Paper Type
Complete
Abstract
The advent of artificial intelligence (AI) is transforming how organizations operate, reshaping strategies, business processes, and technological infrastructures. To adapt effectively, companies must adopt a holistic approach that addresses organizational architecture. Enterprise Architecture Management (EAM) provides a framework to align strategic goals with IT systems and processes, supporting organizations in assessing their readiness for AI and identifying opportunities for its adoption. This paper introduces a framework based on EAM principles to evaluate AI maturity, identify gaps, and provide actionable measures to achieve AI-related business objectives. Using the Design Science Research methodology, the framework supports organizations in adopting AI holistically and identifying suitable use cases. While conceptual in nature, the study offers valuable insights for practitioners and highlights the need for future research to validate the framework in real-world scenarios and explore external factors like regulation and ethics. This work contributes to advancing AI integration through a systematic and comprehensive approach.
Paper Number
1080
Recommended Citation
Klingenstein, Felix; Kessler, Samuel; Hoeltl, Moritz; Daume, Pascal; and Fischer, Marcus, "A maturity model to assess and enhance the AI readiness of an Enterprise Architecture" (2025). AMCIS 2025 Proceedings. 27.
https://aisel.aisnet.org/amcis2025/sig_osra/sig_osra/27
A maturity model to assess and enhance the AI readiness of an Enterprise Architecture
The advent of artificial intelligence (AI) is transforming how organizations operate, reshaping strategies, business processes, and technological infrastructures. To adapt effectively, companies must adopt a holistic approach that addresses organizational architecture. Enterprise Architecture Management (EAM) provides a framework to align strategic goals with IT systems and processes, supporting organizations in assessing their readiness for AI and identifying opportunities for its adoption. This paper introduces a framework based on EAM principles to evaluate AI maturity, identify gaps, and provide actionable measures to achieve AI-related business objectives. Using the Design Science Research methodology, the framework supports organizations in adopting AI holistically and identifying suitable use cases. While conceptual in nature, the study offers valuable insights for practitioners and highlights the need for future research to validate the framework in real-world scenarios and explore external factors like regulation and ethics. This work contributes to advancing AI integration through a systematic and comprehensive approach.
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