Iterative Minimum Viable Product Approach to Implementing AI, RPA, and BI Solutions

Abstract

Breakthrough technologies can be considered as exponentially disruptive to organizations across industries within the last few decades of the 21st century, as they have significantly altered the way their business units or customers operate. Artificial Intelligence related cognitive technologies are some of the latest disruptive solutions currently being adopted by organizations. Organizational leaders may feel both the pressure and excitement of adopting such nascent technology quickly and at scale. However, due to organizational knowledge gaps of nascent solutions, transformative large-scale initiatives have a higher risk of negative impact on failure to implement. On the other hand, an iterative approach allows for the implementation to occur in smaller amounts and leaves room for incorporating feedback and lessons learned in future iterations, thus mitigating the risks involved with the undertaking. This article breaks down the nascent field of advanced cognitive technologies into three main categories based on their business use cases: process automation, cognitive insights, and cognitive engagement. It then explores implementing this technology in each of its three categories through the lens of a popular iterative product lifecycle management approach (i.e., the Minimum Viable Product) to reduce the risk of failure or other negative impacts on an organization adopting cognitive solutions.