{"id":79624,"topic":"ai","source":"rsmus.com","title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","url_hash":"cf0e6cd53635294d2a16d5dae1eec7d8a766a307","author":"","summary":"AI use cases in the energy industry: Insights from research and company filings rsmus.com","content":"An RSM analysis shows how middle market companies are using AI across energy subsectors.\nAn RSM analysis shows how middle market companies are using AI across energy subsectors.\nUtilities appear to be deploying AI at a higher rate than oil and gas companies.\nPredictive maintenance has emerged as the most common use case.\nImplementing artificial intelligence has become an imperative for companies across industries, including those in energy. Identifying the right use cases is a critical step in maximizing the value of this technology. This is where sector-specific use cases are especially important to understand.\nWhile some companies have successfully deployed AI and machine learning use cases—from the front and back offices to plant and field operations—challenges remain. This calls for leaders to ask key questions such as:\nThere are no easy answers to these questions, but an RSM analysis of how middle market energy companies are deploying AI offers valuable insights, including lessons learned and an understanding of how AI use cases vary across energy subsectors.\nFor this analysis, we reviewed company disclosures, including earnings calls, annual reports and investor presentations from over 300 publicly traded, North America-domiciled energy companies with annual revenue between $100 million and $20 billion from January 2021 through July 2026. We also reviewed other public sources of information for these companies, including vendor case studies, utility commission filings and engineering papers.\nThis analysis provides a data-backed view of how middle market energy companies are deploying AI and what others can learn from their experience.\nHere are some of the key takeaways:\nTake a closer look at the findings below.\nMany of the barriers slowing AI adoption in the energy sector stem from data—its governance, controls and trustworthiness—not the technology itself. As AI becomes more embedded in operations, tax data, reporting processes and governance frameworks need to keep pace with the broader transformation. Stronger data foundations can support more reliable reporting and better-informed business decisions.\nLearn more: AI is working in tax. Scaling it is the hard part.\nAcross the 307 companies, 45% have at least one AI or machine learning use case running in operation. Because the analysis excluded unverified announcements and use cases without specific evidence, actual adoption is likely higher. Our conversations and work with energy companies on their AI journey anecdotally point higher still.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies. Oil and gas may be at an inflection point, however, as mentions of AI in earnings calls by Russell 3000 energy companies tripled in the first half of 2026 compared to the same period in 2025.\nGrouped more broadly, power and utilities companies showed a 77% AI deployment rate, compared with 43% for renewables and 33% for oil and gas companies. Some of that gap may be because utilities more often operate under a regulated filing regime and provide more disclosures.\nThe next natural question is what specific use cases are most often named, and what value they are delivering.\nIn our analysis of AI use among energy companies, some of the common use cases companies identified were:\nOverlap between the oil and gas sector and the power and utilities sector varied by use case.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies.\nOther insights from our analysis include:\nMore AI deployments have documented business outcomes now than when we last analyzed industry use cases in 2023.\nThe examples below show how companies across energy subsectors are turning use cases into business value.\n| Oil field services | Agentic AI parts ordering for 800 field technicians | $3M annual return; 90,000+ hours saved | \n| Upstream | AI agents monitoring drilling in real time | 12% lower drilling cost, or about $1M per well | \n| Oil field services | AI demand forecasting and frac fleet scheduling | 30% fewer trucks across ~1M annual trips | \n| Midstream | Neural-network routing of a gas gathering network | Near-zero non-routine flaring, no new capital expenditure | \n| Downstream | Autonomous refinery drones with AI analytics | One mission found $15M+ potential savings | \n| Gas utility | AI disaggregation of monthly gas billing data | 283% of savings goal | \n| Electric utility | Machine learning to target high-risk vegetation | ~40% fewer vegetation-related outages | \n| Solar power | Machine learning solar tracker control | Up to 4% higher yield across 50+ GW | \n| Wind power | Neural-network wind blade defect detection | 61% faster detection; 95% accuracy | \nImportantly, these are company- and vendor-reported figures, so we interpret them not as a promise but as a directional signal of where AI is heading and what a well-executed deployment can achieve. Notably, we’re now seeing agentic AI showing up in use cases, and we only expect its presence to grow.