Earticle

초록

영어

Digital Transformation (DX) is the primary innovation strategy to enhance organizational flexibility and productivity in the workplace. Each company promotes DX within its unique organizational direction and situations, and employee competency becomes a critical factor for successful transformation. However, existing small and medium-sized enterprises (SMEs) have limited resources to hire AI experts or develop appropriate DX implementation strategies compared to large enterprises. As a result, many SMEs adopt a more practical and efficient approach by implementing internal AI reskilling programs for their employees. In this paper, we aim to examine whether such reskilling programs effectively drive DX adaptation within SMEs. For our investigation, we designed AI-based reskilling programs that were implemented across 39 SMEs from diverse industries, alongside Proof of Concept (PoC) projects that were aligned with each company’s DX goals. We extracted key terms from the reskilling program reports and categorized the companies by industry type and organizational scale. We also conducted text analysis to identify strategic directions and transformation intents regarding DX. Our findings demonstrated that AI reskilling programs could effectively support SMEs in achieving DX. Our analysis results also highlight how industry-specific characteristics, and the availability of internal resources influence DX implementation. In particular, the number of employees and the ratio of R&D investment emerged as key factors. Furthermore, this study provides a practical roadmap for AI adoption and strategy tailored to similar types of SMEs, serving as a foundational reference for inclusive and sustainable digital transformation.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Digital Transformation and Performance
2.2. Workforce Development for Digital Transformation
Ⅲ. Research Model and Hypotheses
3.1. System Model of DX Reskilling Scheme
3.2. Subject Characteristics
3.3. Measurements
3.4. Methodology
Ⅳ. Textual Analysis and Experiment Results
4.1. Reskilling AI Education Program by Industry (RQ1)
4.2. Reskilling AI Education Program by Firm Attributes (RQ2)
4.3. Reskilling AI Education Program by DX Performances (RQ3)
Ⅴ. Discussion and Consideration
5.1. Key Insights and Considerations
5.2. Limitation and Future Research Directions
Ⅵ. Conclusion

키워드

저자

• Hyangmi Kim [ Senior Manager, LG AI Research, Korea ]
• Hyo Jin Song [ Undergraduate, School of AI Convergence, Sungshin Women’s University, Korea ]
• Joon Young Kim [ Assistant Professor, School of Artificial Intelligence (AI) Convergence, Sungshin Women’s University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658