing assets

- Audio: call center recordings, voice commands

Use case: sentiment analysis, image recognition, natural language processing

Behavioral data
Tracks user interactions and engagement.

- Web/app usage: clicks, scrolls, time spent

- Purchase paths: cart additions, drop-offs, conversions

- Device data: location, OS, browser type

Use case: personalization, recommendation engines, UX optimization

External data
Sourced from outside the organization to enrich insights.

- Market data: competitor pricing, industry trends

- Social data: public sentiment, influencer impact

- Environmental data: weather, traffic, geopolitical events

Use case: market forecasting, risk modeling, strategic planning

Labeled vs. unlabeled data

- Labeled data: annotated for supervised learning (e.g., spam vs. not spam)

- Unlabeled data: used in unsupervised or semi-supervised learning

Use case: training models for classification, clustering, anomaly detection

Challenges of AI for energy

Data quality and availability:
Ensuring high-quality, accurate data is critical for effective AI implementation in energy data analytics.
AI cybersecurity risks
Interconnected systems create potential vulnerabilities for cyber threats, requiring robust governance.
Lack of AI expertise
The skill gap in understanding AI’s potential and its application in energy remains a significant challenge.
Legacy infrastructure
Older systems may not support real-time data processing or AI integration, necessitating costly upgrades.
Regulatory and ethical concerns
Ensuring AI’s transparency and fairness in decision-making is crucial in the heavily regulated energy sector.
High implementation costs
Initial investments in AI platforms, infrastructure, and training can be substantial.
Change management and adoption
Cultural shifts are necessary to embrace AI-driven decisions and innovations.
Model interpretability and trust
Transparency in AI models is essential for regulatory compliance and gaining operator trust.

Thomas Greene, Marketing Manager at Tenaris, notes that while AI can deliver creative and novel solutions, gaining full trust and ensuring accuracy requires time and consistent use. He emphasizes the importance of framing prompts correctly to extract valuable and reliable information from AI tools.

Benefits of AI for energy?

AI holds tremendous potential for the energy industry and new use cases are being discovered every day. Adoption by energy companies is moving at a slower pace than other industries, in part due to its heavily regulated environment, but the interest is definitely there.

Benefits of energy companies using AI in day-to-day operations are similar to other industries.

Improved operating efficiency and cost reduction
Capital efficiency is top of mind for energy producers and has been for a few years now. Oil and gas operators are looking for any edge to protect base production and optimize remaining inventory at a profit. AI can assist with predictive maintenance, automate manual tasks, optimize processes, reduce carbon emissions and enhance other areas, improving the bottom line.
Deployed effectively, S&P Global documents operational performance improvements in the 10%-25% range.
AI can also be applied to power grids to balance supply and demand, forecast solar and wind energy production, optimize energy storage and load shifting to ensure consistent power delivery. AI enables virtual power plants that aggregate distributed energy resources.

Smarter, faster decision making
Energy is a dynamic business. Market conditions and geopolitical tensions can shift overnight, hydrocarbons are a finite resource, energy demand is rising at a rapid pace, and an aging North American grid infrastructure needs to be updated. To remain competitive in this complex environment requires decision-making based on accurate data analysis. But many employees face challenges with data quality, sheer value of data and access across multiple different systems. AI can provide real-time insights by combing massive data sets, providing the information necessary to make informed decisions in minutes, versus weeks or even months. But the quality of the analysis and answers depends on the quality, specificity and connectedness of the data.

Workforce augmentation
AI needs humans to direct it to the desired result. Think of AI as a multiplier of one employee’s knowledge and skills and you get digital amplification of your existing workforce. AI augments human decision-making, freeing up teams for higher-value work.

Improved customer experiences
An important part of an oil and gas operator is the lease agreements with private landowners and arrangements with joint interest partners. These business relationships require communication, royalty payments and JIB invoices, and often involve questions from the landowners. Virtual assistants and chatbots can improve the support necessary for these stakeholders without requiring additional resources from the operator.

Key trends for AI for energy: What are other energy companies doing with AI?

