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Looking beyond RPA to intelligent automation

The automation journey can be long and arduous for many organizations. Beginning with robotic process automation allows organizations to immediately find opportunities to increase productivity and accuracy in the back office. This is, however, just the first stage of a process that can increase operational efficiency and create even more value across the organization.

This stage is defined by technologies that build on RPA: intelligent automation. These technologies provide a structured output, which is exactly what RPA bots require in order to be at their most efficient.

Intelligent automation technologies provide the following features:

  • Image Recognition, which can review an invoice or confirm an identity via facial recognition.
  • Natural Language Processing (NLP), which can understand the intent in a fixed body of text.
  • Cognitive Reasoning, which can make a complex decision based on various data points.
  • Data Analysis and Extraction, which can find patterns in data sets or extract information from a contract or other legal document. When orchestrated correctly, intelligent automation technologies are enabling companies to automate processes that – until now – have been un-automatable.

An industry currently reaping many benefits from intelligent automation is banking. Take the process of curbing money laundering, for example. In order to prevent illegal behavior in the banking industry, previously an agent must cast his or her eye over a number of documents, confirm the address and identity of the individual and check documents like bank statements, utility bills, passports or driving licenses.

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An attendee working on a laptop computer participates in the Yahoo! Inc. Mobile Developer Conference Hackathon in New York, U.S., on Tuesday, Aug. 25, 2015. The Hackathon is an opportunity for mobile developers to come together and hack around the Yahoo! Inc. Mobile Developer Suite. Photographer: Victor J. Blue/Bloomberg

The challenge is that the bank must not only scrape information to compare against an application, it also must check the validity of the documents. This requires more than standard optical character recognition (OCR) technology. In this context, a bank would need an image recognition algorithm built specifically to highlight traits in a document that may point to its fraudulence.

Where RPA Can’t Go

Another way to understand the value intelligent automation can bring to an enterprise is to understand the limitations of RPA. While RPA can take the pain out of repetitive and mundane processes and reduce errors and improve compliance, it cannot read text, communicate with a person, interpret a contract, scrape information from an invoice, make a complex decision or improve over time. This is why automation centers of excellence around the globe are starting to think about building cognitive capabilities into their RPA initiatives.

When a company adds cognitive capabilities like cognitive reasoning, NLP and image recognition to RPA, they can automate nearly double the number of processes in the back office and explore new areas of the business for opportunities to automate, including in customer service, legal and marketing.

For many, customer service automation is the holy grail of automation because of its ability to offset the significant costs associated with employing people to respond to so many different contact channels. In fact, some organizations are looking more seriously at bringing service centers back home, which they can only do with the help of intelligent automation in the form of conversational AI in the interactive voice response (IVR) or chatbots – an idea that is no longer pie in the sky, especially since Google’s recent Duplex demo.

Even as it is becoming easier to find effective intelligent automation in today’s market, it is an essential first step to identify the problem to be solved. Buying only a hammer risks making everything look like a nail – and buying the tool first may mean you miss out on the most effective solution. Identifying the potential may only require breaking the process into characteristics, like “look,” “read,” “copy” and “decision making.”

Soon it will become apparent where intelligent automation technologies can work together to create an eco-system of capabilities, which, in turn, can enable more end-to-end customer journeys to be automated.

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