Work

AI-driven organizational adaptation in non-renewable energy sector: A systematic review and theoretical framework development

Year: 2026

Type: review

Abstract: The non-renewable energy sector is undergoing profound transformation driven by the diffusion of artificial intelligence (AI) technologies across upstream, midstream, downstream, and strategic decision-making processes. However, the sustainability implications of AI adoption remain theoretically fragmented and empirically inconsistent. This study employs a systematic literature review to analyze AI-driven organizational adaptation mechanisms in the non-renewable energy sector. Using the PRISMA framework, we systematically analyzed 39 publications from 2018 to 2025. Key findings reveal AI disruption operates at operational, analytical, and strategic levels; while organizational adaptation serves as a mediating mechanism linking the AI adoption to sustainability outcomes. Furthermore, the study conceptualizes adaptation as a form of configurational adaptation, through strategic alignment, technical readiness, and human transformation dimensions. Building on these insights, the study advances theory by proposing the integrative AI-DRIVEN ORGANIZATIONAL ADAPTATION MODEL (AD-OAM) framework and provides actionable, sector-specific guidance for digital transformation in the non-renewable energy sector.

Source: Energy Strategy Reviews

DOI: 10.1016/j.esr.2026.102299

Authors: Abdirahman Mayow Abdi, Xiuquan Deng, Abdulkadir Jeilani Mohamud

Institutions: Beihang University, Somali National University

Language: English

Topic: Impact of AI and Big Data on Business and Society

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $4,180