What question did this study set out to answer?

This research aims to identify factors that affect the adoption of AI technologies and examine the changes in the implementation process within organizations.

May 17, 2026Open Access

Assimilating artificial intelligence in organizations: a qualitative investigation of the adoption and implementation challenge

Puntos clave

This research aims to identify factors that affect the adoption of AI technologies and examine the changes in the implementation process within organizations.
Exploratory interviews and multiple case studies were conducted for data collection.
Existing models of technology adoption and implementation were reviewed to identify gaps.
Frameworks like Technology-Organization-Environment and Technology-Organization-People were extended in the analysis.
Established factors like relative advantage and management support were validated; unique AI-specific factors such as data infrastructure were identified.
AI implementation outcomes were significantly influenced by firms’ strategies, relationships with AI vendors, and awareness levels across the organization.
Insights gained help clarify the complexities of AI's integration beyond simple success or failure metrics.

Resumen

Artificial Intelligence (AI) is predicted to contribute 15 trillion to the global economy by 2030, establishing it as a crucial General-Purpose Technology. However, the implementation of AI in businesses has not kept pace with the advancements in its research and the surrounding excitement. This has prompted the need for in-depth studies exploring the dynamics of its adoption and utilization by organizations. This dissertation contributes to the technology and innovation management literature with two main objectives: (1) identifying factors that influence organizations' decisions to adopt AI technologies and (2) examining how the technology implementation process is altered when organizations integrate AI. Initially, the research reviews existing models of technology adoption and implementation, pinpointing gaps in the current understanding of AI integration into business practices and advocating for new theoretical perspectives to comprehend its organizational impacts. Empirical investigation follows, based on primary data collected through exploratory interviews and multiple case studies, as detailed in Chapter 3. The findings, elaborated in Chapters 4 and 5, extend frameworks like Technology-Organization-Environment and Technology-Organization-People. The initial part of the research validates the importance of established factors such as relative advantage and management support, while also identifying unique AI-specific factors like the need for data infrastructure and technology understandability. The latter part of the dissertation delves into how AI implementation unfolds within companies. It reveals that the interplay between firms' implementation strategies, their relationship with AI vendors, and the gradual increase in AI awareness at various organizational levels significantly influences the implementation outcomes, moving beyond the simple dichotomy of success and failure. Overall, this work enhances our understanding of AI's role in organizational transformation and lays the groundwork for further academic and practical investigations.

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