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Businesses are beginning to address process complexity as part of digital transformation automation, which is raising awareness of the impact of tasks that are dependent upon human knowledge workers have on productivity, responsiveness, and profitability. The treatments for these requirements are process redefinition, process optimization, and straight-line automation. The goals of these activities are typically better customer/user experience, reduced time to completion, higher quality data capture, and the ability to scale in a non-linear fashion. To this end, Robotic Process Automation (RPA) products offer varying levels of support to replicate human-led activities as machine-based processing.
The aforementioned treatments have critical importance to the evolution of the RPA product industry and the value of RPA tooling as part of your digital transformation. For example, if a business undergoes a process redefinition effort, they may design away the requirement for human intervention through new business software and redefined activities. Hence, process redefinition may minimize the need for RPA tooling, whereas straight-line automation might only be possible with the combination of tools provided via an RPA product.
Current market trends illustrate that businesses are opting to adopt straight-line automation as the primary focus of their transformation activities, attempting to address “low-hanging fruit” objectives with a high return-on-investment. This bodes well for the immediate future of RPA products. However, regardless of how much machine intelligence is available, automation is susceptible to breakage and this means that some investment must be made to deal with exception handling and maintenance of the automation.
The other key factor affecting adoption is the rise of the citizen developer and the democratization of automation technology. In the past, RPA solutions required a significant amount of technical expertise and programming experience. Today’s tools are being designed for use by the more technology-savvy office productivity worker. That is, those capable of development of spreadsheet models and word processing macros can automate some of their own mundane tasks, and even be able to incorporate sophisticated artificial intelligence. While we are seeing modest growth of the citizen developer it is enough to raise the concern for the need for technology governance surrounding these automated tasks.
As stated above, RPA provides the means to rapidly transition analog, human-led activities into automated digital activities. Precipitating this need is a mix of physical constraints, such as the need to process unstructured content as is often the case in financial services and healthcare, and legacy systems accessibility. Ultimately, the money being saved by automating issues related to legacy systems accessibility should be reinvested in modernization efforts that will reduce reliance on RPA products for this purpose. Hence, the longer-term success for RPA is going to rely on the vendors’ ability to move into other markets such as integration, low-code application development, and analytics.