From early deployments in the financial services sector, RPA has now proliferated to all industries and been boosted by the growth of hyper-automation offerings that integrate artificial intelligence (AI) and machine learning into process automation.
As a result, the RPA market surged 63% to US$1.4 billion in 2019, holding its position as the fastest-growing segment in the enterprise software market for a second year, according to Gartner. This growth shows no signs of slowing; looking ahead six or seven years, compound annual growth estimates for RPA range from 25% to 40%, and more.
In short, no organization can afford to ignore the potential of this disruptive yet intelligent technology.
RPA enables organizations to streamline processes, reduce errors and delays, and free their people for higher-value work, creating a compelling return on investment proposition. COVID-19 has also put a business continuity twist on this calculation. The overnight shift to digital operations and work-from-home business models exposed business processes that relied on inefficient manual ways of working.
At the cutting edge of RPA today is hyper-automation, or what SAP calls intelligent RPA. It goes beyond automating tedious tasks to tap into data across multiple systems and uses AI to empower the RPA bot to make decisions that cannot be modelled within a rule framework. This helps to safeguard business continuity, prepare for future growth, and improve employee and customer satisfaction.
However, RPA projects are often more complex and time-consuming than expected. It is even possible for RPA to amplify existing problems, as when an inefficient process is automated without first being improved. New issues can also arise if workflows are fragmented into RPA silos instead of seamlessly linked.
Here are four steps to avoiding these pitfalls:
- Start simple: It is advisable to begin RPA by automating a repetitive task to solve a defined problem with the backing of your end-users. The finance team is often a great partner in this endeavour—able to identify inefficient manual processes that involve the entry, manipulation and re-entry of data.
- Unpack the process: The key to any RPA project is to begin with a thorough understanding of how the current business process works. Companies can now mine processes through technology, which is intelligent enough to comb through core systems and raise alerts of potential bottlenecks. Such audit trails will identify areas for improvement at which companies can reliably automate processes based on data and system logs.
- Follow the business flow: To bridge different processes, especially where human intervention is required to approve an invoice or advise a customer, you will need to be able to collect, consolidate, and manipulate data from multiple third-party sources, and to trigger actions from one bot to another. This will require combining several technologies, including RPA, AI, and natural language processing, to automate the workflow and achieve efficiency.
- Build your bots: The quality of bot development is crucial to intelligent RPA. Ready-to-deploy bots and connectors speed up the development process and a new generation of low-code/no-code development platforms is making it possible to create robust and intelligent RPA solutions with little or no programming required.
Providing graphical development tools, this low-code/no-code environment lets those who actually use the business processes get involved with RPA bot development. This frees your developers to focus on quality-proofing the bots and assuring security standards, among other value-added tasks.
Promising to automate processes and augment people, intelligent RPA is all about minimizing unnecessary human effort and maximizing human value. Recognizing this and taking steps to implement RPA without delay will help you offer more rewarding work and make your businesses more successful.