How Can AI Super Charge RPA?
Blog: Jim Sinur
We have seen a rush to automation for time, money, and elimination of grief. Robotic Process Automation (RPA) has been on the cutting edge of this wave of automation benefits. RPA is changing its focus from just automating assisting or eliminating mundane work to seeding automated worker bots into processes and systems. The level of intelligence of these bots or agents is going up, thanks to AI. This post will concentrate on the new kinds of work that RPA is taking on now and in the future.
Today without AI
Today RPA is good at mundane tasks and reducing nasty work for people. Typical tasks would include auto-keying, screen/form integration, application or data integration, automated decisions, and rudimentary task management. It can reach to simple task sequencing and simple resource orchestration.
Today with Machine Learning, Mining & Analytics
As RPA moves towards straight-through processing, it will have to do forms of event and pattern recognition, informed decisions, and smart actions. This means that process instances, events, and other forms of journey data will have to be inspected and learned from at greater speeds. RPA may also act in an unsupervised fashion to adapt to change.
Tomorrow with NLP and Unstructured Content
Tasks that are knowledge-intensive will also need the help of the combination of AI & RPA. Natural Language Processing (NLP) can search structured and unstructured data for the presence of knowledge represented by the presence of entities and relations. This emergent knowledge can be captured by flexible knowledge taxonomies that can be leveraged in journeys, processes, or systems. The mix of unstructured data will also likely include image, voice, and video
Tomorrow with Deep Learning & Deductive Analytics
As AI advances to include right-brained activities such as judgment, particularly in context, RPA can make informed decisions based on leveraging the combination of AI & analytics. Deductions can be made after integrated information sources and knowledge worlds are run through advanced algorithms to take smarter action that considers multiple contexts.
Tomorrow with Cognitive AI & Predictive Analytics
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.
Net; Net:
As the combination of AI & RPA progress over time, the agents/bots will play a bigger role in making more automation possible to the point of completing more complicated work and shifting more satisfying work to human partners. At a minimum, these automation agents will assist employees and customers better than before.
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