Artificial Intelligence-Based Knowledge Management Implementation in Operational Management: A Case Study of PT Blue Bird Tbk
DOI:
https://doi.org/10.35870/ijmsit.v6i2.8393Keywords:
AI-Driven Knowledge Management, Operational Management, Case Study, SECI ModelAbstract
As artificial intelligence (AI) becomes embedded in organizations' operational knowledge management amid accelerating digital transformation, empirical evidence on how AI-Driven Knowledge Management actually functions in practice remains limited, particularly within the transportation service sector. This study analyzes the implementation of AI-Driven Knowledge Management in supporting process improvement and data-driven decision support in the operational management of PT Blue Bird Tbk during 2020-2025, grounded in the Knowledge-Based View (Grant, 1996) and the SECI model (Nonaka & Takeuchi, 1995). It employs a single descriptive qualitative case study design (Yin, 2018) structured in two sequential phases: an integrative literature review to build the conceptual framework, followed by analysis of 13 secondary documents (annual and sustainability reports 2020-2025, academic publications, media coverage) and confirmatory interviews with four informants (two Regional Managers, two Operations Managers). NVivo 12 Pro was used to analyze the data, yielding 8 themes and 162 references with an inter-rater reliability of Cohen's Kappa κ = 0.85. Three concrete contributions emerge from the findings: data-driven decision-making across all managerial levels, improved fleet-assignment processes through digital dispatch, and faster information dissemination across organizational levels. Yet the AI-Driven Knowledge Management theme received zero coding across all four interview transcripts despite having 7 references from secondary documents, and SECI-cycle analysis identified two breakpoints at the externalization and combination stages. This study proposes the concept of an automation shortcut, a pattern in which AI generates operational efficiency through direct automation without first strengthening the organization's knowledge cycle, diverging from assumptions commonly held in prior literature.
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