The rise of algorithmic marketplaces has transformed consumer experiences while simultaneously creating new vectors of manipulation, such as dark patterns and algorithmic persuasion. Traditional consumer protection frameworks, built for slower and more transparent forms of commerce, struggle to keep pace with these high-velocity harms. This chapter examines the promise and limitations of automating consumer protection enforcement through artificial intelligence (AI). It argues that while AI can effectively detect large-scale patterns of unfair trade practices, such as deceptive user interfaces or misleading advertisements, legal interpretation of what constitutes “unfair” or “misleading” remains inherently context sensitive. Drawing on jurisprudential theories of ambiguity and the fractal nature of law, the chapter contends that full automation of legal enforcement is neither feasible nor desirable. Instead, it advances a hybrid “judge-in-the-loop” model: AI-powered systems conduct large-scale detection and triage, while Online Dispute Resolution (ODR) platforms ensure rapid, human-reviewed adjudication. Through this framework, regulators can achieve scale without sacrificing due process, transparency, or accountability. The chapter situates this proposal within India’s evolving consumer protection regime, offering a model of coregulation that balances algorithmic efficiency with human judgment.