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

TL;DR

This study explores how employees in an energy company perceive and implement generative AI, identifying practical use cases and challenges for integrating AI tools into daily workflows.

Contribution

It provides empirical insights into AI adoption in the energy sector, highlighting specific use cases and strategies for incremental implementation.

Findings

AI is useful for reporting, forecasting, and anomaly detection.

Employees prefer incremental AI integration aligned with existing workflows.

The study offers a structured framework for practical AI adoption in energy companies.

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.