Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review
Stefan Julian Kooy, Jean Paul Sebastian Piest, Rob Henk Bemthuis
TL;DR
This systematic literature review investigates how Generative AI (GenAI) affects enterprise architects (EAs) in agile environments. Using Kitchenham and PRISMA-guided protocols, the authors screened 1,697 records and included 33 studies spanning enterprise, solution, domain, business, and IT architect roles to map GenAI opportunities, risks, and governance needs. Key findings show GenAI accelerates design ideation, artifact generation (code, models, documentation), and architectural decision support, while introducing opacity, bias, context misalignment, privacy concerns, and potential overreliance. The study offers a structured mapping of GenAI use cases and risks, discusses implications for capability building and governance, and outlines an initial research agenda on human-AI collaboration in architecture to enable responsible, agile digital transformation.
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.