Agentic AI in Oil & Gas Supply Chains: What Energy Executives Need to Know
Agentic artificial intelligence is moving quickly from theory to implementation in oil and gas supply chains. Boston Consulting Group’s 2025 work on AI-first oil and gas companies estimates that firms which fully integrate AI and AI agents into operations can generate incremental profits equivalent to thirty to seventy per cent of earnings before taxes over five years, shrinking processes from months to weeks and cutting operating costs materially across the value chain. At the same time, global surveys find that nearly nine in ten large organizations now use AI in at least one business function, and that generative and agentic systems are beginning to influence core workflows rather than just peripheral analytics.
Against this backdrop, capital is flowing into oil and gas infrastructure in ways that depend on digital coordination. Industrial Info Resources reports that Latin America as a whole is positioned to benefit from disruptions in other producing regions, tracking more than five hundred active capital oil and gas production projects in Brazil alone worth over forty-two billion dollars, with the Brazilian government projecting oil and gas investments of approximately eighty-three and a half billion dollars by 2032 and potentially more than five hundred billion dollars between 2025 and 2035. These projects rely on complex supply chains involving offshore platforms, pipelines, storage, shipping, trading and finance. As agentic AI is layered onto that infrastructure, the question for chief operating officers and senior executives is not whether these systems will be deployed, but how their deployment reshapes operational, legal and geopolitical risk.
Agentic systems differ from earlier AI deployments in two respects that matter for governance. First, they are designed to act across multiple steps without continuous human initiation. In an oil and gas supply chain, that may include monitoring vessel positions and weather, re-sequencing cargoes, adjusting nominations, initiating hedging instructions, triggering payments or reprioritizing maintenance based on modelled risk. BCG analysis already envisages “trading agentic AI” that supports arbitrage and automates deal execution, and AI agents orchestrating turnaround planning and health, safety and environment tasks in real time. Second, the value attributed to these systems derives from their ability to operate at a speed, scale and level of integration that no human team can match. That is precisely what complicates governance. When a model can reconfigure logistics or financial exposure across dozens of assets in minutes, ex post review becomes a reconstruction exercise rather than a check on a proposed action.
From a risk perspective, three features of agentic AI are central for COOs and executives. The first is opacity. Even when vendors provide high-level descriptions of models and decision policies, the internal logic of agentic systems is rarely transparent in a way that would satisfy regulators or courts if a decision sequence is challenged. This is not a new problem in AI, but it is magnified when outputs take the form of action rather than recommendations. The evidentiary record in a dispute or investigation will be a combination of code, logs and vendor documentation, not a clear narrative of why a given shipment was diverted, or a contract was executed at a particular price.
The second is coupling. Supply chains connect physical infrastructure, digital platforms and financial flows, often across jurisdictions with very different regulatory and security baselines. The World Economic Forum’s 2025 Global Cybersecurity Outlook emphasizes that critical infrastructure is increasingly dependent on networks of interconnected devices and legacy systems, and that energy systems in particular are at heightened risk from sophisticated actors exploiting that complexity. When agentic AI systems are integrated into this environment, a failure or compromise in one component can propagate rapidly. For instance, a model trained on historical port performance and security incidents may begin to avoid certain terminals in response to new threat intelligence, reallocating cargoes to alternative routes that themselves face unmodelled risks. If the underlying data or logic is flawed, the system may concentrate exposure rather than diversify it.
The third is jurisdictional and geopolitical exposure. As Industrial Info’s tracking of Latin American projects and Brazil’s long term investment forecasts suggest, a growing share of upstream and midstream activity is located in markets that combine substantial opportunity with higher cyber, governance and political risk. World Bank work on AI and jobs in Latin America and the Caribbean highlights that digital exclusion is particularly severe in poorer countries and among vulnerable groups, with up to seventeen million jobs theoretically able to benefit from generative AI but lacking basic tools to do so. This unevenness is mirrored at the corporate level. Multinational operators may design agentic AI systems and governance frameworks in line with advanced economy standards, but implementation runs through local networks where security, oversight and enforcement capacity may be lower. When an agentic system executes a sequence that crosses borders, any failure will be judged not only against local norms but also against the expectations of home-state regulators, trading counterparties and financiers.
For COOs and senior executives, the governance question is therefore how to integrate agentic AI into supply chain operations without creating an unmanaged layer of risk. The answer begins with recognition that these systems exist within, not outside, existing duties and regulatory expectations. In most jurisdictions, directors and officers remain bound by duties of care, skill and diligence which require them to understand material risks and to ensure that processes are in place to manage them. That obligation is not satisfied by delegating responsibility to vendors or internal technical teams and accepting opaque assurances about model performance. It is satisfied, if at all, by establishing governance structures in which agentic AI deployment is mapped, monitored and accountable.
Practically, this means that executives need to demand a baseline of documentation and control before authorizing agentic AI in supply chain workflows. At a minimum, they need a clear articulation of what tasks an agent is permitted to perform, under what constraints, and with what escalation triggers. They need to know how data is sourced, curated and updated, including where data may incorporate sanctions, export-control and environmental compliance information. They need to understand where systems are hosted, how cross-border data transfers are managed in light of regimes such as GDPR and its analogues, and how vendor obligations align with the company’s own duties under data protection, financial regulation and critical infrastructure law. They also need to know how actions and decisions are logged in a form that can support explanation if regulators, auditors or courts ask how a particular sequence unfolded.
The World Bank’s findings on constrained AI benefits in Latin America and the Caribbean and the World Economic Forum’s analysis of cyber risk in critical infrastructure both point to a further requirement. Agentic AI systems must be designed and deployed with an awareness of local capacity and threat profiles. It is not enough to roll out a globally standardized agentic platform and assume that controls effective in Houston or Rotterdam will translate seamlessly to Guyana, Brazil or Trinidad. Governance needs to account for gaps in local digital infrastructure, differences in law enforcement engagement and the practical challenges of incident response when supply chain nodes span multiple jurisdictions.
None of this implies that COOs should resist agentic AI in principle. The efficiency and resilience benefits documented in the oil and gas context are real, and competitors will pursue them. It does mean that executives who treat agentic systems as a technical upgrade, rather than as a reconfiguration of decision-making and accountability, are likely to find themselves exposed in ways that are difficult to defend after the fact. The standard that will be applied, whether by regulators, investors or courts, will not be “did you use agentic AI” but “did you use it in a way that was consistent with your governance obligations and with the foreseeable risks in your supply chain.”
Sources & Further Reading
Boston Consulting Group, “The AI-First Future of Oil and Gas Companies” (2025).
McKinsey & Company, “The State of AI: Global Survey 2025.”
World Bank, “Quantifying the Jobs Potential of AI in Latin America and the Caribbean” (2025).
World Bank, “Transforming Jobs in Latin America and the Caribbean” (2024).[blogs.worldbank]
Industrial Info Resources, “Latin America Oil Poised to Benefit from Middle East Disruptions” (2026).[industrialinfo]
Industrial Info Resources, “Brazil Oil and Gas Investments Forecasted to Reach $83.5 Billion by 2032” (2026).[industrialinfo]
World Economic Forum, “Global Cybersecurity Outlook 2025.”