AI is reshaping the energy industry in profound ways, and driven by the need for efficiency, sustainability and resilience. According to Enverus Intelligence® Research, use cases include using AI agents to evaluate well data for workover decisions, safety applications, reliability improvements, renewables integration, data mining and optimizing trading and logistics.

Here’s how current AI trends are impacting the sector with some real-world examples:

1. Grid modernization and smart infrastructure

AI is helping utilities upgrade aging infrastructure to handle modern energy demands.

Predictive maintenance: Digital twins simulate real-time behavior of assets like substations and wind farms, predicting wear and optimizing performance. Companies are embedding AI into control systems to turn raw telemetry into smarter decisions
Outage detection and load balancing: Edge computing enables real-time analysis of sensor data at the grid’s edge, improving outage detection and load. Renewable energy forecasting helps balance intermittent sources like solar and wind with grid needs. Smart meters and home energy hubs use AI to balance demand and reduce grid strain.

2. Operational efficiency and innovation

AI is driving performance improvements across the board, being used in everything from refinery design to battery optimization and energy market hedging. S&P Global reports 10–25% gains in operational efficiency from AI adoption.

Production optimization: One North American onshore independent E&P company is differentially investing in generative AI relative to other oil and gas companies. The company aims to save $250 million through production optimization—built largely on scaling machine learning models. The company has invested in standardizing sensors across its thousands of wells and is working to use that data in real time with physics models and algorithms to determine optimal flowing conditions for each well.

Watch this video to see how Enverus AI makes benchmarking your peers’ drilling performance quick and easy.

Improve safety in the field: Amy Studdert, Eagle Ford Subsurface Lead at Murphy Oil, cites an example of enhancing on-site safety and efficiency by using robotics and AI to automate mud logging operations.
Land title research: Jason Lyons, GCR Land & Regulatory Manager at ConocoPhillips, shared that using AI automation can shift the focus of land professionals from transactional tasks to broader strategy. By significantly reducing the time spent on document validation and research, AI enables land professionals to concentrate on higher-value activities, such as identifying risks and opportunities tied to leases and well production. This proactive approach contrasts with traditional reactive methods, allowing for more strategic business impact and faster decision-making.

Watch the video to see how Enverus AI uncovers lease details effortlessly.

Faster, more effective asset screening and decision making: Brendan Whipple, Associate Director at Scotiabank, says leveraging Enverus AI to streamline the company’s inventory analysis workflow enables faster and more accurate views of inventory. The ability to refresh data sources and update technical assumptions quickly allows for more effective asset screening and decision- making. This aids in assessing potential buyers for assets, considering factors like break-evens, material inventory additions and operational synergies.

Watch the video to see how Enverus AI makes deal screening quick and painless.

Watch the video to see how Enverus AI pinpoints prime locations for renewables, fast.

Improving production outcomes: Andrew McMurray, CEO of ShearFRAC®, cited insights into the application of neural networks and AI in hydraulic fracturing operations. AI is used for pressure pattern recognition and providing intelligent recommendations during the fracturing. McMurray emphasized the importance of ROI in AI applications, urging a focus beyond cost and time savings. By reacting to real-time data and making informed decisions, AI has helped improve production outcomes, particularly in managing the performance of new wells adjacent to older ones.
Anil Godumagadda, Vice President, Digital Solutions at Patterson-UTI, also shared the substantial wins achieved through AI, particularly in real-time systems for drilling operations. His focus was on equipment performance, process improvement and emissions reduction, where machine learning models have demonstrated significant. For instance, AI models adapted from speech recognition technologies have been employed to enhance equipment reliability and optimize processes, while emissions reduction has been achieved through predictive models for engine usage.
Quickly find power purchase agreement details: Ben Rosen, Power Markets Team Lead at Enverus, explains how generative AI is employed to extract power purchase agreement details from dense documents and web pages, significantly reducing the need for labor-intensive manual This automation allows analysts to focus on more valuable tasks and integrates AI seamlessly into their workflows, presenting results in a structured format that saves time and enhances accuracy.

Watch the video to see how prep time for earnings calls gets reduced to minutes with Enverus AI.

