As artificial intelligence transforms how we understand our world through generative capabilities and agentic workflows, some physical and digital ecosystems remain opaque, either through intentional measures taken to hide or simply because the ecosystem is so large and so concentrated that most signals are internal. This chapter presents a groundbreaking methodology for illuminating previously opaque ecosystems. By analyzing patterns in carefully curated surrounding co-dependent commercial environments, researchers demonstrate how AI can detect perturbations that traditional methods miss, particularly during cascading disruptions. The research reveals that attempts to increase opacity paradoxically create new observable patterns, challenging conventional understanding of system visibility and suggesting great value for the approach. Moreover, the methods described in this chapter prove particularly valuable during compound disruption events, when the response to one disruption is incomplete before another disruption takes place. These situations are particularly confounding to traditional predictive models due to the lack of training data and other statistical constraints. As AI continues to transform how we interact with complex systems, including human sociotechnical ones, the ethical implications of enhanced visibility become increasingly significant. The chapter concludes that organizations must develop comprehensive approaches to understanding their ecosystem interactions while establishing robust governance frameworks that balance the benefits of transparency with privacy concerns. This research offers critical insights for navigating the evolving landscape of human decision-making and AI systems.