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.

翻译：各组织机构正在研究生成式人工智能如何支持其运营工作与决策流程。本研究调查了一家能源公司员工对人工智能采纳的理解，并识别了人工智能及基于大语言模型的智能工作流可协助日常工作的领域。研究通过为期四周的数据收集，对九个部门的十六名员工进行了半结构化访谈，并辅以内部文件与研究者观察记录。分析识别出员工认为人工智能具有应用价值的领域，包括报告撰写、预测分析、数据处理、维护相关任务及异常检测。参与者还描述了如何通过渐进式步骤引入生成式人工智能与基于大语言模型的工具，使其与现有工作流程相协调。本研究提供了能源领域人工智能采纳的概览，并为识别实际实施的切入点及开展跨行业比较研究提供了结构化基础。