AI Adaption within SMEs: Analysis of Impedances and Suggested Approaches

Autor/innen

  • Celeste Chudyk Institute of Machine Learning and Analytics, Offenburg University of Applied Sciences
  • Damian Läufer Institute of Machine Learning and Analytics, Offenburg University of Applied Sciences
  • Simone Braun Institute of Machine Learning and Analytics, Offenburg University of Applied Sciences https://orcid.org/0000-0002-4825-1648
  • Tobias Hagen IMLA https://orcid.org/0000-0003-2594-4932

Schlagworte:

AI, SME

Abstract

In this paper, we present the main obstacles faced by small and medium-sized enterprises (SMEs) when implementing artificial intelligence (AI), and suggest a novel “plug and play” guided approach for further integration. In order to identify the relevant barriers, we first compile results from recent literature reviews that address challenges specific to SMEs and AI. Then, based on the AI maturity model for SMEs by Schuster et al. [1], we analyze the current status of AI in local German SMEs with which we have worked in the context of the “KI-Labor Südbaden”[2] project. Based on the results of the analysis, we detail a structured approach utilizing pre-identified successful AI implementations as the basis for further technological development. By structuring their AI integration on known successful use cases, SMEs have the chance to leapfrog their AI development and remain competitive in today’s landscape.

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Veröffentlicht

2024-10-29