3. Forecasting and demand management

AI is revolutionizing how energy companies predict and respond to demand:

Supply and price optimization: Energy demand forecasting uses weather, user behavior and market data to optimize supply and pricing.
Navigate market volatility and smarter trading decisions: Energy traders cite management and pattern recognition as critical areas where AI can deliver real value. AI’s ability to aggregate quantitative and qualitative data, such as geopolitical risks and market sentiment, can significantly enhance trading decisions. There is also the potential of AI to perform time series forecasting and anomaly detection, which can help traders navigate volatile markets more effectively.
Reduce unnecessary repairs and customer outages: AES used AI to reduce unnecessary repairs and customer outages. The company saw a 10% reduction in customer power outages, saving $1M annually.

4. Predictive maintenance

AI enables proactive maintenance, reducing downtime and costs. AI models analyze drone, satellite and sensor data to detect equipment issues before failure. Utilities use AI-powered drone inspections to enhance grid reliability and reduce emissions.

Autonomous control systems: One area in energy where AI is predicted to be disruptive in the near future is the autonomy of control systems. AWS is working with two companies to automate entire power plants, which the companies believe will drive down costs and increase electricity generation.
Another example cited by AWS is a sand plant south of San Antonio Texas that integrated GenAI to run its control system, resulting in a 6% reduction in the use of gas to heat the sand and an 8% increase in sand production.

5. Sustainability and decarbonization

AI supports clean energy goals and environmental impact reduction by optimizing the integration of renewables, managing carbon emissions and improving energy efficiency:

Support for multi-energy strategy: TotalEnergies is collaborating with French artificial intelligence firm Mistral AI to support its multi-energy strategy, especially in low-carbon energies. The first envisioned use cases will be designing an assistant for Total’s 1,000 researchers to support their mission to develop new energies and reduce the company’s environmental footprint; develop decision-support solutions to enhance performance of its industrial assets and lower CO2 emissions; and implement support solutions aimed at improving the customer experience and helping them save money. The joint lab will also test digital solutions for other use cases, especially renewable energy production.

6. Customer experience and personalization

AI can enhance customer engagement and service:

Customer support automation: AI can predict consumption patterns and suggest cost-saving Octopus Energy, an energy provider, sought to improve its customer service through enhanced email response quality. They implemented generative AI to automate responses to customer emails, achieving an 80% customer satisfaction rate, surpassing the 65% rate of human agents.

7. Data governance and cybersecurity

As IT and OT systems converge, AI helps secure energy networks:

AI monitors for anomalies, encrypts telemetry and supports unified data governance
Unified data models improve decision-making and regulatory compliance

AI best practices for energy companies

If there is one thing not to do, it’s to sit by and watch the adoption of AI without participating. If you feel like your company is observing this AI wave, you’re falling behind.

Move beyond pilots to scalable execution
Many energy companies are stuck in pilot phases. Best-in-class firms are shifting toward scaling AI across the enterprise, aligning projects with strategic goals and ensuring cross-functional collaboration.

Focus on high-impact, feasible use cases
Prioritize AI initiatives that offer clear ROI and are technically feasible. Examples include predictive maintenance, energy demand forecasting and smart grid optimization.

Combine top-down and bottom-up approaches
Leadership must champion AI adoption while empowering teams to experiment and innovate. This dual approach fosters organizational buy-in and agility. Companies need to take a strategic approach in messaging to encourage internal adoption, focusing on specific benefits that AI can bring to individual roles and tasks.

- Top-down approach: Amy Studdert from Murphy Oil Corporation highlighted the top-down approach in her company, where leadership’s strong focus on AI facilitated the adoption of these tools for administrative tasks, reducing time expenditure from hours to minutes.
- Bottom-up approach: Brandon Finks, VP of Strategy at DG Petrol Oil & Gas, says a bottom-up approach, where initial AI usage for weekly notes gradually expanded to more complex tasks at his company, showcasing the versatility and efficiency of AI-driven solutions.

