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Artificial intelligence is transforming industries worldwide, and the energy sector is no exception. As energy companies seek greater operational efficiency, improved decision-making, and enhanced safety, AI is rapidly becoming embedded across operations, creating complex legal, regulatory, and governance challenges that demand careful oversight.
This topic was the focus of a recent Legal Innovation Forum webinar hosted in association with Exterro: AI in the energy sector: Opportunity, risk, and the role of lawyers. Drawing on perspectives from private practice, in-house counsel, and legal technology, our expert panelists explored how AI is reshaping the energy industry and the role of legal leaders in navigating the legal, regulatory, and commercial risks it creates.
It’s clear that AI is no longer an emerging technology that organizations can observe from the sidelines. It is becoming integral to how energy companies operate, creating new responsibilities—and opportunities—for legal departments.
The dual impact of AI
Clifford Chance energy partner Marcia Hook described this phenomenon as creating a “speed to power” challenge.
“The interesting thing about AI is it’s not just a tool in the energy sector,” said Hook. “It’s also driving demand. That demand is, in itself, shaping the energy industry and investment decisions.”
As technology companies race to build AI infrastructure, they require access to reliable electricity much faster than timelines typically allow. This growing demand is reshaping investment priorities across electricity generation, transmission infrastructure, renewable energy projects, and grid modernization.
As AI infrastructure expands, the challenge for energy companies is no longer simply embracing AI, but ensuring they can deliver the power needed to support it.
At the same time, energy companies themselves are adopting AI to improve operational performance. Panelists highlighted a range of practical applications, including pipeline integrity monitoring, safety, document management, and energy trading.
“In the integrity space—so in pipelines and transmission lines—we’re also seeing the use of AI-powered technology to improve the quality of crack detection,” noted Melissa Stoesser Young, energy partner at McMillan.
Practical adoption
Our speakers emphasized that AI adoption within the energy sector is much broader and more practical than many realize.
Many energy companies are already deploying AI to automate document review, analyze operational data, detect equipment anomalies before failures occur, improve safety monitoring, and support environmental reporting.
TC Energy’s director of litigation & employment law, Lesley Lee, said organizations should resist the temptation to pursue AI everywhere at once.
“The right strategy is to be focused and disciplined in how you adopt AI… so that you’re achieving real value,” said Lee.
Instead, many energy companies are prioritizing targeted use cases where human oversight remains central to decision-making. Rather than replacing employees, AI is being used to augment professional expertise and reduce repetitive administrative work.
AI-assisted document review, contract analysis, legal research, and eDiscovery are helping legal departments manage increasing workloads while improving efficiency.
AI’s greatest value is allowing lawyers to spend more time on strategic work.
“Being able to focus on that higher-value work is really what clients are looking for,” said Bryant Bell, lead product marketer, eDiscovery at Exterro, an AI-powered data risk management software company, based in Portland, Oregon. He added that AI is also prompting firms to rethink traditional billing models and, where appropriate, “move toward charging by the matter.”
Responsible governance
While AI offers significant opportunities, the panel consistently returned to one overarching message: governance cannot be an afterthought.
As Stoesser Young explained, energy companies need governance frameworks that evolve alongside AI adoption.
“As you move up the chain of enterprise risk management… it’s essential that you have the human component not getting completely replaced with AI,” she said.
Stoesser Young also pointed out that AI is changing traditional approaches to legal risk.
“You’re no longer guaranteed to be operating within the traditional legal allocation of risk space that we’ve been used to for a long time,” she said, reinforcing why governance frameworks, vendor due diligence, and cross-functional oversight have become essential as organizations adopt AI.
Training also emerged as a critical component of successful implementation. Employees need practical education not only on how AI systems function but also on their limitations. AI-generated outputs may appear authoritative while containing inaccuracies or even fabricated information, making human review essential.
Managing legal risk
While AI adoption is accelerating, the regulatory landscape remains fragmented across jurisdictions, creating additional complexity within the highly regulated energy sector.
