Identifying and Prioritizing Generative AI Use Cases in an Organization: An Industrial Case Study
For organizations in the energy sector, this study offers a structured approach to identifying and prioritizing generative AI use cases, but it is an incremental contribution based on a single case study.
This study investigates how employees in an energy company understand AI adoption and identifies areas where generative AI and LLM-based agentic workflows could assist daily activities, including reporting, forecasting, data handling, maintenance, and anomaly detection. The analysis provides a structured basis for identifying entry points for practical implementation.
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.