\nWhen it comes to slow AI adoption, the obstacle is rarely the AI itself. In our client work, echoed by surveys from the Massachusetts Institute of Technology, the most common constraints relate to data availability and governance, lack of trust in the AI output, cybersecurity and privacy/regulatory concerns, lack of executive sponsorship, and difficulty managing organizational change.\nScaling AI use cases from pilot to implementation is also a key challenge. Among respondents to RSM’s Middle Market AI Survey 2026: U.S. and Canada, only 36% of respondents have AI fully embedded across core processes. Among those who conducted AI pilots in the prior two years, about half (51%) described the success of those pilots as moderate or limited—and among those respondents, data quality issues (53%) and integration challenges (47%) were the leading reasons.\nFurther, the survey showed that while technology-related items are the more common inhibitors to AI deployment, two-thirds of respondents noted AI governance was established before implementing AI pilots or production.\nPredictive maintenance, forecasting and operational optimization tools all rely on data that is accurate, connected and trusted across the organization. Establishing clear data ownership and governance frameworks can help energy companies move AI initiatives from pilot projects to broader business adoption while improving confidence in AI-generated insights.\nLearn more: AI consulting services\nEnergy companies looking to accelerate AI adoption should:\nOver the coming years, we expect the constraint on AI adoption among energy companies to be less about access to the technology and more about whether a company’s data is in a condition to use it. Those that make strategic investments now to organize and govern their data will be better positioned to capture value as adoption accelerates.","image_url":"https://rsmus.com/content/dam/rsm/insights/industries/financial-services/1social/treio_ind_social.jpg","lang":"en","published_at":"2026-09-10T17:03:45+00:00","fetched_at":"2026-09-10T17:15:04+00:00","status":"read","starred":0,"extract_state":"ok","summary_auto":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","cluster_id":null,"extract_retries":0,"extract_error":null,"contract_version":"news_item.v1","format_contract_version":"news_item_formats.v1","dedup_url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 7193 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":7193,"summary_length":177,"usable_text_length":7193,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":7193,"summary_length":177}},"news_item":{"id":79624,"canonical_url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","source_url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","source_name":"rsmus.com","author":null,"published_at":"2026-09-10T17:03:45+00:00","locale":"en","topic":"ai","tags":[],"rss_summary":"AI use cases in the energy industry: Insights from research and company filings rsmus.com","full_text":"An RSM analysis shows how middle market companies are using AI across energy subsectors.\nAn RSM analysis shows how middle market companies are using AI across energy subsectors.\nUtilities appear to be deploying AI at a higher rate than oil and gas companies.\nPredictive maintenance has emerged as the most common use case.\nImplementing artificial intelligence has become an imperative for companies across industries, including those in energy. Identifying the right use cases is a critical step in maximizing the value of this technology. This is where sector-specific use cases are especially important to understand.\nWhile some companies have successfully deployed AI and machine learning use cases—from the front and back offices to plant and field operations—challenges remain. This calls for leaders to ask key questions such as:\nThere are no easy answers to these questions, but an RSM analysis of how middle market energy companies are deploying AI offers valuable insights, including lessons learned and an understanding of how AI use cases vary across energy subsectors.\nFor this analysis, we reviewed company disclosures, including earnings calls, annual reports and investor presentations from over 300 publicly traded, North America-domiciled energy companies with annual revenue between $100 million and $20 billion from January 2021 through July 2026. We also reviewed other public sources of information for these companies, including vendor case studies, utility commission filings and engineering papers.\nThis analysis provides a data-backed view of how middle market energy companies are deploying AI and what others can learn from their experience.\nHere are some of the key takeaways:\nTake a closer look at the findings below.