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Gelashvili-Luik, T., Vihma, P., Pappel, I. & Ferreira, F. (N/A). AI-augmented knowledge management in Fintech: Dynamic capabilities for strategic decision-making in complex and uncertain environments. Journal of Modelling in Management. 1-20

T. Gelashvili-Luik et al.,  "AI-augmented knowledge management in Fintech: Dynamic capabilities for strategic decision-making in complex and uncertain environments", in Journal of Modelling in Management, pp. 1-20, N/A

@article{gelashvili-luikN/A_1779603128841,
	author = "Gelashvili-Luik, T. and Vihma, P. and Pappel, I. and Ferreira, F.",
	title = "AI-augmented knowledge management in Fintech: Dynamic capabilities for strategic decision-making in complex and uncertain environments",
	journal = "Journal of Modelling in Management",
	year = "N/A",
	volume = "",
	number = "",
	doi = "10.1108/JM2-09-2025-0483",
	pages = "1-20",
	url = "https://www.emerald.com/jm2/article/doi/10.1108/JM2-09-2025-0483/1366739/AI-augmented-knowledge-management-in-Fintech"
}

TY  - JOUR
TI  - AI-augmented knowledge management in Fintech: Dynamic capabilities for strategic decision-making in complex and uncertain environments
T2  - Journal of Modelling in Management
AU  - Gelashvili-Luik, T.
AU  - Vihma, P.
AU  - Pappel, I.
AU  - Ferreira, F.
PY  - N/A
SP  - 1-20
SN  - 1746-5664
DO  - 10.1108/JM2-09-2025-0483
UR  - https://www.emerald.com/jm2/article/doi/10.1108/JM2-09-2025-0483/1366739/AI-augmented-knowledge-management-in-Fintech
AB  - Purpose – This study aims to investigate how artificial intelligence (AI) shapes knowledge management (KM) practices in FinTech and how these changes influence human judgement in strategic decision-making. It responds to the need for clearer understanding of how dynamic capabilities develop when AI is embedded in knowledge-intensive work. Design/methodology/approach – This research draws on a qualitative case study of a global FinTech organisation. Data were gathered from ten semi-structured interviews with managers, KM specialists and operational staff, supported by internal documents. Thematic analysis was guided by the dynamic capabilities framework (DCF).  
Findings – This study shows that AI-enabled KM develops through recursive and overlapping capability cycles, rather than linear stages. Three mechanisms support this process: knowledge trust and cross-functional alignment operate as ongoing preconditions for reliable AI use; mediation roles, such as Business Intelligence teams, link technical outputs with operational interpretation; and AI can ease cognitive load and improve efficiency but still requires active human judgement. These mechanisms highlight both the benefits of AI augmentation and the risks of over-reliance if knowledge or oversight structures lag behind. 
Research limitations/implications – As a single-case study, the findings reflect one organisational context 
and a specific moment in time. Future research should explore how these mechanisms operate across sectors and regulatory settings. 
Originality/value – This research extends the DCF by identifying how AI changes the microfoundations of sensing, seizing and transforming. It clarifies the role of alignment and mediation as enabling capabilities and demonstrates how KM evolves from maintaining static repositories to supporting continuous interpretation and organisational adaptability. 
ER  -