Repetitive tasks can be mind numbing, a potential deterrent to creativity and advancement. 10% – 20% of all human work hours are wasted on no-brainers across different industries. The IT industry loses 30% of its work hours to running scheduled checks on servers to make sure they are up and running. Finance departments lose at least 25,000 hours every year performing avoidable rework caused by human error. These tasks that we refer to as repetitive include accounting, invoice creation, transferring data from system to system, approvals, routine maintenance of servers, etc. While these are all quite indispensable for a company they hardly create any additional value, on the other hand every manually filed invoice costs a company between $5 to $25. This is where RPA or Robotic Process Automation comes in.

What is Robotic Process Automation?

RPA or Robotic Process automation is the use of software bots to automate rules-based tasks that are repetitive and thereby do not require complex decision making. In a way RPA is an improvement on assisted process automation where human workers used to be supported by programmes that could provide crucial information in real time.

RPA programmes are easy to implement and do not require the business owners to code anything. In some cases they can choose the mode of automation from a simple drag and drop user interface. RPA software can free up knowledge workers from repetitive tasks and eliminate the invariable human error here and there and all for a small fraction of the cost of maintaining a large workforce.

What RPA is not

It is definitely not humanoid robots roaming around the workplace doing one thing or the other. RPA cannot engage in decision making or predictive work. RPA systems can scrape data from databases and learn to perform simple repetitive tasks like opening an attachment, filing invoices, transferring data, and creating transaction reports. They cannot provide customer support by breaking queries made through natural language. They cannot analyze consumer data to create product recommendations for them. These tasks are what IPA is needed for.

What is Intelligent Process Automation or IPA?

Intelligent process automation is RPA that is boosted by the state of the art apparatus of artificial intelligence. When you combine robotic process automation with technologies like computer vision, deep networks, and natural language processing, it evolves into IPA or intelligent process automation. IPA systems can capture and analyze data be it situational, visual, textual or aural, and respond with an appropriate course of action.

Some examples of IPA implemented across different industries

  • In the healthcare sector IPA systems can save doctors and medical professionals hours of case study by suggesting treatment options by analyzing a patient’s medical reports in light of the immense amount of medical research data available.
  • The insurance sector can instantly nullify faulty claims by using IPA systems to analyze agreements. For instance, a health insurance provider in the USA recently implemented IBM’s Watson Cognitive Computing technology. It took the robot 15,000 hours of training to be able to match unstructured data from medical prescriptions with the guidelines of the insurance provider.
  • Volkswagen has deployed collaborative robots which can recognize human proximity and act accordingly to keep their human counterparts working on the supply line away from harm’s way.


Although we have pitted these systems against each other by using vs between and they do have certain differences, indeed they play complementary roles following the same philosophy – augmentation of labour, enhancement of quality and service, reduction in costs.


A system of organizational functionality that aspires to eliminate every piece of such work that does not create additional value. All these non productive tasks are referred to as waste or mudas in Japanese. These mudas are classified into areas often expressed with the acronym DOWNTIME. They are:

  1. Defects
  2. Overproduction
  3. Waiting
  4. Non-utilized talent
  5. Transportation
  6. Inventory
  7. Motion
  8. Extra-processing

Organizations that use Lean principles improve productivity while lowering operating costs. However this mode of operation can only yield the expected results if an organization can operate on a lean framework end to end. A lean supply chain cannot work if the marketing unit fails to work on similar terms. A Lean production unit cannot thrive if the logistic department lags behind.


We have already discussed the basics of RPA. It is simple arithmetic how RPA can support the Lean principle. If a robot is deployed to perform a repetitive task it instantly eliminates the D, W, and  N of DOWNTIME. There will be zero human errors, a lot of time will be saved and the knowledge worker will be able to contribute in a more meaningful way.


Intelligent process automation is a more advanced and more expensive version of RPA. IPA systems are a bit harder to train as we are expecting a lot more from them. IPA can help companies fine tune their business process management by providing critical insights about consumer behaviour, changing trends, and other socio economic factors that may influence the market. Data analytics augmented by IPA can really boost productivity and cut down wastes for organizations. It can help you manage the inventory so as to keep only what is most likely to sell.