As Lee explained, navigating that complexity is especially challenging for organizations like TC Energy that operate across multiple jurisdictions.
“The regulatory framework around AI is still quite limited,” she said. “We operate in three jurisdictions—Canada, the U.S. and Mexico. The laws are different at the state and federal level… so piecing that together is a challenge.”
Rather than one comprehensive framework, organizations must navigate a patchwork of emerging legislation, privacy requirements, sector-specific rules, contractual obligations, and professional responsibilities.
Hook echoed that point, noting that legal teams must think beyond today’s rules.
“We always have to worry about future-proofing our contracts… making sure that we draft our contracts in a way that can survive evolution of the regulatory regime,” said Hook.
For legal departments, AI governance cannot simply be delegated to IT teams. Lawyers are increasingly involved in technology procurement, vendor due diligence, policy development, contract negotiation, regulatory monitoring, and incident response planning.
The human advantage
A recurring theme throughout the webinar was that AI should enhance—not replace—human expertise.
“AI can help your accuracy… but what AI doesn’t do is replace the judgment of your legal team,” said Bell. He emphasized that while AI can improve efficiency, organizations cannot assume its outputs are always reliable.
“A lot of the GCs are worried about… can I rely on AI to actually give me the correct data?” he said. “You can connect AI to your operational technology… but if it isn’t giving you accurate data, what type of liability does that open up?”
For Bell, the answer lies in maintaining professional judgment.
“If you use AI, it’s your professional responsibility to make sure that you review it and give it a critical review before you pass it on,” he said. “That’s just the way we should all work, whether it’s AI or not.” AI may accelerate legal work, but accountability ultimately remains with the lawyer.
Similarly, Lee emphasized that AI is most effective as a tool to support experienced professionals—not as a replacement for them. Whether reviewing contracts, summarizing documents, or analyzing operational data, final decisions remain the responsibility of experienced users.
Energy companies that maintain meaningful human oversight are better positioned to identify errors, challenge unexpected outputs, and ensure accountability for important decisions.
preparing for what's next
As AI adoption accelerates, legal departments must evolve alongside the technology. Waiting for regulatory certainty before taking action is no longer a practical option. Instead, Lee encouraged organizations to start small and learn from experience.
“Run test cases… and then roll that out to more cases, as opposed to trying to do everything all at once,” she advised. “That’s how you figure out where you’re actually getting value.”
Energy companies should also establish enterprise-wide governance policies, invest in employee education, evaluate technology vendors carefully, and continue monitoring evolving legislation. Our speakers agreed that legal teams that proactively engage with AI today will be better positioned to support innovation while protecting their organizations from unnecessary risk.
A strategic opportunity
The discussion underscored that AI represents more than another technology trend. It is fundamentally reshaping both how energy companies operate and how the industry will meet rising demand.
As organizations continue to experiment with AI, the challenge is no longer whether to adopt the technology, but how to do so responsibly. Strong governance, cross-functional collaboration, and meaningful human oversight will be essential to realizing AI’s benefits while managing legal and commercial risk.
Despite concerns about the future role of lawyers, our speakers remain optimistic.
Panelists agreed that AI will not replace technical expertise or legal judgment, but it will increasingly define how work gets done. For legal leaders, the opportunity is to help their organizations embrace innovation with confidence—building governance frameworks that enable progress while protecting the business from unnecessary risk.
“It’s our job as outside counsel to figure out how to use AI, and that it makes our teams able to do much better, more sophisticated work for our clients, and so… I’m optimistic still about us having value, even in the AI future,” said Hook.
Frequently Asked Questions: Managing AI Risk, Governance, and Compliance in the Energy Sector
The primary legal risks of AI in the energy sector involve regulatory non-compliance, operational failures, and the erosion of legal privilege. Energy companies face a unique landscape where inaccurate AI outputs can lead to physical infrastructure damage, such as pipeline integrity issues or grid instability, in addition to standard financial and legal exposures.
