Towards a Comprehensive Understanding of AI Adoption in Organizations: A Managerial Perspective

Artificial intelligence (AI) is increasingly becoming part of how organizations work, make decisions, and plan for the future. In the current business world, this shift is especially visible in the information technology (IT) area. Organizations are experimenting as well as implementing AI to automate many routine tasks, support vital decision-makings, improve service delivery at different levels, and redesign business processes. Although AI adoption has received growing scholarly attention, there is still limited understanding of how this adoption process is happening inside organizations, particularly across different managerial levels. Existing studies often examine AI adoption either at the organizational level or at the individual user level. Less attention has been given to the people in the middle of this transition: managers who are expected to interpret AI initiatives, translate them into practice, respond to employee concerns, and deal with the organizational tensions that come with technological change. We address this gap by exploring how managerial-level personnel in IT organizations experience and make sense of AI adoption. The study focuses on organizations of different sizes and at different stages of AI maturity, recognizing that AI adoption is unlikely to look the same in a small firm experimenting with basic automation and a large enterprise integrating AI into core business processes. Managers are a particularly important group to study because they often sit between strategic decision-makers and operational employees. They may support AI adoption, but they may also question it, slow it down, or reshape it depending on available resources, organizational culture, technical readiness, and perceived risks. We adopt an exploratory qualitative case study design. The case study is guided by an interpretivist perspective. We believe this perspective is appropriate because AI adoption should not be treated as a purely technical implementation problem. Rather, this phenomenon should be understood as a social and organizational process shaped by people's interpretations of AI, their expectations, fears, and how they believe it integrates with existing work practices. For this study, we use Sociotechnical Systems theory as the primary theoretical lens. From this view, AI adoption depends on the interaction between people, technologies, tasks, structures, and organizational environments. In other words, the success or failure of AI integration is likely to depend not only on the quality of the technology but also on how well it fits with human work, managerial routines, and organizational priorities. The study is also guided by Structuration Theory and Institutional Theory. Structuration Theory helps explain how existing organizational structures may shape AI adoption by enabling certain actions while limiting others. At the same time, AI may gradually reshape those structures by changing workflows, decision-making processes, accountability arrangements, and patterns of coordination. Institutional Theory adds another layer by drawing attention to external pressures, such as competition, regulation, professional expectations, and industry norms. These pressures may influence why organizations adopt AI, how quickly they do so, and how they justify AI-related decisions. For this study, data will be collected through semi-structured interviews with managerial-level personnel from multiple IT organizations, including small, medium, and large enterprises. Participants will be selected from strategic, middle, and operational management roles so that the study can capture a range of perspectives across organizational hierarchies. The interviews will examine areas such as AI awareness, organizational readiness, perceived benefits and risks, implementation barriers, leadership support, employee response, and the practical challenges of integrating AI into existing systems and routines. The data will be analyzed using thematic analysis, allowing patterns, and recurring insights to emerge from the interviews while remaining connected to the theoretical framework. We expect the study to make both theoretical and practical contributions. Theoretically, it extends sociotechnical and structuration-based perspectives to the study of AI adoption in IT organizations. It also offers a more grounded, multilevel understanding of AI adoption by examining how managers interpret and manage AI-related changes across different organizational contexts. On the practical level, the findings of this study should help organizational leaders better understand why AI initiatives become successful in some settings, but not so successful in others. The study should also identify contextual variables that make AI integration more acceptable in organizations. As a research-in-progress paper, this study will present the conceptual foundation, theoretical framing, and methodological design of the project. Preliminary insights from early interviews will be discussed where available. Overall, the study responds to the need for more context-sensitive qualitative research on AI adoption and contributes to the broader conversation about how organizations can adopt AI in ways that are strategic, realistic, and responsible.