Generative AI in the energy sector: From buzzword to bottom line

Leading up to OTD Energy in Stavanger on 15–16 October 2025, we are sharing insights from projects and processes that show how technology delivers operational value. The first article, "Our experience from the energy sector," can be found here.

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A practical approach for the energy industry

Everyone is talking about artificial intelligence (AI), but the path to implementing AI in a larger organization is often described as long, costly, and complex. Especially in an industry like the energy sector, where safety and precision are critical, the threshold for adopting new technology can feel high. But implementing generative AI in the energy industry does not have to involve a complete overhaul of IT systems. On the contrary, the first value-creating steps can be taken quickly and with low risk.

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Step 1: Forget AI, start with your data

The most common misconception about AI is that it is a magic box you can buy and switch on. In reality, the value of any AI solution is entirely dependent on the quality of the data it has access to. For companies in the energy industry, important sources may include:

• Technical manuals and procedures
• HSE documentation
• Historical project reports and lessons learned
• Maintenance logs
• Internal guidelines

The goal is not to build a massive data warehouse overnight. Instead, start with a selection of high-quality data in a format that AI can read. This ensures that the solution provides precise, relevant, and safe answers — based on your reality, not generic information from the internet.

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Step 2: Build an internal AI assistant, not a revolution

The most effective starting point for generative AI is often internal. Instead of aiming for a complex, externally facing service, start by solving a concrete bottleneck. One of the most valuable and low-threshold projects is to develop an internal AI assistant — a chat solution that gives employees quick and precise answers, based on the organization's own procedures, goals, and experiences. Imagine an engineer being able to ask: "What is the procedure for replacing valve X on compressor Y according to the latest maintenance manual?" and receiving a precise answer with a reference to the correct document.

The benefits are clear:

• Faster information flow: employees find answers in seconds, not hours.
• Increased safety: based on updated, approved sources.
• Democratization of knowledge: critical knowledge becomes accessible to everyone, not just the experts.

Such a solution works as a perfect pilot. It allows you to validate the technology in a controlled environment, demonstrate value to the organization, and build experience before potentially scaling up.

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How to get started, without major disruption

At Seven Peaks, our philosophy is to build on what already exists. We do not create new language models from scratch — we use the power of market-leading platforms such as Azure OpenAI and Amazon Bedrock. Our role is to tailor and integrate these services so that they work seamlessly with existing systems and data. This is the same approach we used when we helped a player in the circular economy identify how AI and automation could streamline processes. The industries are different, but the principle is the same: we analyze needs, identify low-hanging fruit, and build solutions that deliver fast and measurable value.

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Generative AI for energy: ready to use today

Generative AI is not the future — it is a tool you should be putting to use already today. By starting small and focusing on internal solutions, energy companies can reap benefits quickly, while simultaneously building a solid foundation for further innovation.

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Meet us at OTD Energy Stavanger 2025

Are you attending OTD Energy 2025 in Stavanger on 15–16 October 2025? Get in touch with us for a no-obligation conversation about what an internal AI assistant could look like for your company — and how we can help you make AI part of the bottom line, not just a buzzword.

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