Agility and Artificial Intelligence Adoption: Small vs. Large Enterprises

Rožman Maja (), Oreški Dijana (), Crnogaj Katja () and Tominc Polona ()
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Rožman Maja: University of Maribor, Faculty of Economics and Business, Razlagova 14, 2000 Maribor, Slovenia
Oreški Dijana: University of Zagreb, Faculty of organization and informatics Varaždin, Pavlinska 2, 42000 Varaždin, Croatia
Crnogaj Katja: University of Maribor, Faculty of Economics and Business, Razlagova 14, 2000 Maribor, Slovenia
Tominc Polona: University of Maribor, Faculty of Economics and Business, Razlagova 14, 2000 Maribor, Slovenia

Naše gospodarstvo/Our economy, 2023, vol. 69, issue 4, 26-37

Abstract: This article presents the findings of a survey conducted in Slovenia, encompassing a random sample of 275 enterprises, to analyze the factors influencing the transition to an agile approach, the AI-supported organizational culture, AI-enabled workload reduction, and AI-enabled performance enhancement in small and large enterprises. The study investigates whether there are statistically significant differences between small and large enterprises in Slovenia regarding these aspects. These findings provide valuable insights into the distinct perspectives and priorities of small and large enterprises in Slovenia regarding agility and the adoption of AI technologies. The results highlight areas where small businesses may need additional support or targeted strategies to fully leverage the benefits of agility and AI. Policymakers and industry leaders can utilize these findings to promote tailored approaches that enhance agility and facilitate effective AI integration in both small and large enterprises, ultimately contributing to the growth and competitiveness of the Slovenian business landscape.

Keywords: Firm performance; IT Management; Agility; Artificial intelligence; Slovenia (search for similar items in EconPapers)
JEL-codes: L25 M15 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ngooec:v:69:y:2023:i:4:p:26-37:n:5

DOI: 10.2478/ngoe-2023-0021

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