AI Integration in SME Management Processes and the Potential of SAAS-Based Applications

Abstract

Purpose – This study aims to examine the integration of artificial intelligence (AI) into the management processes of Small and Medium-sized Enterprises (SMEs), identifying challenges encountered in this process and evaluating the potential solutions offered by the Software as a Service (SaaS) model. Methodology – The research was conducted using a qualitative case study approach. Semi-structured interviews were conducted with 22 industry experts and 10 SME representatives to collect data, which was subsequently analyzed using content analysis. Findings – Findings indicate that businesses are at different stages of AI adoption; some are in the planning phase, while others have previously implemented AI applications. AI is predominantly used in content creation, advertising, and product recommendation processes. Key challenges in the integration process include resource constraints, workforce limitations, and infrastructure costs. However, SaaS-based AI solutions have been found to offer cost advantages, reduce the need for expertise, and provide flexible usage. Nevertheless, the SaaS model also presents limitations such as data security concerns, customization challenges, and system dependency. Discussion – This study examines the current state of AI integration in SMEs, the challenges they face, and the advantages and limitations of SaaS-based AI solutions. Although SMEs are inclined toward AI technologies due to efficiency and cost benefits, the integration process is hindered by a lack of technical expertise, financial constraints, and security concerns. While SaaS-based AI applications offer advantages such as low cost, ease of maintenance, and flexibility, they also pose risks related to data security, service interruptions, and dependency. Data management is a critical factor in the integration process, requiring security, organization, and an adequate amount of data. For a successful transition, the formation of interdisciplinary teams and the strengthening of technical infrastructure are recommended.