\nMany of the barriers slowing AI adoption in the energy sector stem from data—its governance, controls and trustworthiness—not the technology itself. As AI becomes more embedded in operations, tax data, reporting processes and governance frameworks need to keep pace with the broader transformation. Stronger data foundations can support more reliable reporting and better-informed business decisions.\nLearn more: AI is working in tax. Scaling it is the hard part.\nAcross the 307 companies, 45% have at least one AI or machine learning use case running in operation. Because the analysis excluded unverified announcements and use cases without specific evidence, actual adoption is likely higher. Our conversations and work with energy companies on their AI journey anecdotally point higher still.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies. Oil and gas may be at an inflection point, however, as mentions of AI in earnings calls by Russell 3000 energy companies tripled in the first half of 2026 compared to the same period in 2025.\nGrouped more broadly, power and utilities companies showed a 77% AI deployment rate, compared with 43% for renewables and 33% for oil and gas companies. Some of that gap may be because utilities more often operate under a regulated filing regime and provide more disclosures.\nThe next natural question is what specific use cases are most often named, and what value they are delivering.\nIn our analysis of AI use among energy companies, some of the common use cases companies identified were:\nOverlap between the oil and gas sector and the power and utilities sector varied by use case.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies.\nOther insights from our analysis include:\nMore AI deployments have documented business outcomes now than when we last analyzed industry use cases in 2023.\nThe examples below show how companies across energy subsectors are turning use cases into business value.\n| Oil field services | Agentic AI parts ordering for 800 field technicians | $3M annual return; 90,000+ hours saved | \n| Upstream | AI agents monitoring drilling in real time | 12% lower drilling cost, or about $1M per well | \n| Oil field services | AI demand forecasting and frac fleet scheduling | 30% fewer trucks across ~1M annual trips | \n| Midstream | Neural-network routing of a gas gathering network | Near-zero non-routine flaring, no new capital expenditure | \n| Downstream | Autonomous refinery drones with AI analytics | One mission found $15M+ potential savings | \n| Gas utility | AI disaggregation of monthly gas billing data | 283% of savings goal | \n| Electric utility | Machine learning to target high-risk vegetation | ~40% fewer vegetation-related outages | \n| Solar power | Machine learning solar tracker control | Up to 4% higher yield across 50+ GW | \n| Wind power | Neural-network wind blade defect detection | 61% faster detection; 95% accuracy | \nImportantly, these are company- and vendor-reported figures, so we interpret them not as a promise but as a directional signal of where AI is heading and what a well-executed deployment can achieve. Notably, we’re now seeing agentic AI showing up in use cases, and we only expect its presence to grow.\nWhen it comes to slow AI adoption, the obstacle is rarely the AI itself. In our client work, echoed by surveys from the Massachusetts Institute of Technology, the most common constraints relate to data availability and governance, lack of trust in the AI output, cybersecurity and privacy/regulatory concerns, lack of executive sponsorship, and difficulty managing organizational change.\nScaling AI use cases from pilot to implementation is also a key challenge. Among respondents to RSM’s Middle Market AI Survey 2026: U.S. and Canada, only 36% of respondents have AI fully embedded across core processes. Among those who conducted AI pilots in the prior two years, about half (51%) described the success of those pilots as moderate or limited—and among those respondents, data quality issues (53%) and integration challenges (47%) were the leading reasons.\nFurther, the survey showed that while technology-related items are the more common inhibitors to AI deployment, two-thirds of respondents noted AI governance was established before implementing AI pilots or production.\nPredictive maintenance, forecasting and operational optimization tools all rely on data that is accurate, connected and trusted across the organization. Establishing clear data ownership and governance frameworks can help energy companies move AI initiatives from pilot projects to broader business adoption while improving confidence in AI-generated insights.