ML and AI projects are technology-heavy, data-rich and rely on increasingly large teams with deep technical skills and expertise. As more companies make larger investments in ML and AI, the pressure to succeed in these complex projects is increasing. Still, many of these projects fail. They fail not because the technology fails, not because the data is poor or because the team lacks skill but because they were not set up for success. The way the project was conceived, framed and begun destined them for failure. In this session to kick off the business track, you’ll learn how to set ML and AI projects up for success by getting that first step right. You’ll see how to frame the problem correctly, learn why step 1 has to be business-led and understand why deployment and operationalization depend on getting step 1 right.