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An Introduction to Intelligent Process Automation (IPA)

  • Ashesh Anand
  • Sep 23, 2021
An Introduction to Intelligent Process Automation (IPA) title banner

The digital workplace is undergoing rapid transformation, and this trend appears to be unstoppable. Intelligent process automation, often known as IPA, is expected to take off in the near future. 


This acronym stands for Intelligent Process Automation and blends together RPA (robotic process automation), as well as AI (artificial intelligence).


Intelligence Process Automation is one facet of a larger technological revolution known as Automation as a whole. 


Automation is helping to make apparently space-age technology possible today, from driverless vehicles to autonomous drones, by developing and exploiting new kinds of intelligence. 


Whether it's automated chores across a business or customer contacts via desktop assistants, automation is changing the way we live and work.


What is IPA?


Artificial intelligence and machine learning algorithms are used to automate or improve processes in intelligent process automation (IPA). In a number of corporate processes, IPA technologies can decrease human interaction.


IPA solutions are more than just a set of rules. Intelligent Process Automation (IPA) is a technology stack that allows you to manage, automate, and integrate digital processes. Digital Process Automation (DPA), Robotic Process Automation (RPA), and Artificial Intelligence are the three main technologies that make up IPA.


Intelligent Process Automation (IPA) is a technology that allows businesses to automate processes using structured, semi-structured, and unstructured document types, such as forms, documents, text, pictures, and video.


Watch this video on IPA by Capgemini:

It's a collection of business-process enhancements and next-generation tools that help knowledge workers by eliminating repetitive, repeatable, and regular chores. 


It may also significantly enhance customer journeys by streamlining interactions and accelerating procedures.


IPA imitates human behaviors and learns to do them increasingly better over time. Because of developments in deep learning and cognitive technologies, traditional rule-based automation levers are being supplemented with decision making capabilities. 


The promise of IPA is higher efficiency, improved worker performance, lower operational risks, faster reaction times, and better customer journey experiences.


(Also Read: AI Strategies to maximize business revenue )

IPA is a mixture of these technologies: Machine Learning, Cognitive Learning, Artificial Intelligence, Robotic Process Automation.

IPA: A mixture of different technologies


IPA's main technologies include the following:


  • Unattended robots, often known as server-based bots, completely automate activities that do not require human judgment or involvement.


  • Machine learning algorithms that use "supervised" and "unsupervised" learning to discover patterns in structured data.


  • Intelligent workflow solutions that aid in the management, integration, and handoff of processes involving both humans and machines.


  • Cognitive agents are virtual agents that perform tasks, learn from data sets, and communicate with humans using a combination of machine learning and natural language production.


  • Optical Character Recognition, for example, is a computer vision technique that converts a scanned document or photo into text.


  •  Natural language processing (NLP) software allows a computer to comprehend, interpret, and modify spoken and written language. Chatbots and virtual assistants rely heavily on this technology.


(Also Read: Guide to NLP )


Differences between IPA and RPA


Intelligent process automation is frequently mistaken for robotic process automation (RPA). This is just partially accurate. While robotic process automation is frequently a major feature of IPA systems, it is not required for IPA’s to always include RPAs.


Robotic process automation (RPA) refers to software (apps, platforms, or scripts) that automates basic, rule-based, repetitive processes. When done manually, these activities might take a long time. Instead of manually gathering phone numbers from apps, an RPA tool may be trained to do so.


(Related: What is RPA? Tools, Benefits, and Myths )


RPA tools, on the other hand, have the drawback of being inflexible due to their rule-based nature. The RPA tool will not be able to finish the work if the firm modifies its form or if a client submits information in the wrong row.


At the point where RPA is no longer effective, intelligent process automation is commonly employed. An IPA tool can accomplish increasingly complicated procedures using a range of current and upcoming technologies by utilizing artificial intelligence.


Watch this video, to explore more about RPA:


Benefits of IPA


IPA has the potential to be a genuinely transformative technology, with benefits ranging from increased efficiency to significant gains in customer experience, productivity, and efficiency. 


The good news is that when businesses begin their IPA journey, they will be able to see a return on investment as they implement specific IPA components.


In the long run, IPA will help a company automate more complicated activities and create more adaptive workflows. 


These are activities that RPA can't perform because they include a lot of exceptions or require unstructured data, but that are onerous, dull, or time-consuming for humans to complete.


Consider that, a huge bank that uses a combination of RPA and AI to automate the difficult process of establishing a bank account. A chatbot may start the process by presenting the consumer with an online application form. 


When the form is finished, it is sent to unattended robots for automatic background checks and identification verification (using computer vision to extract data from a passport, for example).


