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Influence of Artificial Intelligence in Organisational Knowledge Management Initiatives
Thesis   Open access

Influence of Artificial Intelligence in Organisational Knowledge Management Initiatives

Hasini Balage
Southern Cross University
Masters by Thesis, Southern Cross University
2026
DOI:
https://doi.org/10.25918/thesis.598
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Abstract

Artificial Intelligence Knowledge Knowledge Management AI-Integration Grounded Theory Cross-case analysis
Artificial Intelligence (AI) is reshaping industries, geographies, and socio-political contexts by influencing creativity, productivity, and efficiency. Within organisations, however, the integration of AI extends beyond a mere technological implementation challenge; it represents a fundamental reconfiguration of knowledge practices. This study examines AI integration through the lens of knowledge management (KM), recognising that both AI and KM seek to enhance knowledge creation, capture, sharing, and application to improve decision-making, innovation, and organisational performance. Despite decades of investment in KM initiatives, incorporating AI into existing knowledge infrastructures remains complex, requiring careful consideration of organisational knowledge assets, processes, and the balance between tacit and explicit forms of knowledge. The study addresses the research question: How do organisations usefully integrate Artificial Intelligence into operational practices, in light of the existing Knowledge Management practices? Adopting a multi-method qualitative research design, the study began with an empirical phase that involved an inductive analysis of 100 successful AI implementation cases, employing the Gioia methodology. The research identifies the process of AI–KM integration. Findings from cross-case analysis reveal four distinct strategic archetypes. These archetypes are shaped by three critical organisational choices: (1) the nature of knowledge extraction (vertical vs. horizontal), (2) the workflow configuration (automation vs. augmentation), and (3) consolidate knowledge into stable artefacts (verified vs non-verified). The resulting archetypes: (i) Human-Verified Vertical Augmentation, (ii) Human-Verified Horizontal Augmentation, (iii) Human-Verified Horizontal Automation, and (iv) Non-Human-Verified Horizontal Automation, offer a novel framework for understanding how organisations align AI capabilities with knowledge processes. These archetypes were further validated through in-depth field data collected from industry practitioners via semi-structured interviews.

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