Alex Kozlov's blog

Getting Smarter with RPA

Machine learning capabilities allow a bot to identify a mistake and apply a fix.

As the Robotic Process Automation (RPA) market matures, enterprises are taking stock of lessons learned and exploring ways to take existing RPA capabilities to the next level.

Early days were characterized by excitement over the dramatic productivity and cost-saving benefits enabled by RPA. Over time, however, the limitations of rules-based bots have emerged. For one thing, basic RPA tools can’t adjust to new conditions or changes in their environment. Even the slightest deviation from the process they’re trained to follow triggers an exception that requires a human to step in, thereby sapping the solution’s productivity.

Another issue is the complexity surrounding deployment of RPA bots. While instructing a bot to perform a task is relatively easy, it does involve a level of programming expertise. Most end users of RPA are on the business side and lack the requisite technical knowledge. That means that setting up a bot requires an RPA programmer. Demand for RPA skills, meanwhile, is through the roof.  (Witness the volume of urgent “we’re hiring” notices on LinkedIn pleading for people with Automation Anywhere, Blue Prism and UiPath certifications.) As a result, because the intervention of scarce technical resources is required, bottlenecks often occur when deploying a bot for a business user.

Alex Kozlov, Director of Content for Softtek US & Canada