How Aligning AI Project Teams Can Ensure AI Success

While AI deployments are picking up steam, challenges to success, both technical and cultural, abound. One of the biggest challenges is the lack of alignment among the three pillars of enterprise AI success: business users who are closest to the data, data engineers charged with keeping the data pipes open, and data scientists who make AI work. This lack of alignment typically means that data science teams find themselves "boiling the ocean" without a clear scope, while data engineers don't know what data sets to focus on – which can lead to very disappointing results for AI projects. Only by aligning these groups around a standard data science methodology can consistent AI success be achieved. Business leaders who came out of school 15 or more years ago were never trained to take advantage of big data.