Generative artificial intelligence (GenAI) challenges several assumptions that have long underpinned design science research (DSR), including that artifacts can be clearly bounded, evaluation results can be treated as evidence about stable contributions, and prescriptive knowledge can travel across contexts with limited boundary specification. This paper critically reviews how empirical DSR studies engage with these tensions when designing GenAI artifacts. Using a PRISMA-guided systematic literature review, we analyze the emerging corpus through a configuration-centric lens that examines how studies define the artifact, stabilize evidence, formulate reusable knowledge, and address governance in use. The review shows that most GenAI artifacts are not bounded tools but socio-technical configurations that combine models, prompts, retrieval mechanisms, orchestration logic, and human oversight. It also identifies recurring weaknesses, including under-specified artifact boundaries, limited configuration disclosure, unstable evaluation evidence, incomplete portability conditions, and governance concerns that are acknowledged but rarely operationalized as design features. Based on these findings, we argue that GenAI should be treated as a boundary condition that intensifies key DSR challenges and requires adaptations in artifact conceptualization, evaluation, reporting, and the formulation of reusable design knowledge.