The interconnected risks posed by climate change, energy transitions, and sustainable development require transdisciplinary perspectives to understand the complex dynamics and interdependencies involved.  Recent breakthroughs in artificial intelligence (AI) present promising opportunities to accelerate MultiSector Dynamics (MSD) research in this regard.  There is potential for AI in combination with longstanding techniques from fields in natural language processing (NLP) and graph theory to be used to uncover hidden connections, extract causal relationships, and discover insights across domains.  We demonstrate a systematic approach to doing this type of analyses using a combination of modern NLP, graph and other machine learning, and large language models to gain on-demand topical access to, and insight from, a corpus of over 100,000 scientific publications and other ancillary data sources that describe the literature landscape for the field of MSD at large.  These insights help us identify stable and emerging communities of researchers and research topics within MSD. We are also able to identify and describe cross-domain literature and concepts through the lens of interconnected risks to quickly understand gaps in the form of opportunities that currently exist for MSD researchers. Our MSD community of practice believes that cross-disciplinary training and teaming is critical for advancing complex adaptive human-Earth systems science in a world of deeply uncertain and interconnected risks.