“In three to five years, RPA and AI will become a commodity productivity tool, the same way as you use Excel and PowerPoint.”

Boris Krumrey, chief robotics officer, UiPath (source)

RPA is being leveraged across multiple industries to automate high-volume, repetitive (and rules-based), and digitized processes, allowing workers to focus their valuable time on more customer-centric and value-added activities. For example, instead of Finance or Customer Care teams aggregating data for reports and system updates, the teams can focus on extracting insights from the report and isolating the root causes around problems. Analysts and agents who use robotic process automation can spend their time solving for root causes of discrepancies in their reports and spend more time focusing on a customer’s sentiment rather than navigating between three or four systems looking for his or her records.

By combining RPA with cognitive tools and techniques such as machine learning, computer vision, and natural language processing/generation, organizations are now automating higher-value, more complex, tasks that in the past required perception and judgement from humans. In other words, robotic process automation is bridging the gap between AI use cases and current business processes.

Robotic Process Automation Implementation

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Robotic Process Automaton Use Cases

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Here are just a few examples of processes that are ideal candidates for robotic process automation:

“Automation will create 14.9 million new jobs — and transform 85% of existing ones — by 2027.”

Forrester, Reinventing Work in the Era of Automation (source)

Process mining accelerates the value from RPA

RPA can yield a host of productivity and cost-reducing benefits … but only if it’s applied to the right processes. One key thing to remember: NEVER automate a broken process. If a process is slow, wasteful, or otherwise ineffective, implementing robotic process automation will only add a layer of complexity that yields little if any improvement. If the process can be re-imaged to shorten time, mitigate risk, and take substantial effort away from employees, then the true value of RPA at scale can be realized. Therefore, organizations must carefully evaluate their automation opportunities, and a good place to start is with process mining.

Process mining extracts and analyzes event log data from business systems to identify patterns and uncover opportunities by highlighting inefficiencies (e.g., where steps take a long time, or where hand-offs cause bottlenecks, or where key information is stored in multiple places). Instead of guessing how their processes run (or relying on ideas of how they should run), businesses can see what actually happens based on real-world data.

Several process mining tools are available today, and all leading RPA tools are designed to integrate with them. In fact, process mining capabilities will soon be embedded directly within RPA platforms such as UiPath’s Explorer.

Process mining allows organizations to discover inefficiencies and, in a data-driven manner, identify root causes of deviations. With this information in hand, the business can take steps to remove friction from their processes and set their RPA implementation up for success. In addition, process mining can continue to analyze processes after robotic process automation is implemented and demonstrate the value of automating (e.g., showing time saved per month on a given process or demonstrating how average handle time for a request decreased by XX% using RPA).

Logic20/20 can help

Logic20/20 takes a holistic approach to robotic process automation, with a focus on leveraging technology to create better user experiences and generate business value. Our experienced digital transformation team is proficient in all aspects of RPA implementation, from opportunity assessment through implementing and scaling your Center of Excellence (CoE). We have partnerships with top robotic process automation vendors, and this enables us to select and implement the right tool for your organization’s needs.

RPA implementation process