In my previous post i.e. Do you really need RPA in your Business? I
stated the high level criteria for any enterprise automation seeker to
classify a process as eligible for automation or not.
Automation can also be classified into various stages. Some process
automation only require partial automation or automation of certain steps
within a workflow which is now popular by the name of desktop automation or RDA
(Robotic Desktop Automation), Where as other business cases are more of
enabling the organization wide systems with power to take autonomous rule based
decisions which is popularly know as AI or Artificial Intelligence.
The objective of this post is to understand the stages in the journey from
RDA to AI and what are the criteria to achieve each state.
1.
RDA (Robotic Desktop Automation):
The basic need for any enterprise that is very new to automation is that
it wants to reduce the redundant work for its work force or increase the
output of the workforce. This need is generally answered b automation of
certain steps in the tasks which are conducted by the workflow. These steps may
still need human intervention as the decision making is done by human beings.
- RDA is process driven
- Manual intervention is
needed as trigger is human initiated
- Decision making is done
my humans
- Some steps in a workflow are
automated to reduce tasks which were earlier done by human beings
- Examples are copy pasting
data from excel to form or vice versa
2.
RPA (Robotic Process Automation):
The next obvious step is to allow the process to take decision to
trigger the event so that human intervention is reduced even further. Thus this
level of automation intends to automate not just few steps but move towards increasing
the level of automation in the entire workflow or process to achieve a final
state of complete process automated with minimal or no human intervention
- · RPA is also process driven
- · Triggers are digital and self serviced
- · Intent is to automate the end to end workflow
- · Triggers are rule based
- · Examples are digital customer journey for opening of a new bank account
3.
ML (Machine Learning):
The next successive state for
any enterprise in this journey is to allow the system do make decsions based on
previous decisions made by humans. The in the previous two states we have seen
that the triggers or decision are either made by humans or based on rules made
by humans. This state however takes it to a new level where the decisions are
made based on the data and decision rules are selected by machines based on the
data.
- · ML is data driven
- · Perspective analysis and decision engines are at the heart of this stage
- · Triggers are based on historic data, rules are made based on historic data
- · Its not focused on one workflow but is capable of enterprise wide transformation
- · Examples are running decision engines and data analysis on customer journey’s and repeat purchase to come up with decision on product positioning and marketing mix
4.
AI (Artificial Intelligence):
The ultimate state for an enterprise
is to be able to create intelligent systems which can not only make decisions
but are also able to assist its stakeholders in making intelligent decisions. Intelligent
systems can not only make decisions based on historical data but also are
capable to deducing future events
based on current set of steps and information from past.
- · AI data driven
- · Deductive analysis is the heart of this stage
- · System is intelligent to guide humans to take certain steps
- · Examples which I can think of “Jarvis” from Age of Ultron
