Integrating AI into supply chains : developing a framework addressing technical and social dimensions
Beliaevskaia, Mariia (2025)
Diplomityö
2025
School of Engineering Science, Tuotantotalous
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20251124111072
https://urn.fi/URN:NBN:fi-fe20251124111072
Tiivistelmä
This study explores the integration of Artificial Intelligence (AI) into supply chains through the lens of a socio-technical systems (STS) approach. The study responds to a growing need to balance technological innovation with human and organizational factors in digital transformation processes. The purpose of the study is to develop a comprehensive socio-technical framework that supports organizations in integrating AI into supply chains in a balanced and sustainable manner. The research combines a systematic literature review with an expert survey conducted among ten professionals representing international and local companies. The survey collected qualitative and quantitative insights on technical and social success factors, challenges, and strategies for AI adoption.
The results reveal that successful AI integration depends on strong data foundations, effective system integration, leadership commitment, and an innovation-oriented culture. Social and human factors, including digital skills, communication, and cross-functional collaboration, emerged as equally critical for achieving long-term success. Among the main barriers identified were poor data quality, lack of digital competence, and resistance to change. Respondents emphasized that structured change management, employee engagement, and gradual scaling through pilot projects are the most effective strategies for overcoming these challenges.
The findings confirm that AI implementation in supply chains is not merely a technical project but a complex socio-technical transformation. The study proposes a practical profiling tool that allows organizations to assess their socio-technical maturity and identify areas for improvement across data infrastructure, culture, skills, and governance. This framework bridges a notable gap in the existing literature, offering both theoretical contribution and managerial guidance.
In conclusion, the research demonstrates that organizations achieving the most sustainable AI outcomes are those that align technological progress with social readiness. By integrating technical capabilities and human factors, companies can build supply chains that are not only intelligent but also adaptable, transparent, and resilient.
The results reveal that successful AI integration depends on strong data foundations, effective system integration, leadership commitment, and an innovation-oriented culture. Social and human factors, including digital skills, communication, and cross-functional collaboration, emerged as equally critical for achieving long-term success. Among the main barriers identified were poor data quality, lack of digital competence, and resistance to change. Respondents emphasized that structured change management, employee engagement, and gradual scaling through pilot projects are the most effective strategies for overcoming these challenges.
The findings confirm that AI implementation in supply chains is not merely a technical project but a complex socio-technical transformation. The study proposes a practical profiling tool that allows organizations to assess their socio-technical maturity and identify areas for improvement across data infrastructure, culture, skills, and governance. This framework bridges a notable gap in the existing literature, offering both theoretical contribution and managerial guidance.
In conclusion, the research demonstrates that organizations achieving the most sustainable AI outcomes are those that align technological progress with social readiness. By integrating technical capabilities and human factors, companies can build supply chains that are not only intelligent but also adaptable, transparent, and resilient.