Artificial intelligence ‐ driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals

Ignat Kulkov, Julia Kulkova, Rene Rohrbeck, Loick Menvielle, Valtteri Kaartemo and Hannu Makkonen

Sustainable Development, 2024, vol. 32, issue 3, 2253-2267

Abstract: This study presents a comprehensive literature review using a systematic approach to explore the role of artificial intelligence (AI) in promoting sustainable development in line with the United Nations Sustainable Development Goals (SDGs). The systematic review approach was applied to collect and analyze topics, and the literature search was conducted in two stages, encompassing 57 articles that met the research requirements. Our analysis reveals that AI's contribution to sustainability is concentrated within three key areas: organizational, technical, and processing aspects. The organizational aspect focuses on the integration of AI in companies and industries, addressing barriers to implementation and the relationship between companies, partners, and customers. The technical aspect highlights the development of AI algorithms that can address global challenges and contribute to the growth of stability and development in society. The processing aspect emphasizes the internal transformation of companies, their business models, and strategies in response to AI integration. Our proposed conceptual model outlines the essential elements organizations must consider when incorporating AI into their sustainability efforts, such as strategic alignment, infrastructure development, change management, and continuous improvement. By addressing these critical aspects, organizations can harness the potential of AI to drive positive social, environmental, and economic outcomes, ultimately contributing to the achievement of the SDGs. The model serves as a comprehensive framework for organizations seeking to leverage AI for sustainable development, but it should be adapted to individual contexts to ensure its relevance and effectiveness.

Date: 2024
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https://doi.org/10.1002/sd.2773

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Persistent link: https://EconPapers.repec.org/RePEc:wly:sustdv:v:32:y:2024:i:3:p:2253-2267

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