Today, the Dismantle, Inspection, and Failure Analysis (DIFA) process for electrical submersible pump (ESP) failure analysis is a tedious, human-intensive, and time-consuming activity. The activity involves a set of data and various information formats from several activities in the ESP operation lifecycle. This paper proposes a novel artificial intelligence workflow to improve the efficiency of the DIFA process using an ensemble of machine learning (ML) algorithms. This ensemble of algorithms brings together structured/unstructured data across equipment, production, operations, and failure reports to automate root-cause identification and analysis post breakdown. As a result, the time and human effort required in the process has been reduced, and process efficiency has drastically improved.