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TRUVACE RECORD VERSION
record: TRV-2026-1101
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-15T14:19:23.768301Z
status: published
lens: g_space
sector: business
headline: Artificial intelligence ‐ driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals
dek: 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…
gain_title: Organizations integrating AI can advance sustainability and contribute to UN Sustainable Development Goals by aligning strategy, infrastructure and continuous improvement to produce social, environmental and economic benefits.
problem_title: (none)
trace_subject: (none)
gain_reading: Organizations integrating AI can advance sustainability and contribute to UN Sustainable Development Goals by aligning strategy, infrastructure and continuous improvement to produce social, environmental and economic benefits.
gain_evidence: drive positive social, environmental, and economic outcomes
problem_reading: (none)
problem_evidence: (none)
quick_read: This peer-reviewed systematic review examined 57 articles on artificial intelligence and sustainable development, finding that contributions cluster in organizational integration, technical algorithm development, and internal processing changes. It proposes a conceptual model for organizations to incorporate AI into sustainability efforts.

The framework matters because it links AI adoption to social, environmental and economic outcomes tied to the UN Sustainable Development Goals, while highlighting implementation barriers and the need for business model change. As of the 2023 publication date, the model remains conceptual and context-dependent rather than an empirically tested intervention.
limitation: Conceptual model is derived from a literature review of 57 articles and has not been empirically validated, requiring adaptation to individual organizational contexts to be relevant and effective.
tag: Evidence-backed gain
key_points: Systematic review of 57 articles identified three concentration areas for AI's sustainability contribution: organizational, technical, and processing aspects. | Organizational aspect covers integration of AI in companies and industries and relationships between companies, partners, and customers. | Technical aspect covers development of AI algorithms that address global challenges and support stability and development in society. | Processing aspect covers internal transformation of companies, their business models, and strategies in response to AI integration.
rundown: The review was conducted in two stages and analyzed 57 articles, categorizing findings into organizational integration, technical algorithm development, and processing-level business transformation.

Authors propose a framework that includes strategic alignment, infrastructure development, change management, and continuous improvement as essential elements for leveraging AI toward sustainability.
sources:
- peer_reviewed | Sustainable Development | https://doi.org/10.1002/sd.2773 | 2023-10-06
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