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Use of Machine Learning and Computer Vision to Make RPA More Effective

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RPA simply executes its programming, so if requirements change, it needs to be reprogrammed, whereas machine learning is more dynamic.

Machine learning relies on large data sets to inform computer systems how to make decisions. An exciting advancement in the automation space is the integration of these capabilities, where RPA becomes the engine that accelerates ML, NLP and AI capabilities with the ability to produce an output at scale.”

Tommy McEvoy, Senior Lead Technologist in the AI practice, Booz Allen Hamilton

By having RPA rapidly clean and feed data into a machine learning algorithm, an organization can achieve a fully automated solution. For example, Booz Allen developed fully automated service solutions that can capture a customer’s refund requests over the phone, transcribe that information, classify the customer’s intent and then translate all of that into an appropriate trigger for the automation.

A true automation platform includes RPA and machine learning, as well as decision management frameworks and event architectures to trigger actions. RPA has driven a significant rise in document extraction technologies, systems integration and process mining. I think all of these things together are what you need for intelligent automation, but certainly, RPA and machine learning are a big part of it.”

Bill Lobig, VP of product management, IBM

Computer vision and RPA

Genpact, a global IT technology services company uses computer vision to make RPA more effective and more applicable to a wider range of use cases. The company also pairs machine learning with computer vision to discover and mine existing business processes, as well as their deviations and variations. The company also uses machine learning to look at RPA engine log files to determine the root cause of issues that need to be resolved in RPA.

“We use the computer vision capability a lot because there’s a lot of unstructured data sitting in PDFs and other things. We use ML for three things: designing the [process automation] configuration rules, execution and eliminating systemic upstream issues that drive downstream problems.”

Sanjay Srivastava, CDO, Genpact

Srivastava also underscored the need to build a data foundation, which some organizations overlook.

“I find people jumping into RPA without having thought through that. Data science professionals know they can’t get a thought out of the garage unless they have a database structure set up, so I would not lose focus on that in the context of RPA. The true test of RPA is not around automating the stuff that you know, it’s about the stuff you didn’t know was happening. Process discovery and process mining are central to figuring out the footprint, which is a massive opportunity for data scientists.”

Sanjay Srivastava, CDO, Genpact

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