The future of artificial intelligence (AI) in knowledge management (KM) is expected to redefine how organisations create, share, and apply knowledge. This chapter examines key directions for AI-enabled KM, including hyper-personalisation of knowledge delivery, predictive analytics for decision support, conversational AI for seamless interaction, and immersive technologies such as augmented and virtual reality for enhanced transfer. It also highlights the emergence of distributed and federated learning (FL) models, enabling more secure and collaborative knowledge exchange. Alongside these opportunities, critical challenges related to ethics, transparency, data privacy, algorithmic bias, and trust are emphasised as determining factors for sustainable adoption. The discussion suggests that KM’s future will be shaped by a hybrid model where human judgement and machine intelligence complement each other, ensuring knowledge remains context-sensitive, adaptive, and ethically managed. By outlining these opportunities and risks, the chapter provides a roadmap for organisations aiming to leverage AI for resilience, innovation, and competitiveness in the digital era.
Rezaei, M., Future in Artificial Intelligence and Knowledge Management, in Rezaei, M. (ed.), Knowledge Management in the AI Era: Evolution, Challenges, and Strategic Adaptation,, Emerald Group Publishing Ltd., Leeds 2026: 131- 174. 10.1108/978-1-80686-251-120261006 [https://hdl.handle.net/10807/332985]
Future in Artificial Intelligence and Knowledge Management
Rezaei, Mojtaba
Primo
2026
Abstract
The future of artificial intelligence (AI) in knowledge management (KM) is expected to redefine how organisations create, share, and apply knowledge. This chapter examines key directions for AI-enabled KM, including hyper-personalisation of knowledge delivery, predictive analytics for decision support, conversational AI for seamless interaction, and immersive technologies such as augmented and virtual reality for enhanced transfer. It also highlights the emergence of distributed and federated learning (FL) models, enabling more secure and collaborative knowledge exchange. Alongside these opportunities, critical challenges related to ethics, transparency, data privacy, algorithmic bias, and trust are emphasised as determining factors for sustainable adoption. The discussion suggests that KM’s future will be shaped by a hybrid model where human judgement and machine intelligence complement each other, ensuring knowledge remains context-sensitive, adaptive, and ethically managed. By outlining these opportunities and risks, the chapter provides a roadmap for organisations aiming to leverage AI for resilience, innovation, and competitiveness in the digital era.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.



