Ai For Utilities Requires Capability And Control

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Oct 15, 2026

6 minute read

AI for utilities requires capability and control

Energy and utility companies are increasingly using AI to transform many aspects of their operations. Common use cases include strengthening cyber and infrastructure defenses, making engineering knowledge easier to find, and reducing the time that employees spend assembling information. Realizing this value requires the same discipline that utilities apply to other consequential technologies, including processes to define operating boundaries, validate performance, and ensure accountability.

For an organization responsible for critical energy infrastructure, model capability is only one part of the purchasing decision. Where AI runs, what information it can access, and what authority it receives determine which workloads a utility can responsibly put into production.

Recent discussions between frontier AI labs and utility executives illustrate an important tension facing critical-infrastructure operators.

The question for utilities is no longer simply: “What can this model do?”

It is also: Where does it run? What information can it access? What actions can it take? How is it monitored? And who remains in control?

Cohere North is designed to address these questions and much more.

In this post, we’ll take a look at key considerations for utility leaders and how North can meet a utility organization’s security, data, and operational requirements. Recent AI developments raise the stakes OpenAI’s recent engagement with utilities reflects the growing role of AI in critical-infrastructure cybersecurity. Its September Daybreak announcement describes a gathering of utility leaders whose organizations cover 40 states and Washington, D.C. and collectively serve more than half the US population. The initiative aims to help utilities identify vulnerabilities, analyze suspicious activity, and develop and test fixes.

The opportunity comes with a clear reminder about control. In OpenAI’s August incident report , the company described how models undergoing internal cybersecurity evaluations in July circumvented isolation controls, communicated through unauthorized channels, and compromised parts of its research infrastructure and Hugging Face’s systems. The evaluations used reduced safeguards, and the disclosure does not establish that utility grid-control systems were compromised. OpenAI described subsequent measures to strengthen isolation, monitoring, and alignment.

For utility leaders, these developments underscore a practical point: greater AI capability must be accompanied by secure deployment boundaries. Five questions every AI proposal should answer What measurable problem will this solve? Define the workflow, establish a baseline, and evaluate output quality alongside speed. What can the agent actually do? Specify which systems the agent can access, what actions it can take, and which human approvals are required. What information leaves our environment? Identify every external dependency. Follow the data through the entire workflow, including document processing, model inference, storage, logs, integrations, telemetry, and support access. How will recommendations be validated? Require traceability of supporting source material and associated citations, along with human-in-the-loop evaluations by subject matter experts (SMEs). What happens after the pilot? Establish a licensing agreement that provides commercial predictability throughout the path to full production and mitigates token shock.

A different approach: Bring AI to the utility’s data Cohere’s approach to enterprise AI is to deliver the models to the data that utilities need to protect. Rather than requiring organizations to redesign sensitive workflows around a public AI service, Cohere’s North platform can be deployed within the operator’s secure perimeter, which can fundamentally expand where utilities are able to use AI.

North creates a path for utilities to work with engineering documents, asset histories, emergency procedures, customer information, and internal operating knowledge within their controlled environments without the potential for data egress. This allows operators to select an architecture that fits the sensitivity of the workload and its requirements for control over data and infrastructure .

The following considerations can help utilities ensure a successful rollout of AI across their organization. Keep...