Invest in AI-ready infrastructure
- Data quality is foundational. If companies want to connect generative AI tools at scale, they need to focus on their data. AI demands robust data pipelines, cloud infrastructure and edge computing.
- Companies need to modernize their IT stacks to support real-time analytics and automation. Companies also need to work on developing AI skills of employees, focus on end user needs and consider mobile-centric solutions for increased adoption.

Conduct continuous ongoing employee training
- Preparing the workforce for future technological advancements in AI requires continuous training and development to ensure that new employees understand both the business and the technical aspects of AI tools. Companies must also keep in mind that while AI can serve as a powerful assistant, human expertise remains essential for verifying and optimizing AI outputs.

- Deep Joshi, Senior Data Scientist at Quantum Capital Group, emphasizes the importance of user feedback and iterative improvement. When the company launched its flagship AI product, extensive user training and adjusting the models based on feedback to ensure they provided actionable and precise predictions was critical to deployment success.

Consider the needs of your company when considering developing in-house versus using third party tools
- This decision hinges on cost, development time and the need for transparency. Developing AI internally offers greater control and flexibility, but third-party solutions can speed up deployment. The key is finding a balance that provides a competitive edge without reinventing the wheel.

How to get started with AI for energy

Energy companies ready to start their AI journey should take these practical steps:

Lead with a strategy
Define a phased approach to becoming an AI-first organization. Start with operational and financial pain points identified by leadership where AI can deliver measurable value. Common use cases in energy include: The key is finding a balance that provides a competitive edge without reinventing the wheel.

- Predictive maintenance for equipment and infrastructure
- Demand forecasting and load balancing
- Energy trading optimization
- Grid management and fault detection
- Customer service automation (e.g., chatbots, billing inquiries)
- Renewable energy integration (e.g., solar/wind forecasting)

💡 Tip: Prioritize use cases based on ROI, feasibility and data availability.

Upgrade platforms and ensure seamless data flows across the business.
AI thrives on quality data. Conduct a data audit to:

- Identify relevant data sources (sensor data, SCADA systems, CRM, ERP, etc.)
- Ensure data is clean, structured and accessible
- Address gaps in data collection or storage

💡 Tip: Consider building a centralized data platform or data lake to support AI initiatives.

Establish governance and ethics
AI in energy can impact safety, privacy and fairness. Set up:

- Clear governance frameworks
- Ethical guidelines for AI use
- Risk management protocols

Build or upskill your teams
You’ll need a mix of domain experts, data scientists and AI engineers. Options include:

- Hiring AI talent
- Upskilling existing staff through training programs
- Partnering with AI vendors or consultants

Choose the right technology stack
- Select tools and platforms that support your goals:
- Cloud platforms (Azure, AWS, GCP) for scalability
- AI/ML frameworks (TensorFlow, PyTorch, etc.)
- MLOps tools for model deployment and monitoring

💡 Tip: Ensure your tech stack supports security, compliance and interoperability with existing systems.

Start small, show impact, then scale
Begin with a pilot project to test and validate your approach. Use it to:

- Demonstrate value
- Refine your methodology
- Build internal buy-in

Once successful, scale to other use cases and business units. Reinvest early in scaling successful AI projects and funding foundational enablers.

Monitor, measure and iterate
Track performance using KPIs like:

- Cost savings
- Uptime improvements
- Forecast accuracy
- Customer satisfaction

Use feedback to continuously improve models and processes.

Enverus AI: Built for Energy. Intelligence You Can Trust.

Because in energy, generic AI isn’t good enough.

Did you know Enverus is the AI-native platform for global energy? As the most trusted energy dedicated SaaS company, we unify proprietary data, analytics, software and research into a single decision system— purpose-built for energy professionals.

With Enverus AI you can collaborate with trusted intelligence in seconds — powered by 25+ years of proprietary energy data, analytics and research. Enverus AI is vertical generative AI built for energy, by energy professionals, trained on the most complete intelligence in the industry and designed to work alongside your teams in the workflows they use every day

Choosing the right solution for your business is critical. Here are a few reasons to seriously consider Enverus AI:

An Agent for Every Use Case. One Mission: Create Shareholder Value

Think of Enverus AI agents as digital teammates, deeply trained to execute across land, trading, finance, operations and planning. In an industry that moves fast, you’ll find insights ahead of the curve.

