From Desktop Automation to Artificial Intelligence - A journey

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

Return on investment for digital transformation initiatives

The dynamic technology environment today has left the CXO in a midst of a quagmire as the CXO has a limited technology budget and endless potential investment opportunities in the field of technology which could improve the performance of the bank. Clearly there is a need to get at least some guideline on the kind of returns on investments which the bank would be getting for any investment made in the field of technology. 

In this rapidly digitizing ecosphere businesses are still at a nascent stage where most of the industry verticals although now realize that the future is digital but they are either not yet technologically ready to embrace it or have just started assessing the impact of this radical shift in the business models is impacting them and started reacting, there are exceptions though. Banking vertical being most technically inclined to digitization as compared to vertical like Energy or Manufacturing, becomes the obvious choice for this white paper. Every well-known bank today has at least one substantial size project in pipeline where it is trying to improve its digital footprint in one of the three areas i.e. Customer Relationship, Operations or Partnering with Other players in its ecosystem.

It clear that any digital transformation initiative is fruitless unless there is a clear buy in from the top management, in fact most successful digital transformations initiatives have been the ones which have been initiated from CXO down. It is imperative for any organization to transforms its operations into a more digital state in this rapidly digitizing economy. The next question which challenges a CXO is “Which initiatives would give the best results in terms of Returns of Investment?” and/or “Where should the IT dollar be invested to get maximum bang for the buck?” and/or “What kind of returns the top management can expect from these initiatives?”. In order to enable CXO understand the results and ROI, it becomes imperative for a digital transformation advisor to evaluate to identify variables which factor to the success of these initiatives.