20.500.12556/DKUM-96232 Artificial intelligence models and employee lifecycle management a systematic literature review Modeli umetne inteligence in upravljanje kariere zaposlenih sistematičen pregled literature Background and purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL management. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AI algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL management problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary. Ozadje/namen: Uporaba modelov umetne inteligence (AI) za odločanje na podlagi podatkov v različnih fazah upravljanja kariere zaposlenih (EL) narašča. Vendar pa ni celovite študije, ki bi obravnavala prispevke umetne inteligence pri upravljanju EL. Zato je bil glavni cilj te študije osvetliti to teoretično vrzel in ugotoviti prispevek modelov AI k upravljanju EL. Metode: Ta študija je uporabila metodo PRISMA, model sistematičnega pregleda literature, da bi zagotovila dostop do največjega števila publikacij, povezanih z obravnavano temo. Rezultati modela PRISMA so pripeljali do identifikacije 23 povezanih člankov, ugotovitve te študije pa so bile predstavljene na podlagi analize teh člankov. Rezultati: Algoritmi AI so bili uporabljeni v vseh fazah upravljanja EL. Pokazalo se je tudi tudi, da so algoritmi Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree in Algoritmi umetne nevronske mreže boljši od drugih algoritmov in so bili najpogosteje uporabljeni v obravnavanih študijah. Zaključek: Čeprav se uporaba modelov umetne inteligence pri reševanju problemov upravljanja EL povečuje, so raziskave na to temo še vedno v povojih in potrebnih je več raziskav. management vodenje umetna inteligenca kariera true true true Angleški jezik Slovenski jezik Neznano 2025-12-11 11:32:37 2025-12-11 11:32:38 2025-12-12 03:09:00 0000-00-00 00:00:00 2022 0 0 str. 181-198 no. 3 Vol. 55 2022 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2022-01-01 005.95/.96:004.89 162400771 10.2478/orga-2022-0012 1318-5454 https://organizacija.fov.um.si/index.php/organizacija/article/view/920 1 b8d93eb4-d67c-11f0-b416-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=201343 RAZ_Nosratabadi_Saeed_2022.pdf RAZ_Nosratabadi_Saeed_2022.pdf 1 073A86C1364416D2827BB284BDAF39BC 8e8da45d2b3899ae600a987157a3f25de8629af9779c8e18f072284a8914211f f36569a9-d67c-11f0-b416-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=201344 Fakulteta za organizacijske vede Organizacija 0 0 0