\nLearn more: AI consulting services\nEnergy companies looking to accelerate AI adoption should:\nOver the coming years, we expect the constraint on AI adoption among energy companies to be less about access to the technology and more about whether a company’s data is in a condition to use it. Those that make strategic investments now to organize and govern their data will be better positioned to capture value as adoption accelerates.","excerpt":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 7193 characters.","diagnostics_url":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 7193 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":7193,"summary_length":177,"usable_text_length":7193,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":7193,"summary_length":177}}},"display_formats":["compact","card","full","digest_section","json"]},"daily_stack_record":{"title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","summary":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","source":"rsmus.com","date":"2026-09-10T17:03:45+00:00","content":"An RSM analysis shows how middle market companies are using AI across energy subsectors.\nAn RSM analysis shows how middle market companies are using AI across energy subsectors.\nUtilities appear to be deploying AI at a higher rate than oil and gas companies.\nPredictive maintenance has emerged as the most common use case.\nImplementing artificial intelligence has become an imperative for companies across industries, including those in energy. Identifying the right use cases is a critical step in maximizing the value of this technology. This is where sector-specific use cases are especially important to understand.\nWhile some companies have successfully deployed AI and machine learning use cases—from the front and back offices to plant and field operations—challenges remain. This calls for leaders to ask key questions such as:\nThere are no easy answers to these questions, but an RSM analysis of how middle market energy companies are deploying AI offers valuable insights, including lessons learned and an understanding of how AI use cases vary across energy subsectors.\nFor this analysis, we reviewed company disclosures, including earnings calls, annual reports and investor presentations from over 300 publicly traded, North America-domiciled energy companies with annual revenue between $100 million and $20 billion from January 2021 through July 2026. We also reviewed other public sources of information for these companies, including vendor case studies, utility commission filings and engineering papers.\nThis analysis provides a data-backed view of how middle market energy companies are deploying AI and what others can learn from their experience.\nHere are some of the key takeaways:\nTake a closer look at the findings below.\nMany of the barriers slowing AI adoption in the energy sector stem from data—its governance, controls and trustworthiness—not the technology itself. As AI becomes more embedded in operations, tax data, reporting processes and governance frameworks need to keep pace with the broader transformation. Stronger data foundations can support more reliable reporting and better-informed business decisions.\nLearn more: AI is working in tax. Scaling it is the hard part.\nAcross the 307 companies, 45% have at least one AI or machine learning use case running in operation. Because the analysis excluded unverified announcements and use cases without specific evidence, actual adoption is likely higher. Our conversations and work with energy companies on their AI journey anecdotally point higher still.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies. Oil and gas may be at an inflection point, however, as mentions of AI in earnings calls by Russell 3000 energy companies tripled in the first half of 2026 compared to the same period in 2025.\nGrouped more broadly, power and utilities companies showed a 77% AI deployment rate, compared with 43% for renewables and 33% for oil and gas companies. Some of that gap may be because utilities more often operate under a regulated filing regime and provide more disclosures.\nThe next natural question is what specific use cases are most often named, and what value they are delivering.\nIn our analysis of AI use among energy companies, some of the common use cases companies identified were:\nOverlap between the oil and gas sector and the power and utilities sector varied by use case.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies.\nOther insights from our analysis include:\nMore AI deployments have documented business outcomes now than when we last analyzed industry use cases in 2023.\nThe examples below show how companies across energy subsectors are turning use cases into business value.\n| Oil field services | Agentic AI parts ordering for 800 field technicians | $3M annual return; 90,000+ hours saved | \n| Upstream | AI agents monitoring drilling in real time | 12% lower drilling cost, or about $1M per well | \n| Oil field services | AI demand forecasting and frac fleet scheduling | 30% fewer trucks across ~1M annual trips | \n| Midstream | Neural-network routing of a gas gathering network | Near-zero non-routine flaring, no new capital expenditure | \n| Downstream | Autonomous refinery drones with AI analytics | One mission found $15M+ potential savings | \n| Gas utility | AI disaggregation of monthly gas billing data | 283% of savings goal | \n| Electric utility | Machine learning to target high-risk vegetation | ~40% fewer vegetation-related outages | \n| Solar power | Machine learning solar tracker control | Up to 4% higher yield across 50+ GW | \n| Wind power | Neural-network wind blade defect detection | 61% faster detection; 95% accuracy | \nImportantly, these are company- and vendor-reported figures, so we interpret them not as a promise but as a directional signal of where AI is heading and what a well-executed deployment can achieve. Notably, we’re now seeing agentic AI showing up in use cases, and we only expect its presence to grow.