They’re typically viewed as technology projects when they should be seen as organizational ones.
Nearly 9 in 10 enterprises increased annual spending on machine learning and AI projects in 2021, according to a DataRobot report. But their potential ROI is far lower than it could be, says Eric Siegel, a machine learning expert and author of “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.”
Most projects stall out before deploying machine learning models, according to Siegel’s January 2022 survey of data scientists.
In a recent interview with Workflow, Siegel explained why leadership, not technology, is to blame for the high failure rate, and what CIOs and other senior executives can do to reverse the trend.