Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption: Bridging Innovation and Governance in Large Organisations

TL;DR

This paper investigates how Enterprise Architecture Management can serve as a dynamic capability to facilitate scalable and sustainable Generative AI adoption in large organizations by addressing governance, innovation, and organizational challenges.

Contribution

It introduces tailored EA frameworks and conceptual models that specifically address the unique requirements of Generative AI adoption in large enterprises.

Findings

EAM enhances GenAI adoption through strategic alignment and governance.

Tailored EA frameworks improve handling of GenAI-specific challenges.

Organizational agility is critical for successful GenAI integration.

Abstract

Generative Artificial Intelligence is a powerful new technology with the potential to boost innovation and reshape governance in many industries. Nevertheless, organisations face major challenges in scaling GenAI, including technology complexity, governance gaps and resource misalignments. This study explores how Enterprise Architecture Management can meet the complex requirements of GenAI adoption within large enterprises. Based on a systematic literature review and the qualitative analysis of 16 semi-structured interviews with experts, it examines the relationships between EAM, dynamic capabilities and GenAI adoption. The review identified key limitations in existing EA frameworks, particularly their inability to fully address the unique requirements of GenAI. The interviews, analysed using the Gioia methodology, revealed critical enablers and barriers to GenAI adoption across industries. The findings indicate that EAM, when theorised as sensing, seizing and transforming dynamic capabilities, can enhance GenAI adoption by improving strategic alignment, governance frameworks and organisational agility. However, the study also highlights the need to tailor EA frameworks to GenAI-specific challenges, including low data governance maturity and the balance between innovation and compliance. Several conceptual frameworks are proposed to guide EA leaders in aligning GenAI maturity with organisational readiness. The work contributes to academic understanding and industry practice by clarifying the role of EA in bridging innovation and governance in disruptive technology environments.