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<p>Artificial Intelligence (AI) adoption presents significant challenges, including fragmented processes, legacy systems, and concerns about ethics and governance. This study proposes a conceptual framework for AI adoption within business architecture to address these issues, aligned with BIZBOK® principles. The framework integrates business architecture elements—capabilities, organization, information, and value streams—with AI adoption components defined by the Machine Intelligence Continuum, AI technologies, and AI use cases. The framework was developed using the Design Science Research Methodology (DSRM) and evaluated through two case studies in the public and non-profit domains, specifically higher education and local government. Data were collected through semi-structured interviews, document reviews, and expert panel sessions, supported by AS-IS and TO-BE modeling. The results suggest that the framework provides a structured and adaptable approach for aligning AI technologies with organizational goals while incorporating ethical governance. This research contributes practical guidance for responsible and scalable AI adoption across diverse institutional contexts, particularly relevant to public and non-profit sectors.</p>

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A conceptual framework for AI adoption in business architecture with case studies in higher education and government

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月23日

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  • Leni Fitriani,
  • Masayu Leylia Khodra,
  • Kridanto Surendro

摘要

Artificial Intelligence (AI) adoption presents significant challenges, including fragmented processes, legacy systems, and concerns about ethics and governance. This study proposes a conceptual framework for AI adoption within business architecture to address these issues, aligned with BIZBOK® principles. The framework integrates business architecture elements—capabilities, organization, information, and value streams—with AI adoption components defined by the Machine Intelligence Continuum, AI technologies, and AI use cases. The framework was developed using the Design Science Research Methodology (DSRM) and evaluated through two case studies in the public and non-profit domains, specifically higher education and local government. Data were collected through semi-structured interviews, document reviews, and expert panel sessions, supported by AS-IS and TO-BE modeling. The results suggest that the framework provides a structured and adaptable approach for aligning AI technologies with organizational goals while incorporating ethical governance. This research contributes practical guidance for responsible and scalable AI adoption across diverse institutional contexts, particularly relevant to public and non-profit sectors.