\nWhen it comes to slow AI adoption, the obstacle is rarely the AI itself. In our client work, echoed by surveys from the Massachusetts Institute of Technology, the most common constraints relate to data availability and governance, lack of trust in the AI output, cybersecurity and privacy/regulatory concerns, lack of executive sponsorship, and difficulty managing organizational change.\nScaling AI use cases from pilot to implementation is also a key challenge. Among respondents to RSM’s Middle Market AI Survey 2026: U.S. and Canada, only 36% of respondents have AI fully embedded across core processes. Among those who conducted AI pilots in the prior two years, about half (51%) described the success of those pilots as moderate or limited—and among those respondents, data quality issues (53%) and integration challenges (47%) were the leading reasons.\nFurther, the survey showed that while technology-related items are the more common inhibitors to AI deployment, two-thirds of respondents noted AI governance was established before implementing AI pilots or production.\nPredictive maintenance, forecasting and operational optimization tools all rely on data that is accurate, connected and trusted across the organization. Establishing clear data ownership and governance frameworks can help energy companies move AI initiatives from pilot projects to broader business adoption while improving confidence in AI-generated insights.\nLearn more: AI consulting services\nEnergy companies looking to accelerate AI adoption should:\nOver the coming years, we expect the constraint on AI adoption among energy companies to be less about access to the technology and more about whether a company’s data is in a condition to use it. Those that make strategic investments now to organize and govern their data will be better positioned to capture value as adoption accelerates.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 7193 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 7193 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":7193,"summary_length":177,"usable_text_length":7193,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":7193,"summary_length":177}},"tags":[]},"fallback_formats":["markdown","json","html"],"actions":{"read":"/item/79624","export_markdown":"/api/items/79624/export?format=markdown","export_json":"/api/items/79624/export?format=json","diagnose":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html"},"formats":{"full":{"id":79624,"title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","source":"rsmus.com","author":null,"published_at":"2026-09-10T17:03:45+00:00","locale":"en","topic":"ai","tags":[],"excerpt":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","full_text":"An RSM analysis shows how middle market companies are using AI across energy subsectors.\nAn RSM analysis shows how middle market companies are using AI across energy subsectors.\nUtilities appear to be deploying AI at a higher rate than oil and gas companies.\nPredictive maintenance has emerged as the most common use case.\nImplementing artificial intelligence has become an imperative for companies across industries, including those in energy. Identifying the right use cases is a critical step in maximizing the value of this technology. This is where sector-specific use cases are especially important to understand.\nWhile some companies have successfully deployed AI and machine learning use cases—from the front and back offices to plant and field operations—challenges remain. This calls for leaders to ask key questions such as:\nThere are no easy answers to these questions, but an RSM analysis of how middle market energy companies are deploying AI offers valuable insights, including lessons learned and an understanding of how AI use cases vary across energy subsectors.\nFor this analysis, we reviewed company disclosures, including earnings calls, annual reports and investor presentations from over 300 publicly traded, North America-domiciled energy companies with annual revenue between $100 million and $20 billion from January 2021 through July 2026. We also reviewed other public sources of information for these companies, including vendor case studies, utility commission filings and engineering papers.\nThis analysis provides a data-backed view of how middle market energy companies are deploying AI and what others can learn from their experience.\nHere are some of the key takeaways:\nTake a closer look at the findings below.