Organizational and Employee Perspectives towards AI Adoption in SME Business Operations

Abstract

The emergence of generative AI and its rapid development in the last few years has introduced new opportunities and challenges for companies pursuing to integrate AI-driven solutions into company operations. Despite the acknowledged potential of AI to drive business innovation and redesign business operations, a clear gap exists between its potential and actual adoption in small and medium-sized enterprises. The objective of this thesis was to study employee perceptions and organizational readiness for AI adoption to help to define the strategic approach in a SME context. The theoretical framework is built upon the Technology-Organization-Environment (TOE) framework that is extended to conceptual framework with change management perspectives that play a key role in leading the organizational change together with Human-AI collaboration principles addressing AI-related challenges to reduce bias and maintain human control.

This study adopted a qualitative case study approach, combined with thematic analysis and data collected through semi-structured interviews from 11 participants representing employees from the case company based in the EU. The findings revealed challenges on resource constraints, fragmented systems, lack of training and absence of clear guidelines and defined responsibilities to lead AI initiatives across different departments. The main opportunities lie in the process automation of manual and time-consuming workflows, utilizing AI’s capabilities in predictive analytics to enhance decision-making and AI support in navigation of information overload.

This research contributes to the growing literature on AI adoption in business settings by high-lighting the importance of developing organizational and human factors together with technological capabilities from the perspective of a mid-sized technology company. This study emphasizes integration of AI when it fits to the actual business needs of the company and maintaining human oversight to ensure data security, transparency, and fairness in building trust towards AI systems. The study concludes that AI adoption should be aligned with the company objectives, available resources, and employee capabilities. The results are presented through an AI strategy blueprint that provides guidance on enabling responsible and efficient AI adoption. The study provides practical recommendations for SME managers who are in the early stage of adopting AI into company operations.