How to Drive the Right Outcomes with AI for Your Products

AI practitioners are all too familiar with statistics that over 80% of AI projects fail. A lot has been said about what organizations and data science teams can do to increase this low success rate. Nonetheless, even organizations with established machine learning (ML) practices and high-end AI teams struggle. Some AI initiatives become transformational for the business, while others show little return on investment or never even come to fruition. Of course, this isn't unique to AI projects, but since data science is a fairly new discipline, there's another factor impeding success: not everything can be solved with AI.