How AI is Powering the Energy Sector
How AI is Powering the Energy Sector
CATEGORY
Infographic, Energy & Resources, Artificial Intelligence, Generative AI
David Aldrich
Principal, Energy Consulting
Yvette Clark
Senior Manager, North America Energy Business Unit
At the end of 2024, EPAM conducted a comprehensive survey with more than 7,300 professionals to learn how executives, engineers and developers viewed the present and future of AI.
Our work involved various industries and roles, including over 940 energy industry professionals across nine countries and four continents.
Key Findings from Our AI Research
GenAI is Becoming a Strategic Enabler
65%
of energy companies
deployed GenAI tools
55%
of energy companies deployed
traditional AI applications
69%
Non-tech C-suite say they
deployed GenAI tools
64%
Tech C-suite say they
deployed GenAI tools
A key finding from the survey is the quick adoption of generative AI (GenAI) in the energy sector, with 65% of energy companies already using GenAI tools.
GenAI is particularly suited for enhancing unstructured workflows in the energy industry, such as:
- Exploration modeling (querying technical document libraries)
- Operational performance insights (prescriptive recommendations)
- Customer engagement (support for field engineers)
Another interesting insight from the survey is that non-technical C-suite executives are emerging as champions of GenAI. This indicates that leaders see GenAI not just as a technology solution but as a strategic driver for decision-making, operational scaling and competitive differentiation.
FOR COMPANIES IN THE ENERGY INDUSTRY TO BECOME STRATEGIC ENABLERS, LEVERAGING THESE GENAI CAPABILITIES CAN DRIVE THEIR VISION:
- Conversational AI: Deploy GenAI-powered assistants to deliver real-time operational guidelines to field technicians, customer support agents and energy traders.
- Knowledge Management: Use large language models (LLMs) to organize unstructured data, summarize engineering reports and automate the creation of compliance documentation.
- Synthetic Data Creation: Generate usable datasets for exploration modeling, regulatory compliance tests and operator training where limited historical data exists.
Operational Efficiency as a Top Priority
% of respondents who said these are top goals their organization defined for AI adoption and depolyment
% of respondents who said these are the results their organization has already seen from AI projects
Energy companies identified operational efficiency (32%) as the main goal of AI adoption, followed by productivity improvements (31%) and profit margin improvement (26%). These findings align with the areas where AI has delivered results thus far:
- 43% of respondents reported improved operational efficiency
- 41% cited productivity gains
- Cost savings remain tangible but secondary at 36%
These priorities reflect an industry struggling with costly legacy systems, volatile commodity markets and pressure to streamline processes while achieving sustainability goals. It is important to note that the uneven realization of results — such as lower customer satisfaction improvements (35%) — possibly signaling a missed opportunity in deploying AI to enhance client-facing outcomes.
TO ACHIEVE OPERATIONAL EFFICIENCIES, LEVERAGING THESE AI CAPABILITIES CAN MOVE THE NEEDLE FOR ENERGY COMPANIES:
- Prescriptive Maintenance: Leveraging advanced analytics and IoT-enabled sensors to predict equipment failures across upstream, midstream and refining operations, can reduce downtime and optimize maintenance schedules.
- Process Automation: By automating manual workflows across exploration, asset management and supply chains, businesses can streamline operations and achieve cost savings.
- Energy Transition Models: Using AI to simulate the impact of electrification and renewable integration, businesses can support strategic planning while aligning with decarbonization goals.
Workforce Enablement & Skills Gaps
98%
of energy companies plan to
hire AI specific roles in 2025
54%
of energy companies believe their workforce
lacks skills to effectively deploy GenAI
The workforce remains both a key enabler and a challenge for scaling AI initiatives. While 98% of energy companies plan to hire AI-specific roles by 2025, including AI ethics and governance, ML engineering and AI product management, 54% of respondents believe their workforce lacks the skills to effectively deploy GenAI. This points to a discrepancy between the need for AI-specific roles and current AI skill gaps.
On average, respondents predict that 40% of staff at energy companies will need retraining within the next 18 months to navigate AI-powered transformations. C-suite leaders — particularly those in technology — are keenly aware of these challenges, with 56% agreeing that skill gaps hinder effective adoption.
TO ACHIEVE MAXIMUM ROI, THESE ELEMENTS SHOULD BE CONSIDERED TO ADDRESS AI WORKFORCE ENABLEMENT AND SKILLS GAPS:
- AI Training Platforms: Tailored education programs designed to upskill technical teams (e.g., engineers and data scientists) and operational staff on core and GenAI technologies.
- AR/VR Learning Experiences: Modern technology enables employees to practice field instrumentation, safety procedures and infrastructure monitoring in interactive, risk-free virtual environments.
- Talent Partnerships: Organizations can identify, recruit and onboard AI experts through focused recruiting services and partner ecosystems.
Governance & Responsible AI
Top AI Challenges for Energy Companies
25%
Protecting sensitive data
36%
Security Vulnerabilities
72%
Ensuring compliance with emerging regulations
Governance has become a key focus, with companies planning to implement comprehensive AI governance models within the next 16 months to tackle ethics, security and transparency issues.
Data security risks, including breaches and misuse of LLMs, are especially relevant in the energy sector, where proprietary operational data and trade secrets are prevalent. Governance frameworks for responsible AI use are critical to alleviating these concerns and scaling AI solutions enterprise-wide.
TO ACHIEVE MAXIMUM BENEFITS FROM GOVERNANCE AND RESPONSIBLE AI, HERE ARE SOME ELEMENTS TO CONSIDER:
- Ethical AI Development: Establish frameworks for transparent, explainable AI tools aligned with regulatory and ethical standards.
- Security by Design: Deploy tools for real-time vulnerability detection and develop systems to protect sensitive enterprise data from LLM exfiltration risks.
- Compliance Automation: Enable continuous AI revision tracking, auditing and reporting to simplify alignment with regulations such as GDPR, the European AI Act and local standards.
Overcoming Fragmented Roadmaps
55%
Fragmented AI roadmaps as
a key obstacle to AI adoption
34%
Communication silos as a core challenge
to modernizing AI infrastructure
Organizations are struggling with fragmented AI roadmaps, cited by 55% of respondents as a key obstacle. Beginners face particular challenges, with twice as many citing roadmap fragmentation compared to disruptors (25 % vs 17%), while only 21% strongly agree their leadership has a clear strategy for aligning AI with business goals.
A lack of internal alignment between business and technical teams further compounds this issue: 34% of respondents note communication silos as a core challenge in modernizing AI infrastructure. Bridging these gaps will require not only better technology but also better processes and organizational strategy.
The Path Forward
Our research shows that energy companies plan to increase year-over-year spending on AI by 10% in 2025, which translates to an approximate $10-12 million increase in their budgets. The next phase of AI adoption at energy companies is about focusing on use cases with a broad impact and prioritizing them correctly, as we saw in our AI report. Energy companies are less likely than companies in other industries to have validated AI use cases on the market (13%), to have launched one or more proof-of-concepts (9%) or are currently leveraging AI technology at scale (4%).
Like other sectors and industries, energy companies globally are laying the groundwork for AI adoption and implementation and exploring ways to improve efficiency with AI. The companies that can find the right combination of AI technology and creative use-cases will be better positioned in an increasingly competitive landscape. How energy companies invest in these capabilities to achieve ROI remains to be seen.
GET IN TOUCH
Hi! We’d love to hear from you.
Want to talk to us about your business needs?
