Abstract

This research sheds light on how Small and Medium Sized Enterprises (SMEs) adopt and implement Artificial Intelligence (AI). SMEs are vital engines of innovation and employment. However, they currently face numerous challenges including skills gaps, talent shortages, economic uncertainty, trade tensions, etc. that impact their performance, growth and overall sustainability. Embracing digital technologies such as AI can help SMEs address these challenges. Despite this potential, there is limited understanding of how SMEs adopt and implement AI in their processes particularly in areas such as employment and innovation. For instance, it is unclear how AI-powered hiring tools might improve recruitment or how AI can support market research, idea generation, and automation in SMEs. It is particularly important to investigate whether AI adoption in these areas contributes to improve overall performance and innovation outcomes. In particular, examining whether the use of AI in recruitment enhances innovation within SMEs would offer valuable insight. There is ongoing debate about the mixed results of AI adoption, stemming from issues such as algorithmic bias, inaccuracies, and hallucinations in AI-generated outputs. Managers and employees in SMEs may or may not be fully aware of these limitations. Enhancing awareness and knowledge about AI’s capabilities and risks can support more effective and responsible adoption. Moreover, individual characteristics of managers and employees may influence their awareness, attitudes, and behaviors toward AI. Therefore, examining these characteristics can deepen our understanding of AI adoption within SMEs. Overall, the primary objective of this research is to investigate the factors that influence the adoption and implementation of AI technologies and applications by SMEs. The study aims to identify major drivers and challenges associated with AI use particularly in the areas of employment and innovation. It will also assess whether and how the use of AI contributes to improved performance and innovation outcomes. Furthermore, the research will examine firm-level and individual level characteristics including those of managers and employees that may influence AI adoption and use. By exploring these dimensions, the study aims to provide a nuanced understanding of the conditions under which AI can be successfully integrated into SMEs processes. This study will adopt a sequential exploratory mixed methods design, integrating qualitative and quantitative approaches to investigate how SMEs adopt and implement AI applications. The research will begin with a qualitative phase, using semi-structured interviews to explore contextual and experiential factors influencing AI adoption. Building on the qualitative insights and theoretical background, the second phase will involve a survey-based quantitative study designed to test the relationships and hypotheses identified in the earlier stage. This mixed methods design ensures a comprehensive understanding of AI adoption in SMEs by first uncovering key drivers and concerns qualitatively and then validating these insights through broader quantitative analysis. The integration of these approaches enhances both the depth and generalizability of the research findings.

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