AI That Knows Energy—Because It Was Built on It

Our AI is engineered for energy. Trained on decades of proprietary data—7M wells, 50,000+ renewable assets, 1,300+ monitored generators, 43,000+ power constraints, $270B in annual invoice spend, $1 trillion in M&A data, 350 million land records and 3 decades of expert insight. AI workflows designed by industry professionals. Generic AI can’t compete.

Efficiency at Scale—Minus the Complexity

Whether your focus is oil and gas or renewable technology, our expert coverage of projects, acquisitions and evolving regulations ensures your future energy strategy is consistently well informed.

Privacy, Security, Data Integrity

Designed with privacy at its core, Enverus AI ensures secure data handling, encrypted transmission and guarantees no training on your personal or proprietary data—protecting your privacy and confidentiality at every step.

Stay Competitive. Stay Relevant. Stay Ahead of the Curve.

You’re competing against other organizations using AI. Lead with Enverus AI.

Related Blogs

Fast and Permitted | Washington Clears the Way for AI

Posted by Adam Robinson, Enverus Intelligence® | Research (EIR) ContributorJuly 31, 2025

The White House recently released its AI Action Plan, a broad policy roadmap designed to cement American leadership in artificial…

How AI Is Transforming Trading Strategy, Faster Insights, Smarter Moves

Posted by Kiana CruzJuly 15, 2025

In today’s fast-paced markets; traders don’t just need data, they need decisions. At EVOLVE 2025, the Trading & Risk session,…

Everything’s Bigger in Tech-SaaS | Fermi Plans 11 GW AI Campus With TTU

Posted by Adam Robinson, Enverus Intelligence® | Research (EIR) ContributorJuly 10, 2025

The Texas Tech University (TTU) System and Fermi America announced an 11 GW data center powered by a mix of…

How GenAI Will Change Your Oil and Gas Acreage Strategy

Posted by EnverusJune 6, 2025

The following blog is distilled from Enverus Intelligence® Research (EIR) publications. As the upstream oil and gas sector faces mounting pressure…

Enverus EVOLVE: Creating the future of energy with AI, intelligence and innovation

Posted by EnverusMay 28, 2025

Enverus, the most trusted energy-dedicated SaaS company that leverages generative AI across its solutions, successfully concluded its annual EVOLVE Conference,…

Enverus Instant Analyst™ - Courthouse: The First AI-Powered Solution for Title Research

Posted by Temi OyetayoMay 12, 2025

The energy industry thrives on data-driven decisions. For more than 25 years, Enverus has delivered transformative solutions that streamline workflows…

Energy's Hidden Data Gold Mine: How AI Finally Cracks the Code

Posted by Akash SharmaApril 17, 2025

The energy sector has always been complex. From the volatility of oil and gas markets to the technical challenges of…

A Guide to Leveraging AI to Navigate the Oil and Gas Market for Small and Mid-Sized Operators

Posted by EnverusApril 17, 2025

The need to identify and secure quality acreage is more critical than ever for operators, from traditional family businesses to…

Analyst Takes Generative AI

From data to decisions: The role of AI in enhancing oil and gas operations

ByEnverus

April 2, 2024

The energy sector is definitely drawn to the potential of artificial intelligence (AI). The promise of making things work better and more efficiently is too tempting to ignore.

energy-transition-group-of-professionals-meeting

Energy Transition Generative AI

Data Center Demand | Framing the Frenzy

ByCarson Kearl, Enverus Intelligence® Research (EIR) Contributor

March 28, 2024

One of the most common and important questions for the power sector today is what impact artificial intelligence and the data centers needed to generate it will have on energy consumption.

Generative AI

What happens when GenAI makes everyone a power user?

ByJimmy Fortuna

March 18, 2024

That’s right – the Instant Analyst technology that we announced just two weeks ago is coming to PRISM. The Intelligence Vault already has Instant Analyst features enabled for a group of trial customers, helping them get faster answers to complex questions...

Generative AI News Release

Enverus Instant Analyst™ technology added to its AI-powered product portfolio