Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review

TL;DR

This systematic review explores how Generative AI impacts enterprise architecture in agile settings, highlighting its benefits, risks, skills needed, and organizational enablers to guide responsible adoption and digital transformation.

Contribution

It provides a comprehensive mapping of GenAI use cases, risks, and implications for capability building and governance in agile enterprise architecture.

Findings

GenAI supports design ideation and artifact creation

Risks include opacity, bias, and privacy concerns

Emerging skills involve prompt engineering and oversight

Abstract

Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect roles. GenAI most consistently supports (i) design ideation and trade-off exploration; (ii) rapid creation and refinement of artifacts (e.g., code, models, documentation); and (iii) architectural decision support and knowledge retrieval. Reported risks include opacity and bias, contextually incorrect outputs leading to rework, privacy and compliance concerns, and social loafing. We also identify emerging skills and competencies, including prompt engineering, model evaluation, and professional oversight, and organizational enablers around readiness and adaptive governance. The review contributes with (1) a mapping of GenAI use cases and risks in agile architecting, (2) implications for capability building and governance, and (3) an initial research agenda on human-AI collaboration in architecture. Overall, the findings inform responsible adoption of GenAI that accelerates digital transformation while safeguarding architectural integrity.