Structural and Perceptual Constraints in AI Adoption: Financial and Human Capital Challenges Facing Small Businesses in the Era of Industry 4.0

Authors: Adrian Gheorghe Florea, Diana Claudia Perțicaș, Hillary Juma Wafula, Claudia Diana Sabău-Popa

DOI: 10.24818/BASIQ/2025/11/035

Abstract

Our study investigates the structural complexities and perceptual constraints associated with the implementation of digitization and Artificial Intelligence (AI) in firms, with a specific focus on small and medium-sized enterprises (SMEs) operating in the context of Industry 4.0. It explores the impact of financial and human resource limitations, employee resistance, data privacy concerns, and the role of digital leadership in AI adoption. The research adopts a mixed-methods approach, incorporating a literature review and a structural equation modeling (SEM) analysis to examine the hypothesized relationships between perceived barriers and firms’ adoption of AI. Key indicators such as RMSEA, CFI, and TLI were used to assess model fitness, while regression coefficients and p-values determined the strength and significance of the relationships.

The findings reveal that the perception of financial limitations does not significantly hinder AI adoption, challenging conventional assumptions. Conversely, a lack of qualified personnel shows a weak but notable negative correlation with AI implementation capacity. Resistance from employees, initially perceived as a barrier, was found to have a surprisingly positive effect—possibly due to prior exposure to automation. Ethical concerns and data privacy did not significantly deter AI initiatives, with firms adhering to GDPR frameworks. The lack of awareness regarding available funding opportunities emerged as a notable external constraint.

This study offers original insights into how internal perceptions and structural limitations shape the digital transformation journey of SMEs. It moves beyond deterministic views of financial barriers, highlighting the nuanced interplay between organizational readiness and human factors.

The results underscore the need for targeted skill development, transparent AI communication strategies, and improved access to funding information. Policymakers and firm leaders should prioritize digital leadership, workforce reskilling, and inclusive innovation to foster sustainable AI integration in SMEs.