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Fast, efficient and accurate triggers are a critical requirement for modern high energy physics experiments given the increasingly large quantities of data that they produce. The CEBAF Large Acceptance Spectrometer (CLAS12) employs a highly efficient electron trigger to filter the amount of data recorded by requiring at least one electron candidate in each event, at the cost of a low purity in electron identification. However, machine learning algorithms are increasingly employed for classification tasks such as particle identification due to their high accuracy and fast processing times. In this proceeding we present recently published work that showed how a convolutional neural network could be deployed as a Level 3 electron trigger at CLAS12. We demonstrate that this AI trigger would achieve a significant data reduction compared to the conventional CLAS12 electron trigger, whilst preserving a 99.5% electron identification efficiency, at nominal CLAS12 beam currents.