Energy companies are increasingly adopting analytical artificial intelligence technologies.
Key takeaways
Company reports and earnings calls shed light on trends and areas of implementation.
Companies need to understand their existing capabilities and manage challenges that accompany AI.
A look at how middle market energy companies are adopting AI
As the energy sector evolves, it faces new pressures and opportunities, especially from the ongoing energy transition and the digitalization of industrial processes. Companies face tighter profit margins and increased scrutiny on emissions, safety and cyberattacks as environmental, social and governance issues continue to gain attention. To remain competitive and resilient, energy companies are increasing their adoption of analytical artificial intelligence (AI) technologies, including machine learning, in areas ranging from the back office and cybersecurity to field operations and process optimization.
What was considered science fiction not long ago now has proven, practical use cases that larger, leading-edge energy companies have tested and refined. Middle market energy companies can benefit from the lessons learned and investments made by these early AI adopters to chart their own adoption journey.
AI use case trends
If data is the new oil, energy companies are well positioned to leverage AI technologies because they have amassed extensive data on operations over many years.
With these use cases, it is important to recognize that AI solutions are no longer new in the energy sector and should be considered a strategic priority moving forward.
David Carter, Industrials Senior Analyst, RSM US LLP
Energy companies are increasingly evaluating and adopting AI solutions to leverage their data, a trend publicly traded companies have been highlighting to investors in company presentations and earnings calls. A 2023 report from Capgemini found that 64% of energy/utility companies “have started exploring the potential of generative AI” and 33% “have begun working on some pilots of generative AI initiatives.”
TAX TREND: Artificial intelligence
For energy companies adopting or advancing their AI and machine learning capabilities, integrating new systems with modern tax applications can help effectively manage complex tax and financial data. Involve the tax function at the outset of any project to promote an effective integration.
Learn more about RSM’s tax technology consulting services.
We reviewed company presentations, corporate responsibility reports, and earnings calls for publicly traded middle market and some upmarket companies to identify trends in how they are adopting AI solutions. While specific use cases for AI vary widely across energy subsectors, they generally revolve around a few key categories, highlighted below:
Efficiency and optimization of industrial operations
Energy companies are deploying AI and machine learning to optimize production processes, from drilling wells to operating electric turbines. Know-how that once took decades of operator experience to develop can now be modeled in days. Furthermore, predictive maintenance helps identify equipment failures before they occur, and real-time data analysis helps with power grid energy management, leading to substantial cost savings and increased uptime.
Environmental monitoring and emissions reduction
With the growing focus on monitoring and reducing greenhouse gas emissions, energy companies are using cameras and sensors paired with AI to monitor flaring, gas leaks and fires in the field. More efficient turbine operation can also lead to decreased emissions, as can AI-optimized building heating and cooling.
Business process improvement
Companies are integrating AI into various business processes, such as geoscience data interpretation, supply chain optimization, risk prediction and management, and automation of administrative functions. AI is driving their shift toward more data-driven decision making and business strategies.
Safety and security enhancements
Businesses in the energy space are using AI to improve the safety of field operations by monitoring and alerting operators and/or stakeholders on dangerous conditions, as well as simplifying the tracking and reporting of safety issues. On the digital front, companies are adopting cybersecurity tools that leverage AI to detect and block anomalous activity from potential attacks (where attackers themselves are increasingly using AI), monitor cyber compliance and more.
Talent and skill development
Energy companies are investing in AI-focused training programs to upskill their workforce, including by offering training on AI itself. They are also incorporating AI throughout the hiring process and employee life cycle to reduce bias and enhance the applicant and employee experience.
Improved customer experience
AI-powered customer service solutions can improve response times and provide more personalized experiences, leading to higher customer satisfaction scores. AI-driven predictive maintenance makes operations more reliable, reducing outages that affect customers and ultimately enhancing the customer experience.
TAX TREND: Talent and skill development
Energy companies either hiring new employees versed in AI applications, or training their workforce to work with AI might be eligible for state and local credits or incentives that could help offset the costs of those workforce investments.
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With these use cases, it is important to recognize that AI solutions are no longer new in the energy sector and should be considered a strategic priority moving forward. Though generative AI gained prominence only recently, with tools like ChatGPT skyrocketing in popularity, numerous companies are already exploring its use through pilots and full-scale adoption.
Special report
Middle market is confident about AI, despite early-stage adoption changes
- 78% of respondents say their organization either formally or informally uses AI
- 41% report being in the partial implementation phase for AI
- 58% of those who use generative AI want to use it to improve quality control
The path forward
Energy companies, especially those seeking to better harness their data for AI adoption, should consider the following key elements as they look forward:
Data collection and governance
Data, much like oil, needs to be collected and refined to be useful. Companies must identify the data they are collecting and centralize its storage to enable future mining. Proper data governance, literacy and vision will also be essential for supporting adoption.
Understand existing capabilities
Leverage the experiences of peers and larger companies to identify proven use cases. Leaders and employees need to be inspired by seeing what is possible, and companies need to plan around the roadblocks encountered by others to streamline their own adoption.
Manage new risks and challenges
As with many technology adoption projects, the technology itself is just a small part of what companies must consider. AI, and especially generative AI, introduces new concerns around ethics (e.g., bias/discrimination), job replacement, and even legality that companies must evaluate and manage through.
The integration of AI into the energy sector will transform and disrupt existing ways of doing business. These solutions will pave the way for optimizing exploration and plant operations, streamlining business processes, reducing environmental impact and improving customer experiences. Despite the challenges, middle market energy companies must accelerate their AI adoption efforts to remain competitive in today's digital age—and leveraging valuable lessons from earlier adopters is key.
CONSULTING INSIGHT: Generative AI
Generative AI is revolutionizing the development and delivery of products and services, and many organizations are working to understand how to effectively utilize the technology. Gain insights into how you can capitalize on the generative AI trend by increasing the value of key processes while mitigating risks.
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