Environmental policies, though often ambitious in their conception, frequently falter during implementation due to a complex mix of po litical, economic, and social factors. This well-documented “imple mentation gap” creates a significant challenge for Non-Governmental Organizations (NGOs), which have traditionally operated in a reactive capacity, addressing environmental damage only after it has occurred. This paper puts forward a novel conceptual framework for an Artificial Intelligence-powered Early Warning System (EWS) designed to shift these organizations to a proactive and predictive model of advocacy. At its core, the system synthesizes varied, publicly available data streams to detect leading indicators of policy failure. It integrates computer vision to analyze satellite imagery for physical changes, Natural Language Processing (NLP) to monitor political and media discourse for shifts in sentiment and priority, and machine learning to analyze quantitative data and map complex networks. By systematically identifying subtle yet practical signals—such as resource deficits, negative discourse, and adverse lobbying activities—the EWS generates a predictive “failure risk score” for specific policies. This score is designed to equip NGOs to concentrate their limited resources on the most at-risk policies. As demonstrated through a detailed case study on corporate “No Deforest ation” pledges in the Indonesian palm oil sector, the framework allows NGOs to engage policymakers with compelling, data-driven evidence before irreversible setbacks occur. This paper details the framework’s architecture, outlines a methodology for its application, and considers its potential to redefine environmental advocacy by transforming data into pre-emptive action.