Applying Design Thinking for More Valuable Machine Intelligence

Analytics is traditionally viewed as a technical field. Professionals in the machine intelligence space almost universally hail from engineering or mathematics backgrounds. This specialization can pose challenges to internal collaboration and unltimately lead analytics investments to miss their stakeholders' expectations. In this presentation we will review organizational considerations, processes, and tools which can be used to bridge analytical and non-technical teams through the application of design thinking. From within the walled garden of analytical expertise, it can be difficult for researchers and engineers to fully understand and incorporate the needs of their stakeholders. Likewise, non-technical consumers of machine intelligence may feel that while their technical needs have been addressed, the end-product misses the mark. The disconnect between analytics producer and consumer exists across industries and applications - from consulting and internal strategy to external-facing product development. The challenge persists in the growing realm of artificial intelligence and can threaten the value derived from these new technologies. By embedding design thinking across disparate teams, managers of machine intelligence initiatives can ensure their investments in advanced technology maximize value to the end users and are well-positioned to deliver well into the future.