The exception can be forwarded to a human operator if the system cannot confirm the applicant's identification - for example, if someone calls themself Steve on the form but their passport reads Stephen. 


Machine learning algorithms will study how the person manages the exception in the future and learn how to handle it better. The RPA tools may then work with NLP technologies to complete the remaining activities needed to open and activate the account. 


The advantages of a basic IPA use case are numerous, as this example demonstrates. 


  • For starters, rather than focusing on process tasks, human operators may focus on human connections. 


  • Second, large-scale automation of complicated operations will result in considerable cost reductions and productivity improvements. 


  • And, perhaps most crucially, client happiness should rise as a result of the reduction in friction in procedures such as billing questions and account opening.


( Also Read: Business Benefits of Deep Learning )



Use Cases for Intelligent Process Automation

Intelligent Process Automation, like RPA, may be used in a variety of sectors, departments, and roles. 


In addition, similar to RPA's early days, there are particular industries that are early adopters with frequent use cases that are already utilizing Intelligent Process Automation:


Financial Services 


IPA is already being used to develop more precise credit models to reinforce lending processes, enhance trade execution and routing, and use analytics to understand customer pricing sensitivity and preferences in the Financial Services industry.




Insurance firms are aggressively using Natural Language Processing-based chatbots to automate and improve client interactions. 


They're utilized in an IPA framework to automate appointment scheduling and build a self-service approach that allows clients to simply choose an insurance policy.


( Also Read: What are the different types of Insurance?




Intelligent Process Automation has the benefit of seeing data in real-time and delivering it to customers without the need for manual involvement. 


Pharmaceutical firms and medical device makers are leveraging the increased accessibility of data provided by the IPA to reinforce compliance by decreasing fraud and mistakes while enhancing security, safety, and accuracy, according to an article in Information Age.


The healthcare business is also benefiting from the digitalization and automation of document handling and regulatory monitoring to boost drug research and vaccine development.


( Also Read: Top healthcare technologies )




IPA technologies may be used to analyze shipping data in order to optimize shipping routes and timetables in order to eliminate bottlenecks, avoid delays, and make the most of available resources.


(Also Read: Use cases of AI in Logistics )

Image depicts the spectrum of IPA which includes: Initial Automation, Robotic Process Automation, Automation stage, Cognitive Computing, and AI.

Spectrum of IPA


Future Role of Intelligent Process Automation in Automation?


The future of automation is Intelligent Process Automation. While not yet saturated, Robotic Process Automation has emerged from the hype cycle and is being widely deployed.


Even though companies have struggled with disappointing ROI from onerous RPA maintenance and support, as well as a fragile digital workforce as a result of bad automation design methods, RPA’s growing pains will soon be overcome to usher in IPA.


While combining AI with RPA to offer Intelligent Process Automation isn't quite ready for prime time yet, there's plenty of room for experimentation, and early adopters are already experiencing positive results, so it's only a matter of time until it becomes a hot topic.


How Can Companies Benefit from Intelligent Process Automation?


Intelligent process automation does not necessitate a large upfront investment because it may be developed on top of existing data systems and digital infrastructure. 


For example, the robotics process automation component has been installed and is generating value in extremely short periods, often less than a month.


  • Organizations must avoid falling into the trap of automating intelligent processes in silos. A comprehensive approach yields considerably better results. 


  • To identify the areas most likely to generate growth and value through intelligent process automation, a deep and complete examination of ALL business processes must be done, and the design plotted appropriately.


  • Any new intelligent process must have a thorough grasp of the business strategy, operations, and goals in order to be successful. In other words, the operational model's process goals must be very well stated and specified. 


  • More significantly, it should be aware of and intelligently integrated with the system's current capabilities.


  • Focus your early intelligent process automation efforts on speeding up and improving an end-to-end processor, in the case of retail, a customer journey. 


  • When it comes to redesigning, Focus your early intelligent process automation efforts on speeding up and improving an end-to-end processor, in the case of retail, a customer journey. 


(Suggested Read : Customer Behavior Analytics )


A minimal viable process (MVP) should be piloted and examined once it is redesigned to determine what components work, what doesn't, and what can be done to improve it. This activity is not only required but also beneficial in developing the program's full-fledged changes. 


Because this is an area that is always changing, any intelligent process automation solution should allow for expansion and integration with newer technology. 


To guarantee seamless operations across the company, a supporting, underlying communications network is also necessary. A minimal viable procedure (MVP) should be piloted and studied to determine which components deliver and which do not.


Summing Up


Businesses are on the verge of a new era in organizational performance, one that will be powered by intelligent process automation. Businesses that use intelligent process automation for their operations ahead of the competition have a much higher chance to be successful.

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