Key Factors Influencing AI Implementation in Large Organizations

Abstract

Even though the concept of artificial intelligence (AI) can be traced back to the mid 20th century, the technology has rapidly evolved in recent years. Today, it is starting to gain traction throughout multiple levels and instances in society, used by individuals and organizations. The integration of AI within large organizations presents both formidable challenges and significant opportunities. The ability to adapt to continuous technological shifts is becoming increasingly vital for maintaining a competitive advantage and strategic relevance. Against this backdrop, the focus of this thesis is on uncovering the multifaceted factors that influence AI implementation, emphasizing the nuanced interplay between internal capacities and external pressures. By encompassing a qualitative research approach to identify and explore such factors. This approach was designed around a foundational literature review paired with semi-structured interviews involving 13 respondents. The interview data was analyzed through a thematic analysis whose findings later were compared and contrasted with the existing literature to highlight both consistencies and discrepancies. Four main factors emerged from the thematic analysis: Facilitating AI Prerequisites, Aligning Organizational and Technological Elements, Navigating AI Impact on Workforce, and AI Competence and Governance. Each category is substantiated by themes impacting the implementation, which in turn were derived upon codes that originate from the interview data. Theoretically, this thesis enriches the discourse on technology adoption by integrating the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, offering a comprehensive perspective on the factors of AI implementation. The integration of TOE and TAM in this study demonstrates that attributes central to TAM are significantly influenced by TOE. Practically, the study provides insights for organizational leaders seeking to navigate the complexities of AI implementation, highlighting the need for strategic agility and proactive management of technological transitions.