ARTIFICIAL INTELLIGENCE FOR SMALL AND MEDIUM-SIZED ENTERPRISES

IDENTIFYING KEY APPLICATIONS AND CHALLENGES

Abstract

Purpose. Small and medium-sized enterprises (SMEs) play a crucial role in the global economy, yet they often face significant challenges in implementing cutting-edge technologies such as artificial intelligence (AI) due to limited resources and expertise. This study aims to address this gap by identifying the most important applications of AI for SMEs, exploring their benefits and challenges and assessing their impact on business processes.

Design/methodology/approach. A quantitative research approach was employed, including an online survey distributed through various social media platforms. The survey, which was conducted in May 2023, used a standardized questionnaire with closed-ended questions. The survey data were analysed using statistical methods to identify patterns and trends in AI adoption among German SMEs, with a particular focus on their experiences, challenges, and perceived benefits of AI implementation.

Findings. The results of this study highlight the most important AI applications for SMEs in various professions and business activities, including virtual assistants, recommendation systems, and machine learning. The study also highlights the benefits of these applications, such as improved efficiency, productivity, and decision-making, as well as the challenges they present, such as privacy concerns and the need for specialized skills.

Research limitations/implications. The study is limited by the sample size and the self-reported data collected through an online survey. The findings may not be generalizable to all industries and regions. The implications of this study are that SMEs need to carefully consider the potential benefits and challenges of AI before implementing it in their processes.

Originality/value. This study provides a comprehensive overview of the most important AI applications for SMEs and their impact on business processes. Thus, this research serves scientists as a theoretical basis for future research in the field of AI implementation in SMEs. Furthermore, the findings have practical implications for SMEs considering implementing AI in their operations.