\nMany of the barriers slowing AI adoption in the energy sector stem from data—its governance, controls and trustworthiness—not the technology itself. As AI becomes more embedded in operations, tax data, reporting processes and governance frameworks need to keep pace with the broader transformation. Stronger data foundations can support more reliable reporting and better-informed business decisions.\nLearn more: AI is working in tax. Scaling it is the hard part.\nAcross the 307 companies, 45% have at least one AI or machine learning use case running in operation. Because the analysis excluded unverified announcements and use cases without specific evidence, actual adoption is likely higher. Our conversations and work with energy companies on their AI journey anecdotally point higher still.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies. Oil and gas may be at an inflection point, however, as mentions of AI in earnings calls by Russell 3000 energy companies tripled in the first half of 2026 compared to the same period in 2025.\nGrouped more broadly, power and utilities companies showed a 77% AI deployment rate, compared with 43% for renewables and 33% for oil and gas companies. Some of that gap may be because utilities more often operate under a regulated filing regime and provide more disclosures.\nThe next natural question is what specific use cases are most often named, and what value they are delivering.\nIn our analysis of AI use among energy companies, some of the common use cases companies identified were:\nOverlap between the oil and gas sector and the power and utilities sector varied by use case.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies.\nOther insights from our analysis include:\nMore AI deployments have documented business outcomes now than when we last analyzed industry use cases in 2023.\nThe examples below show how companies across energy subsectors are turning use cases into business value.\n| Oil field services | Agentic AI parts ordering for 800 field technicians | $3M annual return; 90,000+ hours saved | \n| Upstream | AI agents monitoring drilling in real time | 12% lower drilling cost, or about $1M per well | \n| Oil field services | AI demand forecasting and frac fleet scheduling | 30% fewer trucks across ~1M annual trips | \n| Midstream | Neural-network routing of a gas gathering network | Near-zero non-routine flaring, no new capital expenditure | \n| Downstream | Autonomous refinery drones with AI analytics | One mission found $15M+ potential savings | \n| Gas utility | AI disaggregation of monthly gas billing data | 283% of savings goal | \n| Electric utility | Machine learning to target high-risk vegetation | ~40% fewer vegetation-related outages | \n| Solar power | Machine learning solar tracker control | Up to 4% higher yield across 50+ GW | \n| Wind power | Neural-network wind blade defect detection | 61% faster detection; 95% accuracy | \nImportantly, these are company- and vendor-reported figures, so we interpret them not as a promise but as a directional signal of where AI is heading and what a well-executed deployment can achieve. Notably, we’re now seeing agentic AI showing up in use cases, and we only expect its presence to grow.\nWhen it comes to slow AI adoption, the obstacle is rarely the AI itself. In our client work, echoed by surveys from the Massachusetts Institute of Technology, the most common constraints relate to data availability and governance, lack of trust in the AI output, cybersecurity and privacy/regulatory concerns, lack of executive sponsorship, and difficulty managing organizational change.\nScaling AI use cases from pilot to implementation is also a key challenge. Among respondents to RSM’s Middle Market AI Survey 2026: U.S. and Canada, only 36% of respondents have AI fully embedded across core processes. Among those who conducted AI pilots in the prior two years, about half (51%) described the success of those pilots as moderate or limited—and among those respondents, data quality issues (53%) and integration challenges (47%) were the leading reasons.\nFurther, the survey showed that while technology-related items are the more common inhibitors to AI deployment, two-thirds of respondents noted AI governance was established before implementing AI pilots or production.\nPredictive maintenance, forecasting and operational optimization tools all rely on data that is accurate, connected and trusted across the organization. Establishing clear data ownership and governance frameworks can help energy companies move AI initiatives from pilot projects to broader business adoption while improving confidence in AI-generated insights.