Authors

Nwankwe, Dimie

Department of Computer Science, Rivers State University, Rivers State, Port Harcourt, Nigeria

V.I.E Anireh

Department of Computer Science, Rivers State University, Rivers State, Port Harcourt, Nigeria

Boye, Aziboledia F.

Department of Computer Science, Informatics & Cybersecurity, Otuoke, Bayelsa State, Nigeria

Abstract

Artificial intelligence (AI) and knowledge management (KM) have converged more rapidly in the first quarter of the twenty-first century than at any time since both fields first emerged. Early rule-based expert systems sought to codify scarce expertise; machine learning then automated the classification, search and recommendation of organisational knowledge; and large language models and other generative AI systems have begun to build, summarise and apply knowledge in natural language at scale since 2022. This paper reviews the literature on AI-enabled KM from scientific, technical and policy perspectives, mainly from 2000 to 2026. This review traces the development of AI for KM in three stages, examines how it has been applied to each of these processes, and critically analyses the advantages and disadvantages in literature. AI has shown that it can significantly increase employees’ productivity (especially among those with less experience in the field), bring knowledge back into the system, help re-use knowledge that would otherwise be lost, and lead to faster decisions. It also shows that AI brings serious risks: fake or unverified outputs, poor understanding of tacit and contextual knowledge, embedded bias, opacity, privacy and security exposure, over-reliance and deskilling, homogenisation of organisational knowledge, workforce displacement, environmental costs and widening digital divides - especially in developing countries. The paper argues that AI is a net value when KM is being done, but not just the technology, but how organisations make decisions about it. It proposes a human-centred AI–KM integration framework with five layers (strategy, knowledge and data foundations, technology architecture, people and culture, governance and assurance), identifies research gaps and suggests methods for practitioners, policymakers and researchers.

Keywords

Artificial intelligence Knowledge management Large language models Knowledge sharing AI governance Human–AI collaboration Digital transformation Developing economies

Citation of this Article

Nwankwe, Dimie, V.I.E Anireh, & Boye, Aziboledia F. (2026). A Review of Artificial Intelligence and Knowledge Management in the 21st Century: Pros and Cons. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(10), 33-49. Article DOI: https://doi.org/10.47001/JAIET/2026.310005 

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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