Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases

2026-02-10

AI Summary

This study addresses the widespread lack of understanding of generative AI among energy sector employees, which hinders the identification of viable application entry points and implementation pathways. Through semi-structured interviews, internal document analysis, and on-site observations, the research systematically identifies five high-potential application scenarios: report generation, forecasting, data processing, equipment maintenance, and anomaly detection. It proposes a gradual deployment approach for generative AI that aligns with existing workflows. This work establishes the first practical framework for implementing generative AI in the energy industry, offering reusable design strategies for LLM-based agent workflows and providing both theoretical grounding and actionable guidance for AI-driven transformation in heavy industrial sectors.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand AI adoption and identify areas where AI and LLMs-based agentic workflows could assist daily activities. Data was collected in four weeks through sixteen semi-structured interviews across nine departments, supported by internal documents and researcher observations. The analysis identified areas where employees positioned AI as useful, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection. Participants also described how GenAI and LLM-based tools could be introduced through incremental steps that align with existing workflows. The study provides an overview view of AI adoption in the energy sector and offers a structured basis for identifying entry points for practical implementation and comparative research across industries.

Problem

• Generative AI
• Energy sector
• AI adoption
• LLM-based workflows
• Operational support

Innovation

• Generative AI
• LLM-based agentic workflows
• AI adoption
• energy sector
• incremental implementation