\nLearn more: AI consulting services\nEnergy companies looking to accelerate AI adoption should:\nOver the coming years, we expect the constraint on AI adoption among energy companies to be less about access to the technology and more about whether a company’s data is in a condition to use it. Those that make strategic investments now to organize and govern their data will be better positioned to capture value as adoption accelerates.","reading_time_min":6,"extraction":{"state":"ok","confidence":0.9,"error":null,"explanation":"High confidence: full text extraction produced 7193 characters.","diagnostics_url":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 7193 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":7193,"summary_length":177,"usable_text_length":7193,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":7193,"summary_length":177}}},"quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 7193 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":7193,"summary_length":177,"usable_text_length":7193,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":7193,"summary_length":177}},"actions":{"read":"/item/79624","export_markdown":"/api/items/79624/export?format=markdown","export_json":"/api/items/79624/export?format=json","diagnose":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html"}},"digest":{"id":79624,"title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","source":"rsmus.com","topic":"ai","published_at":"2026-09-10T17:03:45+00:00","excerpt":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","quality_bucket":"high","quality_reason":"High confidence: full text extraction produced 7193 characters.","reading_time_min":6,"cluster_id":null},"card":{"display_title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","subtitle":"rsmus.com · 2026-09-10","summary":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","badges":["quality:high"],"links":{"read":"/item/79624","original":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","diagnose":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html"},"quality_warning":null},"export":{"title":"AI use cases in the energy industry: Insights from research and company filings - rsmus.com","url":"https://rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","summary":"An RSM analysis shows how middle market companies are using AI across energy subsectors. An RSM analysis shows how middle market companies are using AI across energy subsectors.","source":"rsmus.com","date":"2026-09-10T17:03:45+00:00","content":"An RSM analysis shows how middle market companies are using AI across energy subsectors.\nAn RSM analysis shows how middle market companies are using AI across energy subsectors.\nUtilities appear to be deploying AI at a higher rate than oil and gas companies.\nPredictive maintenance has emerged as the most common use case.\nImplementing artificial intelligence has become an imperative for companies across industries, including those in energy. Identifying the right use cases is a critical step in maximizing the value of this technology. This is where sector-specific use cases are especially important to understand.\nWhile some companies have successfully deployed AI and machine learning use cases—from the front and back offices to plant and field operations—challenges remain. This calls for leaders to ask key questions such as:\nThere are no easy answers to these questions, but an RSM analysis of how middle market energy companies are deploying AI offers valuable insights, including lessons learned and an understanding of how AI use cases vary across energy subsectors.\nFor this analysis, we reviewed company disclosures, including earnings calls, annual reports and investor presentations from over 300 publicly traded, North America-domiciled energy companies with annual revenue between $100 million and $20 billion from January 2021 through July 2026. We also reviewed other public sources of information for these companies, including vendor case studies, utility commission filings and engineering papers.\nThis analysis provides a data-backed view of how middle market energy companies are deploying AI and what others can learn from their experience.\nHere are some of the key takeaways:\nTake a closer look at the findings below.\nMany of the barriers slowing AI adoption in the energy sector stem from data—its governance, controls and trustworthiness—not the technology itself. As AI becomes more embedded in operations, tax data, reporting processes and governance frameworks need to keep pace with the broader transformation. Stronger data foundations can support more reliable reporting and better-informed business decisions.\nLearn more: AI is working in tax. Scaling it is the hard part.\nAcross the 307 companies, 45% have at least one AI or machine learning use case running in operation. Because the analysis excluded unverified announcements and use cases without specific evidence, actual adoption is likely higher. Our conversations and work with energy companies on their AI journey anecdotally point higher still.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies. Oil and gas may be at an inflection point, however, as mentions of AI in earnings calls by Russell 3000 energy companies tripled in the first half of 2026 compared to the same period in 2025.\nGrouped more broadly, power and utilities companies showed a 77% AI deployment rate, compared with 43% for renewables and 33% for oil and gas companies. Some of that gap may be because utilities more often operate under a regulated filing regime and provide more disclosures.