Profit from AI and machine learning—The best practices for people and process

For projects employing machine learning or deep learning, the acid test doesn’t come from making that first “win.” Instead, the true test is making successes from embracing AI consistent and repeatable. Like most new technology innovations, for AI, the spotlight has initially been on the technology. Because the AI practice in the enterprise is still in its infancy, there is less knowledge about the “soft” side: understanding how to build the teams of people that make AI happen and creating the processes that can make success repeatable. Dataiku and Ovum are collaborating on a jointly sponsored primary research study to address the knowledge gap on “the soft side” of making AI work for the business, conducting a qualitative survey of specially selected leaders and practitioners in the field, including chief data officers, chief officers and directors of data science, and chief officers and directors of analytics. Tony Baer and Florian Douetteau summarize the lessons learned from this research and identify best practices for ingraining AI into the business, based on actual experience in the field.