Defined as the application of robotic process automation (RPA) along with artificial intelligence (AI) technologies such as machine learning, natural language processing, and computer vision), Intelligent Automation (IA) is bringing fundamental changes to how enterprises operate. Both the work (i.e., business processes) and the worker (i.e., resources) are evolving at breakneck speeds. The workforce now includes machines with software intelligence that enable enterprises to handle unstructured data rapidly, achieve savings within 6-9 months, 100% ROI by the second year, and above 90% efficiency gains. Beyond these hard business benefits, IA brings new skills that enable people to develop entirely new processes, essentially carving out new directions to expand what’s possible for their enterprises.
Despite the purported ease of implementation of IA, several engagements do not achieve the returns that enterprises expect. These are the common pitfalls enterprises encounter when trying to scale IA:
This article talks about the Leading-edge enterprises know that Intelligent Automation is crucial to managing the increasing complexity of business processes and the increasing volume of business data. However, the road to a successful IA implementation is not smoothly paved. As we pointed out in the six pitfalls above, enterprises need to be very astute about their strategy and the associated choices they make. A well thought out plan that clearly sequences out the processes to be included in different phases of the IA implementation while also setting the right expectations for all enterprise stakeholders is a cornerstone of success. The choice of both the tool and service provider partnerships is the next most important decision that will spell the difference between an implementation that advances the enterprise towards competitive advantage, or one that erodes internal confidence in automation and essentially take the enterprise backwards.
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