Technology Category
Cognitive Modeling & Cognitive Systems: Agent ArchitecturesPhilosophy and Ethics of AI: Artificial General IntelligenceMultiagent Systems: Agent/AI Theories and Architectures
Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review
🤖 AI Summary
This study addresses the fragmented empirical evidence on generative AI’s (GenAI) impact on enterprise architecture (EA) work within agile software organizations. To synthesize current knowledge rigorously, we conducted a systematic literature review (SLR) adhering to Kitchenham and PRISMA guidelines, screening 1,697 publications to identify 33 empirical studies. Our analysis reveals, for the first time, three core GenAI roles in EA—ideation support for architectural design, rapid generation of architecture artifacts, and data-informed decision support—as well as four critical risks: technical debt accumulation, governance failure, role ambiguity, and model hallucination. We further identify emerging capability requirements, including prompt engineering and model evaluation. Based on these findings, we propose a research agenda centered on capability development, adaptive governance frameworks, and human-AI collaboration mechanisms. This work establishes a theoretical foundation and actionable pathways for responsible, sustainable integration of GenAI into EA practice.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Investigating GenAI's impact on enterprise architects in agile environments
Mapping use cases and risks of AI in architectural work
Identifying skills and governance for responsible AI adoption
Innovation
Methods, ideas, or system contributions that make the work stand out.
Systematic literature review of 1697 records
GenAI supports design ideation and artifact creation
Identifies risks and skills for human-AI collaboration
🔎 Similar Papers
