Integrating ontology-driven knowledge management and explainable AI: a framework for cognitive and organizational intelligence in industry 4.0

Companies in Industry 4․0 face challenges in managing knowledge․ This research explores knowledge management (KM) architectures that use ontologies and explainable AI technologies as tools to support KM initiatives․ This study developed a framework for transforming tacit knowledge and adopting AI technologies for KM activities․ Using data collected over three years from 47 multinational companies and 12‚847 knowledge workers the paper proposes and tests a new Knowledge AI Synergy Index (KASI) using structural equation modeling․ Benefits included 43․7% faster knowledge searches‚ 37․2% faster decision making‚ and 52․6% greater sharing of knowledge across departments․ Using explainable AI helped reduce perceptions of system complexity by 68․4% and increase user trust in the system․ This study found a link between ontological knowledge and AI to support learning․ Despite limitations‚ findings can help CKOs and other executives implement AI-based KM systems․ This is the first study to explore the use of explainable AI principles in organizational KM․

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Publication Details

Journal
Knowledge Management Research & Practice
Published
2026-10-09
DOI
https://doi.org/10.1080/14778238.2026.2744669
Primary Topic
Knowledge Management and Technology
Type
article
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0.00
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article

Integrating ontology-driven knowledge management and explainable AI: a framework for cognitive and organizational intelligence in industry 4.0

Syed Raiyan Ghani
Knowledge Management Research & Practice
Knowledge Management and Technology
article

Integrating ontology-driven knowledge management and explainable AI: a framework for cognitive and organizational intelligence in industry 4.0

Syed Raiyan Ghani
article en

Abstract

Companies in Industry 4․0 face challenges in managing knowledge․ This research explores knowledge management (KM) architectures that use ontologies and explainable AI technologies as tools to support KM initiatives․ This study developed a framework for transforming tacit knowledge and adopting AI technologies for KM activities․ Using data collected over three years from 47 multinational companies and 12‚847 knowledge workers the paper proposes and tests a new Knowledge AI Synergy Index (KASI) using structural equation modeling․ Benefits included 43․7% faster knowledge searches‚ 37․2% faster decision making‚ and 52․6% greater sharing of knowledge across departments․ Using explainable AI helped reduce perceptions of system complexity by 68․4% and increase user trust in the system․ This study found a link between ontological knowledge and AI to support learning․ Despite limitations‚ findings can help CKOs and other executives implement AI-based KM systems․ This is the first study to explore the use of explainable AI principles in organizational KM․

Knowledge Management Research & Practice
Openalex Percentile: Top 10%
Knowledge Management and Technology
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Integrating ontology-driven knowledge management and explainable AI: a framework for cognitive and organizational intelligence in industry 4.0 — Syed Raiyan Ghani · Knowledge Management Research & Practice (2026) | TGRS Research Map | TGRS