Spillovers of AI Adoption Among Manufacturing SMEs: Evidence from Finland
Skromnov, Marat (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025053018712
https://urn.fi/URN:NBN:fi:amk-2025053018712
Tiivistelmä
This bachelor’s thesis investigates the challenges and barriers that small and medium-sized enterprises (SMEs) in Finland encounter when adopting Artificial Intelligence (AI), while also exploring the potential links between AI adoption and sustainability. Despite the growing significance of AI in enhancing operational efficiency and supporting sustainable practices, SMEs often struggle with limited resources, technical expertise, and organisational readiness. Using the Technology-Organisation-Environment (TOE) framework, this study applies a qualitative methodology based on five semi-structured interviews with representatives from Finnish SMEs engaged in AI adoption.
The findings reveal that key challenges include data security concerns, financial constraints related to infrastructure upgrades, resistance to change, and a general lack of awareness about AI's benefits. Additionally, the rapid pace of AI evolution and limited training opportunities compound these difficulties. While many SMEs view AI as a tool to streamline processes and improve efficiency, the direct connection between AI adoption and sustainability remains ambiguous. However, some organisations recognise AI’s role in predictive maintenance, energy efficiency, and resource optimisation as indirect contributions to sustainable practices.
This study contributes to the limited body of research on AI implementation in SMEs, particularly in the context of sustainability. It offers practical recommendations for overcoming adoption barriers, such as employee training, innovation-oriented culture, and aligning with regulatory standards. The research also underscores the importance of future studies focusing on broader sample sizes and cross-industry perspectives to deepen the understanding of AI’s transformative potential in small business environments.
The findings reveal that key challenges include data security concerns, financial constraints related to infrastructure upgrades, resistance to change, and a general lack of awareness about AI's benefits. Additionally, the rapid pace of AI evolution and limited training opportunities compound these difficulties. While many SMEs view AI as a tool to streamline processes and improve efficiency, the direct connection between AI adoption and sustainability remains ambiguous. However, some organisations recognise AI’s role in predictive maintenance, energy efficiency, and resource optimisation as indirect contributions to sustainable practices.
This study contributes to the limited body of research on AI implementation in SMEs, particularly in the context of sustainability. It offers practical recommendations for overcoming adoption barriers, such as employee training, innovation-oriented culture, and aligning with regulatory standards. The research also underscores the importance of future studies focusing on broader sample sizes and cross-industry perspectives to deepen the understanding of AI’s transformative potential in small business environments.