The convergence of language, social media, and digital activism has become a radicalizing agent in the contemporary age as far as it concerns the development of the format of public policy and the impact on corporate strategies worldwide. The increased usage of online spaces has pushed activism beyond that which is enacted within physical locations and more into action through the instantaneous spread of data, slogans, and hashtags organizing groups of people across geographical regions. This paper discusses the role of the artificial intelligence (AI) technologies such as natural language processing (NLP) and sentiment analysis among others in enabling the identification, amplification, and framing of activist discourse, and how this has in turn affected the policy-making process and industry responses. Applying both methods to a total of 2.4 million social media posts in five high-profile advocacy movements, we developed a mixed-method framework of relevance to this paper by combining both machine-learning-based linguistic pattern recognition and network-based mapping to identify influential actors, theme strata, and sentiment trends. The results indicate that discourse analysis coupled with AI can accurately demonstrate early changes in relative momentum in digital activism, anticipate policy debates with stratospheric accuracy and even foretell industry reaction with reasonable accuracy. In addition, we show that language selection, varying between appeals based on emotions and arguments based on data, influences the probability of engagement, press coverage and even ultimate policy adoption. The paper discusses how AI is used both as a tool of analysis and as an instrument of power in contemporary activism and outlines serious concerns regarding the very nature of algorithmic bias and ethical nudging, as well as the relationships between advocacy freedom and the control of misinformation..