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Cognitive automation makes RPA even better
Cognitive RPA use cases
Handling exceptions with cognitive RPA
From cognitive RPA to cognitive decisioning
Cognitive RPA is a term for Robotic Process Automation (RPA) tools and solutions that leverage Artificial Intelligence (AI) technologies such as Optical Character Recognition (OCR), Text Analytics, and Machine Learning to improve the experience of your workforce and customers.
This highly advanced form of RPA gets its name from how it mimics human actions while the humans are executing various tasks within a process. Such processes include learning (acquiring information and contextual rules for using the information), reasoning (using context and rules to reach conclusions) and self-correction (learning from successes and failures).
Traditional RPA supports automation based on structured data. Cognitive RPA takes things a step further by enabling organizations to automate processes that include unstructured data sources, including scanned documents, emails, letters and voice recordings. The real power of cognitive automation is that it enables enterprises to automate more complex, less rule-based tasks.
Unlike traditional unattended RPA, cognitive RPA is adept at handling exceptions without human intervention. For example, most RPA solutions cannot cater for issues such as a date presented in the wrong format, missing information in a form, or slow response times on the network or Internet. In the case of such an exception, unattended RPA would usually hand the process to a human operator.
Let’s consider some of the ways that cognitive automation can make RPA even better. Firstly, cognitive RPA can be used to improve data. You can use natural language processing and text analytics to transform unstructured data into structured data. Then, an RPA system can use this data in automated processes.
Secondly, cognitive automation can be used to make automated decisions. Predictive analytics can enable a robot to make judgment calls based on the situations that present themselves. Finally, a cognitive ability called machine learning can enable the system to learn, expand capabilities, and continually improve certain aspects of its functionality on its own.
So, to sum up, the power of cognitive RPA comes from its ability to process unstructured data, such as documents and emails, and then use that data to drive higher levels of automation. Here are just a few examples of what the technology can do today:
Optical Character Recognition (OCR) and image identification
Extracting intent and entities
Text analytics
Sentiment analysis
Categorization
Classification
Voice recognition
These technologies can be put to work across a number of use cases. One example is to blend RPA and cognitive abilities for chatbots that make a customer feel like he or she is instant-messaging with a human customer service representative. Another is to create voice-powered bots for telephonic conversations.
Or, dynamic interactive voice response (IVR) can be used to improve the IVR experience. It adjusts the phone tree for repeat callers in a way that anticipates where they will need to go, helping them avoid the usual maze of options. Many email conversations can also be automated. AI-based automations can watch for the triggers that suggest it’s time to send an email, then compose and send the correspondence.
One of the most exciting ways to put these applications and technologies to work is in omnichannel communications. Today’s customers interact with your organization across a range of touch points and channels – chat, interactive IVR, apps, messaging, and more. When you integrate RPA with these channels, you can enable customers to do more without needing the help of a live human representative.
Cognitive RPA abilities enable the automated system to understand the customer’s intent, make sense of the unstructured data associated with the customer, predict behavior, and then execute a request in the backend. AI and cognitive automation can also watch the overall customer journey and weave themselves ever more effectively into it.
Consider the example of a banking chatbot that automates most of the process of opening a new bank account. Your customer could ask the chatbot for an online form, fill it out and upload Know Your Customer documents. The form could be submitted to a robot for initial processing, such as running a credit score check and extracting data from the customer’s driver’s license or ID card using OCR.
If the system picks up an exception – such as a discrepancy between the customer’s name on the form and on the ID document, it can pass it to a human employee for further processing. The system uses machine learning to monitor and learn how the human employee validates the customer’s identity. Next time, it will be able process the same scenario itself without human input.
Another instance might be an accounts payable process. Your automation could use OCR technology and machine learning to process handling of invoices that used to take a long time to deal with manually. Machine learning helps the robot become more accurate and learn from exceptions and mistakes, until only a tiny fraction require human intervention.
What should be clear from this blog post is that organizations need both traditional RPA and advanced cognitive automation to elevate process automation since they have both structured data and unstructured data fueling their processes. RPA plus cognitive automation enables the enterprise to deliver the end-to-end automation and self-service options that so many customers want.
Consider the power of integrating client-facing chat and voice agents, on top of a cognitive RPA core, for example. You can bring your own bot, or choose to integrate with one of a number of ecosystem partners such as Amazon’s Alexa. In the slightly longer-term, expect to see what is known as cognitive decisioning or decisioning automation. This means using AI to automate processes that aren’t rule-based, processes that have aspects of decision making that presently would need to be handled by humans.
With robots making more cognitive decisions, your automations are able to take the right actions at the right times. And they’re able to do so more independently, without the need to consult human attendants. With AI in the mix, organizations can work not only faster, but smarter toward achieving better efficiency, cost savings, and customer satisfaction goals.
