Unlocking RPA ROI through Automated Process Discovery
Discovering the right processes to automate is critical for any enterprise that wishes to unlock the best possible return on investment from robotic process automation. There are many processes ripe for automation in large enterprises – the challenge is to find the best ones. An automated, data-driven solution – such as NICE’s Automation Finder – is the key to getting it right.
Automating process discovery – the key to scaling and succeeding with RPA deployments
Organizations around the world are embracing automation in an effort to meet rising customer expectations, increase efficiencies and offer a better employee experience. With the work from home and physical distancing requirements imposed by COVID-19, many are speeding up their automation journeys as part of their wider digital transformation strategies.
Even under pressure to step up robotic process automation (RPA) and attended automation deployments, it is critical to get the basics right. This begins with choosing the right processes to automate between the multitude of process candidates across the business. This is where process discovery comes into play. The decisions that an organization makes in process discovery will impact the overall success of the RPA project.
Many companies are still using manual methods to identify and prioritize which automation opportunities to focus on first. But process discovery tools such as the NICE Automation Finder offer an approach to finding and picking processes for automation that is more accurate, automated, and scientific.
Process discovery solutions are machine learning-based tools that help organizations identify business processes, record all possible variations using machine learning algorithms, and make recommendations for automation. Not only do process discovery tools distinguish business processes that can be automated—they also help design automation workflows, making the mapping, planning, and implementation of new automations quicker and more efficient.
Why is automated process discovery so important?
A typical organization will have hundreds of processes to pick from when it embarks on the RPA journey. Some are riper for automation than others—for example, those that are highly structured, repetitive and frequent can often be automated for a rapid return on investment (ROI). But identifying and prioritizing the right ones isn’t always simple.
The traditional way to do this would be to brainstorm with business stakeholders, observe how employees work and interview experts to get a view of which processes are best suited to be automated. This approach is subjective, relying heavily on human judgement and trial-and-error to discover candidates for automation. It’s also expensive and time-consuming, slowing down the momentum of the RPA program.
Another approach is to ask expert users to record themselves carrying out a business process. This method’s failure is that it does not capture the variations in how different employees execute a process, nor does it offer insight into work carried out across other business processes.
Automating process discovery addresses these concerns by offering an objective, data-driven approach. This enables organizations to reduce the costs and risks, improve the performance and quality, and speed up the deployment of process automations. It also ensures that good candidates for automation are not missed.
How process discovery works
A robust process discovery solution will be driven by intelligent cognitive technologies such as desktop analytics and unsupervised machine learning. It will use these advanced technologies to analyze employee desktop data such as keystrokes, mouse selections, applications used, pages visited, field entries, and handle time.
The solution is scalable enough to collect data about millions of employee desktop actions throughout the enterprise. The tool will then categorize and structure the untagged and noisy data, finding meaningful sequences that can be sorted and tagged to classify employee actions with automation potential. From this data, it will use machine learning to accurately identify process paths and operational process candidates that are well matched with automation.
These automation opportunities are then scored and ranked according to the potential ROI they could deliver. Business analysts will have a precise view of the value that the process automations will bring to the organization within the context of the business. In the next step, the recommended process sequences are seamlessly built using a process design tool.
Process discovery doesn’t just take place at the beginning of an automation strategy—the solution will continuously map and prioritize processes for automation. This ensures the enterprise has a reliable stream of RPA friendly-process opportunities to consider in its pursuit of higher levels of operational efficiency.
What are the benefits of process discovery?
NICE’s Automation Finder takes out the guesswork in identifying a process that is suitable for automation, mapping how it is done and designing the automation workflow. The organization can make automation decisions based on accurate and relevant data and find process sequences or variations that are not obvious to the human eye.
Automation Finder can thus drive the automation with the biggest ROI potential based on several parameters, such as: frequency, process handle time and manual action types. Automation Finder assesses potential payoff and prioritizes process sequences by considering how long a process takes, how often it’s executed every day, how many repetitive tasks it includes, and how many employees regularly perform the process.
Automation Finder is a long-term solution, matched with the evolving nature of any organization’s operational make-up. It is designed to ensure that the best process opportunities for automation are continually sourced and presented. Not only does it help an enterprise to start its automation journey with the right moves, it is there every step of the way to ensure ongoing improvement and optimization.