Automated Machine Learning (AutoML) promises to democratize AI development by automating the traditionally labor-intensive processes of feature engineering, model selection, and hyperparameter optimization. This research examines the economic conditions under which AutoML delivers positive ROI, drawing on empirical data from 47 enterprise deployments. Analysis reveals that AutoML achieves positive ROI in 62% of cases, with success strongly correlated with use case characteristics rather than organizational size. A validated decision tree methodology enables organizations to predict AutoML ROI with 78% accuracy before investment.
Oleh Ivchenko (Sat,) studied this question.