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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