Artificial Intelligence Support for Software Architecture Practice: A Systematic Review and Future Directions

AI is increasingly applied across software engineering, yet its explicit role in software architecture remains insufficiently understood. Architectural practices rely on complex tradeoffs, documentation, and long-term evolution, all of which are traditionally manual, error-prone, and difficult to sustain. To clarify how AI can address these challenges, we conducted a systematic literature review of 51 peer-reviewed primary studies and systematically mapped their contributions onto 17 practitioner-reported software architecture challenges derived from empirical interviews. This analysis identifies 14 topical areas where AI has been applied to architectural tasks and identifies six AI-specific challenges that expose fundamental gaps between current capabilities and practitioner needs. Building on these findings, we chart a research agenda for AI-driven software architecture organized around five strategic pillars. By grounding the roadmap in both systematic evidence and practitioner insights, this work provides the first peer-reviewed comprehensive synthesis of AI contributions to software architecture, establishes a foundation for future research, and outlines the conditions under which AI can become a trustworthy partner in architectural design, evaluation, and evolution.