Artificial intelligence (AI) is a topic that has garnered substantial attention over the last few years. But while Robotic process automation (RPA) and AI are sometimes used interchangeably, they are hardly the same.
Specifically, RPA is a business process automation technology that relies on the use of software robots that can be easily programmed to automate straightforward but repetitive tasks typically done by employees. In short, RPA mimics human behavior to automate tasks in a way that can help businesses save time and streamline their processes.
This might entail a variety of rule-based activities such as scraping screen content, speed up workflows, aggregating data, respond to incoming emails, or even initiating new actions when certain thresholds are met.
RPA can seamlessly integrate existing applications such as Excel with enterprise applications such as ERP and CRM systems. Think in terms of rules that can easily create and use repeatedly to move structured data across disparate systems – such as from within an Excel spreadsheet into SAP while you sip your morning coffee.
This is quite different from AI, which is a wide-ranging branch of computer science that is focused on building smart machines that can mimic human intelligence. A variety of niches exists here, including machine learning, expert systems, natural language processing (NLP), computer vision, among others.
Using them together
However, there is growing momentum to leverage RPA and AI technology together to advance business process automation. By integrating AI with RPA, an autonomous process could come up with a cognitive response that is sent directly to the RPA system where the task is completed.
This can result in tangible benefits not possible with only one of them. For instance, the wait time for a customer query issued during non-business hours through traditional channels such as e-mail can be substantially reduced, or urgent queries involving time-sensitive matters can be moved to the head of the queue. The result is greater customer satisfaction.
Elsewhere, RPA can be improved to act in an unsupervised fashion in certain contexts, tampering the rigidity of RPA with the flexibility of AI systems. AI-centric systems such as pattern recognition and expert systems can be put to work to further extend the capabilities of RPA.
Moving into the future
What are some capabilities that the amalgamation of AI and RPA is likely to give us? In a post on Forbes, contributor Jim Sinur noted that AI can help RPA move beyond today’s “simple task sequencing and simple resource orchestration”. Processes, events and other forms of data can be inspected, and insights rapidly gleaned and immediately piped to RPA systems, he argues.
Ultimately, deductions can be made by amalgamating integrated information sources and pools of knowledge using advanced algorithms for smarter actions, and in a way that takes multiple contexts into consideration.
“As AI can learn to think, learn, and project by employing predictive analytics, RPA should be able to intercept exceptions and match these patterns or events to expected or unexpected, opportunities, and threats. This puts organizations in a position to think through and respond to emergent behaviors and markets.”
For now, it is hence unsurprising that businesses have jumped on the RPA bandwagon, and its market size is set to exceed USD5 billion by 2024. As AI makes its inevitable way into RPA, expect this number to further increase.
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