Key risks include:
- Operational Liability: Decisions moving high on an enterprise risk matrix require strict human oversight to prevent catastrophic physical failures.
- Data Integrity and Privacy: Confidentiality breaches and intellectual property disputes arise when sensitive data is processed through unvetted models.
- Regulatory Scrutiny: As noted by Bryant Bell of Exterro, GCs are increasingly concerned with whether they can rely on AI for accurate data in highly regulated jurisdictions like the US, Canada, and Mexico.
Exterro’s AI governance tools help organizations mitigate these risks by providing visibility into data sources and ensuring that AI-driven telemetry across pipelines and wells remains precise and defensible.
Effective human oversight in the energy sector is achieved by aligning the level of manual review with the potential risk level on the organization’s enterprise risk matrix. While AI excels at processing massive datasets and identifying patterns in transmission systems, it should serve as a support mechanism for compliance professionals rather than a replacement for professional judgment.
Bryant Bell of Exterro emphasizes that AI implementation should include a "safety gap" where human judgment validates high-stakes outputs. This is particularly critical in energy operations where telemetry data from operational technology must be verified before making grid-level decisions.
To maintain this balance, companies should:
- Define clear "human-in-the-loop" protocols for regulatory filings and operational maintenance.
- Use Exterro’s solutions to manage data sources, ensuring that legal teams can quickly get to the root of issues when AI outputs deviate from expected outcomes.
By focusing AI on repetitive tasks like document summarization, legal teams can focus on higher-level legal reasoning and evaluations that AI currently cannot perform reliably.
An AI governance framework for energy companies must include defined usage policies, verification protocols, and tools to eliminate "shadow AI." These frameworks establish guardrails for data entry, ensuring that employees do not expose sensitive critical infrastructure information to unapproved third-party models.
Bryant Bell highlights the pervasive risk of employees using unvetted AI tools to handle complex data. To counter this, Exterro offers governance and compliance platforms that provide a centralized environment for responsible AI use. This approach moves beyond simple restriction, empowering employees with approved tools and training.
Essential components of an energy sector AI framework include:
- Explicit lists of approved AI tools and forbidden data types.
- Mandatory verification workflows for AI-generated operational or legal drafts.
- Exterro’s data management capabilities to ensure all AI interactions are auditable and compliant with evolving state and federal regulations.
Liability for AI errors in the energy sector typically remains with the operator, as technology providers frequently limit their responsibility for inaccurate outputs through complex contracts.
This creates a gap where the energy company assumes risks traditionally handled by vendors, making contractual review of indemnification and data ownership paramount.
Bryant Bell notes that General Counsels must realize AI is not infallible and cannot "just do everything." Without human oversight, a "hallucination"—such as the example Bryant shared where a model erroneously inserted a fictional character into a legal draft—could lead to severe litigation or regulatory penalties.
To protect against liability, energy legal departments should:
- Audit vendor AI models for potential "data corruption" or training bias.
- Implement Exterro’s eDiscovery and risk management tools to preserve evidence if an AI operational failure leads to a dispute.
Ultimately, saying "the AI did it" is not a valid legal defense for an operational failure that impacts public safety or grid transmission.
Energy companies must address AI risks in contracts by ensuring visibility into how vendors use company data and how third-party models are integrated into provided services. Contracts should clearly define data protection, intellectual property rights, and the specific permitted uses of company information within the vendor’s AI ecosystem.
As Bryant Bell observes, vendors often rely on multiple technology layers, which can obscure where liability actually sits. Companies need greater transparency to understand if their sensitive data is being used to train third-party models.
Strategic contracting steps include:
- Demanding disclosure of all AI tools used by external counsel and service providers.
- Using Exterro’s vendor management capabilities to track compliance with contractual data security requirements.
By leveraging these strategies, energy firms can future-proof their supply arrangements against the rapid evolution of AI technology and the shifting regulatory landscape.
To protect confidential information and privilege, legal departments must implement strict policies that prohibit the entry of sensitive data into unapproved or open AI tools. This involves not only employee training but also the creation of approved technology environments that utilize appropriate safeguards such as encryption and access controls.
