Towards wider adoption of generative artificial intelligence in the energy sector - A qualitative study on how organisations in the energy sector could act to enable generative artificial intelligence adoption, and what factors enable and hinder this phenomenon

Abstract

Purpose: Generative Artificial Intelligence (GenAI) is increasingly seen as a strategic resource in both research and business. At the same time, previous studies show that there are significant barriers to its adoption within organisations, including those related to competence, culture and technological trust. The purpose of this study is to illustrate how organisations in the energy sector could act to enable wider GenAI adoption. To address this purpose, the study investigates factors that are perceived to enable and hinder the adoption of the technology at E.ON, one of the world's largest private energy organisations. Theoretical framework: The study is based on two complementary theoretical perspectives: Technology Acceptance Model (TAM) and Dynamic Capabilities Theory (DCT). TAM sheds light on the individual's experience of GenAI, focusing on how useful and easy to use the technology is perceived to be. DCT complements this by analysing the organisation's ability to identify, respond and adapt to technological change, providing a holistic view of the complexity of adoption. Methodology: The study follows a qualitative research approach and is based on 14 qualitative interviews with managers within the energy organisations E.ON. The empirical material was analysed thematically based on both the themes of the interview guide and an abductive approach. Three main themes emerged from the analysis: Incentives and structure around GenAI use, Communication about value-creating activities, and Knowledge through training and collaboration. Key findings: The study shows that the adoption of GenAI in the energy sector is influenced by a complex interplay between individual perceptions of the technology and organisational capacity for change. Clear structures, leadership support and cultural legitimacy appear to be crucial for the integration of the technology into the organisation. The findings also highlight how the level of experience of users, both early and late adopters, influences the need for support, guidance and differentiated interventions. A key conclusion is that organisations in the energy sector need to work on both strategic clarity and organisational culture to enable wider and sustainable adoption of GenAI.