If we consider predictive analysis of case outcomes as an intrinsic component of legal automation, then we ignore critical contingencies that limit its universal application. If a jurisdiction lacks digitized and standardized legal databases, then AI capabilities are hindered, rendering this sub-component inoperative. For example, in the Philippines, where access to court decisions is often manual and fragmented, a predictive system could not operate without a prior and costly data infrastructure. If training data is biased or incomplete, then predictions may compromise fairness rather than promote it.

Imagine a courtroom in India, where the judge dreams of AI as a savior for judicial backlog. The use of AI for legal research is certainly a key actor, but its role is conditioned by broader and more fundamental forces. A tool like Manthan, as brilliant as it may be for precedent analysis, cannot operate without a stable digital infrastructure and proper training of system personnel. The true transformation of justice depends less on technological prowess than on human will to invest in these foundations, such as access to electricity for remote villages. Without these prerequisites, AI remains a distant promise, an unfinished scene in the grand theater of justice.