What Is Intelligent Process Automation and Why Does It Matter?

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What is Intelligent Process Automation?

Intelligent process automation (IPA) refers to tasks that are automated or optimized in part by artificial intelligence and machine learning algorithms. IPA tools can reduce human intervention in a variety of business processes.

IPA solutions go beyond simple, rule-based tasks. For example, IPA tools can apply artificial intelligence to process unstructured data, something that many RPA tools cannot do, or to provision IT resources to ensure critical SLAs are maintained. Another example can include the use of machine learning algorithms that enable the IPA tool to improve task performance over time.

Intelligent Process Automation vs. Robotic Process Automation

Intelligent process automation is often regarded as being the same as robotic process automation (RPA). This is only half true. While robotic process automation is often a key capability of IPA platforms, IPA does not necessarily have to include RPA.

Robotic process automation refers to tools -applications, platforms, or scripts- that automate simple, rule-based, repetitive tasks. These tasks are often time-consuming when done manually. For example, instead of collecting phone numbers from applications, an RPA tool can be trained to automate the task.

However, the problem with RPA tools is that they are rigid because they are rule-based. If the company updates its form, or a customer enters information into the wrong row, the RPA tool won’t be able to successfully complete the task.

This is where intelligent process automation is frequently used –at the point where RPA is no longer effective. By leveraging artificial intelligence, an IPA tool can complete more complex processes that incorporate a variety of new and emerging technologies.

Artificial Intelligence and Machine Learning for IPA

The use of AI and ML in process automation enables IPA platforms to go far beyond the front-office and back-office tasks RPA is used to automate.

Artificial intelligence, for example, makes it possible for IPA platforms to analyze both semi-structured data and unstructured data necessary for natural language processing (NLP), intent detection, and other cognitive technologies. This allows users to build complex workflows for chatbots, or responding to customer requests.

So whereas RPA tools are used to automate tasks that already exist, IPA tools give users the opportunity to re-imagine existing processes, or to optimize those processes with deep learning, or use new technologies such as intelligent decision making to create innovative new processes.

IPA platforms also leverage machine learning algorithms that analyze historical and real-time data in order to optimize processes both in real time and in the future. For example, by automatically routing workflows based on their predicted runtimes, log contents, or flow control to auto-remedy problem workflows. However, designing and optimizing processes is still only part of the IPA picture.


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Extensible Intelligence for End-to-End Orchestration

In today’s IT and business environments, no platform exists as an island (at least, it shouldn’t) and the same holds true for IPA tools –an IPA tool must access and manipulate data from a variety of sources, many of which rely on disparate technologies and different vendors.

In order to achieve this, a level of extensibility is necessary on the back end, either through a workload automation solution, an enterprise job scheduler, or a service orchestration and automation platform. Such solutions provide the universal connectors and API accessibility needed to seamlessly manage data and dependencies between disparate systems, making truly end-to-end processes possible. With an IPA tool supported by the right data and the right infrastructure, IT teams are able to provide innovative workflows that orchestrate tasks all the way from the data center to the end-user or the customer.

Intelligent Process Automation Examples and Use Cases

IPA tools are used to automate time-consuming, routine business processes, enabling employees to spend more time on cognitive tasks. By freeing-up employee hours, organizations gain efficiency, improve productivity, and save on full-time employees. IPA case studies, therefore, span a variety of industries from finance, to healthcare, to manufacturing.

Financial services: Customer support professionals have to gather customer data from databases, phone calls, email, and online chats. This is time-consuming and can impact the customer journey. An IPA tool can be used to pull data from the database and update records with additional information found in phone calls and emails. 

Insurance: A claims department might spend hundreds of hours a year of entering data from claims forms into the department’s CRM. An IPA tool can be used to scrape necessary data from the forms and then port the information over to the CRM. This task can be included in a larger end-to-end process that delivers relevant information directly to the customer or end-user.

Shipping: IPA tools can be used to analyze shipping data to optimize shipping routes and schedules in order to reduce bottlenecks, prevent delays, and optimize available resources.

Benefits of Intelligent Process Automation

The main benefits of process automation are efficiency, optimization, and innovation. By automating routine processes, employees can save time. Machine learning algorithms can discover new ways to optimize processes for further efficiency and productivity gains, and both business and IT users can leverage new technologies to develop innovative solutions and improve the customer experience.

Furthermore, a recent study by McKinsey found that, by implementing IPA, organizations have:

  • Automated over 50% of manual tasks
  • Reduced process times by 50%
  • Achieved ROIs of over 100%

The Future of Intelligent Process Automation

IPA is to process automation as the internet was to video games –both developments spawned (or are spawning) an array of new possibilities. But both developments rest on other technologies: for gaming, it was high-speed internet and greater processing power, and for automation it is the orchestration of back-end IT processes.

The relationship between IT and business is rapidly changing, with IT becoming increasingly essential to business success, digital transformation initiatives, and customer satisfaction. This is causing IT to align closely with business and end-user needs, orchestrating end-to-end processes that streamline data across IT infrastructure, data centers, and disparate IT and business systems.

As Gartner explains in its 2020 Magic Quadrant for RPA:

“Customers are striving toward an orchestrated, end-to-end, intelligent, event-driven form of automation, delivered with an effective combination of automation tools with multiple machine learning applications and packaged software. Gartner calls this ‘hyperautomation.’”


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Brian is a staff writer for the IT Automation Without Boundaries blog, where he covers IT news, events, and thought leadership. He has written for several publications around the New York City-metro area, both in print and online, and received his B.A. in journalism from Rowan University. When he’s not writing about IT orchestration and modernization, he’s nose-deep in a good book or building Lego spaceships with his kids.

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