Why automate repetitive tasks?

With human workforces augmented with RPA and IPA and the Lean philosophy making an impact on the market the way traditional enterprises work is changing for the better. So, we automate repetitive tasks because that is the only way of thriving in increasingly competitive markets. There are huge skill gaps in advanced analytics and data science. If we can free up knowledge workers from the mundane, they can skill up to fill those gaps. In fact, there is already a surge in enrolments for programmes like an applied AI course in many countries. It goes on to show that the job seekers and aspirants are responding quite positively to the changes.

Piling up the benefits of RPA and IPA

  • Removal of human errors.
  • Reduction in operating cost.
  • Manifold increment in processing speed.
  • More people available for creative work.
  • Increased job satisfaction for knowledge workers.
  • Enhanced customer service.

Enhancing analytics solutions with Machine Learning and RPA

Analytics or the subject that studies data analysis has been positively impacting a wide range of industries for quite some time. With every passing day the amount of data increases and so does the complexity of the data. The old world analytics tools were equipped enough to handle structured data and numbers. As marketers and business executives today are keen to tap into the immense reservoirs of unstructured data existing in the forms of texts, images, videos, audios, and even neuro motor signals, analytics professionals must up the ante.

Machine learning is the process in which a machine is trained to learn from data without human intervention. It is possible to sift through incredible amounts of data to find patterns and insights with the help of machine learning, more the data the better. Robotic process automation powered by machine learning can be and is being used to enhance analytics efforts.

How Artificial Intelligence and  Machine Learning Enhance RPA services

Let us use the example of a financial institute to understand this. If a financial institute uses RPA unsupported by AI and ML, the system will be able to track loan applications and jott the numbers down. But had it been augmented by AI the RPA system could have gone through the credit history of the customer to find out whether he or she was a potential loan defaulter. This is the kind of edge that AI and machine learning lends to RPA.

Foundations of Intelligent RPA

Certain technological advancements have taken RPA to a new level which we call intelligent RPA or IPA. Let us look at some such technologies.

Machine learning and deep learning

I have already explained what machine learning is. Deep learning is an advanced form of machine learning where the machine is taught to function like the human brain. It is more intelligent, needs lesser human intervention, and requires more data than machine learning.

Natural language processing or NLP

Powered by deep learning machines have learned to interpret messages coded in human languages. This is referred to as NLP or natural language processing. This area of machine intelligence is getting better by the day as different languages and even dialects are being incorporated. The next step is to interpret gestures and inconsistent human speech.


OCR stands for Optical Character Recognition and ICR stands for Intelligent Character Recognition. OCR lets the machine interpret characters written in print and ICR recognizes characters written or painted by hand. These technologies coupled with speech to text and text to speech conversion has taken customer engagement to a whole new level.

Computer Vision

This is quite a revolutionary technology which deserves a whole blog to itself. We can just touch the topic by saying that computer vision deals with the interpretation of still and moving images by machines. The sheer complexity and arbitrariness of images make computer vision difficult. It can revolutionize a dozen different industries from automobile to healthcare all by itself.

Augmented intelligence

It is a concept alternative to that of Artificial Intelligence which focuses on assisting human workers instead of replacing them. This is a cognitive technology that adds mechanical efficiency and computational prowess to human insight and creativity.

Conversational Intelligence

It is a quality hard wired in all human beings and a quality engineers have worked really hard for really long to enhance machines with. Today Intelligent chat bots can converse with customers or visitors without losing a beat. Customer service bots can engage people in conversation while accessing their transaction data at the back. This still has room for improvement but we have come a long way nevertheless.

A lot has gone into making RPA what it is today. Enterprises around the world are implementing RPA and IPA systems to improve business process and cut down expenses.  61% of all organizations in the USA were making extensive use of automation in 2019. The RPA industry is expected to be worth $ 2.1 billion in 2020 while enterprises around the world will save $6 trillion to $7 trillion through its implementation.

jooman neshat


Author Since: April 23, 2021

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