AI-Powered Curricula Selection: A Neural Network Approach Suited for Small and Medium Companies

Marco, Marco De; Fantozzi, Paolo; Laura, Luigi; Miloso, Antonio

doi:10.1007/978-3-030-87842-9_2

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most applications are aimed at large companies and are tailored to their structures. Although there are now initial approaches for SMEs (Marco et al 2021;Singh and Desai 2022), these are still in the clear minority. This is particularly contradictory to the fact that there are significantly more companies falling under the SME definition than there are larger enterprises (Kaymakci et al 2022;Perera and Chand 2015).…”

Section: Challenges Regarding Ai Implementation In Smesmentioning

confidence: 99%

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

Oldemeyer,

Jede,

Teuteberg

2024

Manag Rev Q

View full text Add to dashboard Cite

While the topic of artificial intelligence (AI) in multinational enterprises has been receiving attention for some time, small and medium enterprises (SMEs) have recently begun to recognize the potential of this new technology. However, the focus of previous research and AI applications has therefore mostly been on large enterprises. This poses a particular issue, as the vastly different starting conditions of various company sizes, such as data availability, play a central role in the context of AI. For this reason, our systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size. This allowed us to identify various business activities that have been scarcely considered. Simultaneously, it led to the discovery of a total of 27 different challenges perceived by SMEs in the adoption of AI. This enables SMEs to apply the identified challenges to their own AI projects in advance, preventing the oversight of any potential obstacles or risks. The lack of knowledge, costs, and inadequate infrastructure are perceived as the most common barriers to implementation, addressing social, economic, and technological aspects in particular. This illustrates the need for a wide range of support for SMEs regarding an AI introduction, which covers various subject areas, like funding and advice, and differentiates between company sizes.

show abstract