\nThe next natural question is what specific use cases are most often named, and what value they are delivering.\nIn our analysis of AI use among energy companies, some of the common use cases companies identified were:\nOverlap between the oil and gas sector and the power and utilities sector varied by use case.\nConfirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies.\nOther insights from our analysis include:\nMore AI deployments have documented business outcomes now than when we last analyzed industry use cases in 2023.\nThe examples below show how companies across energy subsectors are turning use cases into business value.\n| Oil field services | Agentic AI parts ordering for 800 field technicians | $3M annual return; 90,000+ hours saved | \n| Upstream | AI agents monitoring drilling in real time | 12% lower drilling cost, or about $1M per well | \n| Oil field services | AI demand forecasting and frac fleet scheduling | 30% fewer trucks across ~1M annual trips | \n| Midstream | Neural-network routing of a gas gathering network | Near-zero non-routine flaring, no new capital expenditure | \n| Downstream | Autonomous refinery drones with AI analytics | One mission found $15M+ potential savings | \n| Gas utility | AI disaggregation of monthly gas billing data | 283% of savings goal | \n| Electric utility | Machine learning to target high-risk vegetation | ~40% fewer vegetation-related outages | \n| Solar power | Machine learning solar tracker control | Up to 4% higher yield across 50+ GW | \n| Wind power | Neural-network wind blade defect detection | 61% faster detection; 95% accuracy | \nImportantly, these are company- and vendor-reported figures, so we interpret them not as a promise but as a directional signal of where AI is heading and what a well-executed deployment can achieve. Notably, we’re now seeing agentic AI showing up in use cases, and we only expect its presence to grow.\nWhen it comes to slow AI adoption, the obstacle is rarely the AI itself. In our client work, echoed by surveys from the Massachusetts Institute of Technology, the most common constraints relate to data availability and governance, lack of trust in the AI output, cybersecurity and privacy/regulatory concerns, lack of executive sponsorship, and difficulty managing organizational change.\nScaling AI use cases from pilot to implementation is also a key challenge. Among respondents to RSM’s Middle Market AI Survey 2026: U.S. and Canada, only 36% of respondents have AI fully embedded across core processes. Among those who conducted AI pilots in the prior two years, about half (51%) described the success of those pilots as moderate or limited—and among those respondents, data quality issues (53%) and integration challenges (47%) were the leading reasons.\nFurther, the survey showed that while technology-related items are the more common inhibitors to AI deployment, two-thirds of respondents noted AI governance was established before implementing AI pilots or production.\nPredictive maintenance, forecasting and operational optimization tools all rely on data that is accurate, connected and trusted across the organization. Establishing clear data ownership and governance frameworks can help energy companies move AI initiatives from pilot projects to broader business adoption while improving confidence in AI-generated insights.\nLearn more: AI consulting services\nEnergy companies looking to accelerate AI adoption should:\nOver the coming years, we expect the constraint on AI adoption among energy companies to be less about access to the technology and more about whether a company’s data is in a condition to use it. Those that make strategic investments now to organize and govern their data will be better positioned to capture value as adoption accelerates.","confidence":0.9,"diagnostics_url":"/api/diagnose?url=https%3A//rsmus.com/insights/industries/energy/ai-use-cases-in-the-energy-sector.html","quality_bucket":"high","failure_kind":"none","retryable":false,"quality_reason":"High confidence: full text extraction produced 7193 characters.","quality_profile":{"profile_version":"extraction_quality.v2","bucket":"high","confidence":0.9,"failure_kind":"none","retryable":false,"retry_after_attempts":0,"reason":"High confidence: full text extraction produced 7193 characters.","operator_guidance":{"severity":"ok","recommended_action":"trust_full_text","next_step":"Use the extracted full text as the primary article source.","operator_label":"Ready","can_retry":false,"can_use_summary":false,"diagnostics_required":false},"content_depth":{"contract_version":"content_depth.v1","category":"full_text","label":"Full text","has_full_text":true,"has_summary":true,"content_length":7193,"summary_length":177,"usable_text_length":7193,"source_field":"content"},"legacy_collapsed":false,"signals":{"extract_state":"ok","extract_error":null,"extract_retries":0,"content_length":7193,"summary_length":177}},"tags":[],"format_contract_version":"news_item_formats.v1"}}}