From expectations to reality : organizational adoption of AI and other emerging technologies

Abstract

Rapid technological evolution continues to accelerate the pace of change and completely restructure organizations by improving the effectiveness of their business processes. As the business environment becomes more complex and competitive, organizations are obliged to set performance-driven goals and adopt strategies to enhance productivity, efficiency, and cost-effectiveness. However, despite substantial investments, many technology change adoption initiatives fail to deliver the anticipated organizational value. Prior technology adoption research largely focuses on pre-adoption decision factors and user acceptance, offering limited explanations of how organizational motivations are formed, how they evolve during adoption, and why post-adoption outcomes often diverge from initial expectations. This gap has widened with the rapid organizational adoption of artificial intelligence (AI), where high expectations contrast sharply with implementation challenges and unintended consequences.

This dissertation aims to fill the identified gap through a qualitative and interpretive research approach. The data was collected through semi-structured interviews conducted in two phases with organizations and analyzed using the grounded theory method. The study first investigates motivational drivers, pre- and post-adoption challenges, and success enablers. Further, it identifies triggers behind motivation and their consequences after adopting a particular technology. Finally, it tries to investigate how and why expectations often diverge and emerge as mismatches as post-adoption outcomes, especially analyzing the most demanding, innovative, and rapidly evolving AI technology in the current business era.

The findings reveal that technology adoption is a complex socio-technical process where organizational expectations, shaped by strategic, technological, and environmental factors, often misalign with infrastructural realities and organizational capabilities. This dissertation contributes a process-oriented explanation of technology change adoption that links motivations, triggers, and outcomes, offering both theoretical advancement and practical guidance for adopting AI-driven transformation initiatives.