There are three fundamental pillars of AI adoption: data, technology, and people/ culture/ process. In my experience working with many of the largest organizations in the world, I see a consistent pattern: they are willing to invest in cutting edge projects with the power to revolutionize their organization, but they struggle to operationalize these projects. Approximately 40 percent reach implementation, while 60 percent stall or flame out.
Why is this? It’s because business leaders do not pay attention to crucial change-management processes that need to be considered from the very beginning. If you don’t think about how to operationalize machine-intelligence at the start of a project, you won’t be able to transform your business or realize ROI.
As an executive and a leader, a big part of your role is managing the change that must, by necessity, take place if your organization is to embrace data science applications. Change management is the vital step in operationalizing data-science projects and transforming your organization into a data- and model-driven enterprise.
Read the full post from Nir Kaldero on Grit Daily here.