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This study explores the integration of artificial intelligence in knowledge management to bolster sustainable business practices.

May 31, 2026Open Access

AI-driven knowledge management for sustainable businesses: a comprehensive analysis

Key Points

This study explores the integration of artificial intelligence in knowledge management to bolster sustainable business practices.
Mixed-methods systematic review of 80 articles from Scopus and Web of Science (2004–2025)
Descriptive bibliometrics and R-based science mapping were employed
Qualitative content analysis identified core themes and frameworks.
Post-2019 growth in AI-KM sustainability literature highlighted significant trends
Identified KM, AI, and sustainability as central themes, with green innovation and circular economy emerging as critical areas
Six core themes formed a basis for understanding technological, strategic, and ecological aspects of AI-KM integration.

Abstract

Purpose To address mounting environmental, social and economic pressures, this study aims to explore how artificial intelligence (AI) enhances knowledge management (KM) to support sustainable business transformation, particularly within the emerging context of Industry 5.0. Design/methodology/approach Following preferred reporting items for systematic reviews and meta-analyses guidelines, this mixed-methods systematic review analyzes 80 articles (2004–2025) from Scopus and Web of Science. The methodology combines descriptive bibliometrics (e.g. publication trends) and R-based science mapping (e.g. thematic networks) with qualitative content analysis to decode the AI–KM–sustainability nexus. Findings Bibliometrics reveal exponential post-2019 growth, Asian geographic dominance and reliance on cross-sectional surveys. Thematic mapping identifies KM, AI and sustainability as anchor motor themes, with green innovation and circular economy as a critical emergent frontier. Qualitatively, six core themes form a tripartite framework: technological foundations, organizational strategy and ecological outcomes. It demonstrates how AI–KM integration enhances dynamic capabilities, while highlighting critical sociocultural and ethical governance barriers. Practical implications This study provides managers with actionable maturity models and decision-support frameworks to operationalize corporate sustainability strategies and navigate complex digital integrations. Social implications Within the Industry 5.0 context, this research emphasizes the need for human–AI collaborative ecosystems, advocating for technological democratization, ethical governance and inclusive knowledge sharing. Originality/value By positioning the knowledge-based view as the primary analytical anchor, supplemented by dynamic capabilities and socio-technical systems theory, this study establishes a comprehensive framework linking AI-driven KM with sustainability. It proposes a targeted research agenda prioritizing longitudinal designs, global inclusivity and ethical accountability.

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