In the past two years, Robotic Process Automation (RPA) has been one of the fast-growing tech trends. It’s not surprising: RPA is a major digital transformation enabler that can automate several manual tasks. Yet, several studies are reporting that enterprises are failing to scale and industrialize after deploying just a few bots, with up to 50% of RPA deployments initially failing.
Why is your RPA program not scaling?
For enterprises, scaling their automation program is proving more difficult than anticipated. The reasons are many, including dynamic processes, the tech ecosystem is becoming complex, gathering a team with the right skills is difficult, it’s getting harder to navigate through new privacy regulations, legacy systems, and tighter budgets.
While there is no one “how to scale RPA” guide to help you tackle all the challenges, we’ve found that continuous process discovery is critical for furthering any change initiative and leverage RPA to the fullest.
RPA is not a one-time deployment. The bots need to be maintained and improved regularly to ensure that you get the best returns out of your investment. You need to revisit your business processes regularly to identify gaps. This is where continuous discovery comes in.
Uncover hidden process variations
Process variations are the hidden data points that are hard to capture through manual discovery approaches. You need to understand the why and when of it to design a bot that can automate the task. Without continuous discovery, you can easily miss exceptions and variations. As a result, a bot that was working perfectly fine a week ago could break, making it only a band-aid solution rather than a long-term solution.
Continuous discovery ensures that you are not automating just to make inefficient processes faster. It allows you to account for the additional complexities of processes to ensure the bots continue to function in the dynamic environment and produce the desired results.
Stop automating piecemeal tasks
Piecemeal automation remains one of the most common stumbling blocks to scaling the RPA initiative. Continuous process discovery powered with automatic capture and visualization of end-to-end processes provides enterprises with a deeper understanding of their business. It shows where processes fail, how long does it take to execute, where are the gaps and opportunities for improvement. This level of intelligence will help businesses automate the entire process rather than small tasks.
How SurfaceAI enables continuous discovery
RPA could be the “future of business”, but SurfaceAI is a real game-changer in how enterprises uncover “AS-IS” processes continuously to plan for sustainable and profitable automation. Here’s the four-step process you can follow:
Discover: Get full visibility into end-to-end workflows across multiple users, processes, business functions, and applications your teams use.
Visualize: Once the process workflows are discovered, they are presented as process maps to quickly convey important information to business analysts, executives, partners, and subject matter experts.
Analyze and enrich: Identify key people-process-technology data points that impact execution. Add custom notes and taxonomy to enrich the captured data. Use the insights to lay a strong foundation for optimization and improvement initiatives.
Document: Automatically generate detailed documentation for training and compliance purposes. Leverage the captured metadata to “automate the automation”.
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