Bryant Bell emphasizes that the responsibility for checking work remains with the legal professional, regardless of the tools used. Just as a lawyer would not submit a brief without proofreading, AI outputs must be critically reviewed to ensure they do not inadvertently waive legal privilege or expose proprietary transmission data.
Methods for safeguarding information include:
- Utilizing Exterro’s secure AI governance tools which are designed specifically for legal and compliance environments
- Enforcing "first principle" reviews for all AI-assisted document drafting to maintain professional and ethical obligations.
These measures ensure that the speed of AI does not compromise the foundational requirements of legal practice in the energy sector.
While AI can significantly streamline the collection and analysis of regulatory data, it does not absolve energy companies of their ultimate responsibility to comply with legal mandates.
Organizations remain fully accountable for any inaccurate results or non-compliant actions triggered by an AI recommendation or operational decision.
Expert Bryant Bell notes that as decision risks increase, the need for human oversight grows. AI should be viewed as a tool to accelerate the work of compliance teams, not a way to automate them out of the process. For energy companies operating across state lines with varying regulations, the ability of AI to track developments is a major asset, provided it is verified.
Regulatory compliance is supported by:
- Using AI to identify patterns in regulatory filings that human reviewers might miss.
- Exterro’s compliance platforms that help ensure data used for regulatory reporting is accurate, auditable, and stored according to jurisdictional requirements.
This disciplined approach ensures that AI enhances compliance rather than creating new regulatory liabilities.
For critical infrastructure operators, AI introduces systemic risks such as cybersecurity vulnerabilities, operational data inaccuracies, and jurisdictional regulatory uncertainty. These risks are intensified for companies managing power generation and transmission across multiple regions, where a single AI error can have cross-border legal and physical consequences.
Bryant Bell emphasizes that General Counsels in the energy sector are particularly focused on the liability of adopting AI for front-end maintenance and demand adjustment. If the data provided by AI is incorrect, it opens the company to significant litigation and regulatory investigations.
Operators can manage these risks by:
- Establishing strong AI governance processes that monitor regulatory shifts across state and federal levels.
- Leveraging Exterro’s data preservation tools to ensure relevant operational data is available for analysis in the event of an investigation.
Maintaining human oversight for high-risk decisions ensures that critical infrastructure remains safe and legally compliant in the AI era.
AI significantly enhances litigation and eDiscovery by accelerating the identification of relevant documents, uncovering hidden patterns in massive datasets, and streamlining the document review process. In an industry dealing with increasingly diverse and complex data, AI tools allow legal teams to respond more efficiently to regulatory requests and litigation.
Bryant Bell of Exterro points out that AI is essential for finding information that might otherwise be missed. By using technologies like Exterro, legal professionals can accelerate the process of identifying and managing relevant data, allowing them to focus on the higher-level legal analysis required for sophisticated matters.
Key benefits include:
- Faster identification of potential evidence across complex data sources.
- Exterro’s eDiscovery solutions ensure that outputs are defensible and that privileged or personally identifiable information is protected.
While AI handles the data volume, lawyers must still apply their judgment to ensure the final legal work product meets ethical standards.
Human judgment is the essential component of generative AI use in legal departments, as it provides the critical reasoning, evaluation, and professional ethics that AI cannot replicate. While AI is proficient at summarizing documents and accelerating repetitive tasks, it lacks the ability to perform complex legal reasoning or create sophisticated analysis from scratch.
Bryant Bell notes that AI should inform decisions faster rather than replace the professional. Lawyers have a professional responsibility to check their own work and that of their team, ensuring accuracy and protecting confidential information before any work product is finalized.
The role of the legal professional involves:
- Verifying AI-generated work to ensure it meets ethical and professional standards.
- Using Exterro’s insights to focus on "higher value work" that clients prioritize over routine data processing.
Ultimately, AI is a tool for accuracy and efficiency, but human judgment remains the bedrock of legal services in the energy industry.