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An Extensive Review of Organizational AI Adoption Challenges and Consequent Integrated AI Appliance Proposal for Adoption Facilitation and Impact Studies

Submitted:

01 December 2025

Posted:

04 December 2025

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Abstract
Although AI is widely believed to have transformative potential in organizations, recent reports reveal that many organizations are grappling with value derivation therefrom and the ability to take ownership of due ethical and regulatory demands, among other responsible uses of technology. Our goal is to examine these challenges with a view to proposing an approach to effective AI adoption by organizations and pave the way for further impact studies. As a first step, we reviewed and clarified these challenges, categorizing them into Weak or Non-Existent Strategy, Poor Data Readiness and Privacy Concerns, Inadequate Integration with Existing Technology Stack, Inadequate Human Knowledge Skills and Attitudes/Abilities, Scalable and Secure Infrastructure Challenges, Ethical Governance Concerns, Regulatory Framework Lag, Responsibility and Accountability Concerns as well as Reliability Concerns. Next, we carried out a thematic review of constituent AI technology innovation concepts and tools that have adoption potential in organizations vis-à-vis Enterprise Resource Planning (ERP). In the light of these reviews, we used inductive reasoning to propose an approach to AI adoption and create a tool (OAAD) that exemplifies our recommendations, and which could facilitate well-informed adoption and real-life impact research. To set a compass for our effective adoption approach proposal, we expanded on Yang et al. (2024) and defined organizational AI readiness as the organization’s capacity and disposition to deploy and use AI technology tools in ethical, responsible and accountable ways that add value to the organization. Finally, we make some recommendations for progressive impact studies in line with our proposed adoption experimentation.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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