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• Argues that multi-system interactions are central to sustainability transitions. • Takes stock of relevant work examining multi-system dynamics. • Sketches the contours of a novel multi-system perspective. • Indicates how such a perspective contributes to the transitions research agenda. As sustainability challenges and the transformative changes they necessitate stretch well beyond the boundaries of individual socio-technical systems, there is a pressing need to generate more sustained interest in the dynamics occurring across rather than within systems. In response, this viewpoint takes stock of existing work on multi-system dynamics and sketches the contours of a multi-system perspective, which conceptualizes the interactions among systems as diverse, layered, and evolving. A multi-system perspective draws attention to: the present functional and structural couplings that link systems of interest; emerging sites of interaction that could bring systems further into contact; along with the patterns marking system interactions and their implications for sustainability transitions. Attending more carefully to multi-system dynamics complements several promising directions of inquiry identified by the agenda for sustainability transitions research, including recent efforts to broaden the scope of analysis, capture the pervasiveness of change, and consider opportunities for acceleration. [ABSTRACT FROM AUTHOR]

Key Enabling Technologies (KETs) are pervasive groups of technologies expected to enable innovation. They have been promoted as technologies with tremendous potential for boosting economic growth and sustainability in all sectors of society – a claim whose validity remains underexplored. Building on systems thinking and the Multi-Level Perspective, we develop a novel approach to assess the socio-technical impact of a KET regime on a transitioning sectoral regime. This approach is applied to the case of the Artificial Intelligence (AI) KET impacting the decarbonization of the energy-intensive steel industry. To assess AI's technical impact, we compiled an inventory of AI tools based on reviewing technical scientific articles. Our analysis shows that AI adds technological value to the full range of areas in the steel industry, like predicting process parameters; optimizing operations, scheduling, and electrical energy; and forecasting product demand, quality, and site emissions. Semi-structured interviews were the primary data source to assess AI's broader socio-institutional impact. The results indicate that AI may currently be reinforcing path dependencies of the steel industry, as AI tools are more focused on incremental improvement for existing technologies rather than novel low-carbon technologies. However, AI also offers capabilities to reduce barriers to sustainability innovation, like system integration challenges, flexibility challenges, demand-side barriers, and risk-related barriers. Finally, we reflect on the generalizability of our approach for studying other transitions, and we induce characteristics of the AI-Digital KET regimes. We find that through these characteristics, the AI-Digital KET regime alters existing and creates new system structures (actors, networks, and institutions) within the impacted sector. • Our novel approach analyzes Key Enabling Technologies' socio-technical impact on transitions. • We assess the impact of the AI-KET on the steel industry's decarbonization transition. • We find more AI use on established technologies than on novel low-carbon innovation. • We identify new or altered system structures in steelmaking, introduced by the AI-KET regime. • These structural changes can help reduce some barriers to innovation. [ABSTRACT FROM AUTHOR]