The Importance Of Intelligent Process Automation


Tasks that are optimized or automated by Machine Learning (ML) algorithms and Artificial Intelligence (AI) are referred to as Intelligent Process Automation (IPA). IPA tools thus decrease the human intervention required in different business processes. IPA solutions surpass the simple, rule-based tasks. For example, AI can be applied by IPA tools to process unstructured data – something that most RPA tools cannot achieve, or to provision IT resources to guarantee that critical SLAs are maintained. Another example that can be considered is the use of ML algorithms that enable the IPA tools to ensure that there is efficient task performance over time.

IPA Versus Robotic Process Automation

IPA is often considered to be the same as Robotic Process Automation (RPA). This is not entirely true. Though RPA is often the main ability of IPA platforms, IPA does not essentially have to comprise RPA.

RPA refers to tools – platforms, applications, or scripts – that program and automate easy, rule-based, monotonous tasks. These tasks often consume too much time when done manually. For example, the task of collecting phone numbers from applications can be automated by training an RPA tool. However, one point to note is that RPA tools are rigid as they are rule-based. For example, if an organization modifies its form, or if a customer specifies information in the wrong row, then the RPA tool cannot complete the task successfully. This is where IPA is important –the point where RPA is no longer useful. By leveraging AI, an IPA tool can finish more complex processes that incorporate various new and emerging technologies.

Machine Learning and Artificial Intelligence For IPA

The use of ML and AI in process automation allows IPA platforms go beyond the front-office and back-office tasks that RPA is used to automate. For example, AI makes it possible for IPA platforms to analyze both unstructured and semi-structured data essential for intent detection, Natural Language Processing (NLP), and other cognitive technologies. This enables users to create complex workflows for chatbots, or replying to customer requests.

Thus, where RPA tools are used to automate tasks which already exist, IPA tools provide users the opportunity to re-imagine prevailing processes, or to optimize the processes with deep learning, or use new technologies such as intelligent decision making to create innovative new processes.

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

IPA Use Cases

IPA tools are used to automate routine and time-consuming business processes, allowing employees to spend more time on intellectual tasks. Organizations thus gain efficiency and improve productivity. Some sectors where IPA can be used are:

  • Financial services: An IPA tool can be used to extract data from the database and update records with extra information found in emails and phone calls.
  • Insurance: An IPA tool can be used to scrape necessary data from the forms and then port the data over to the CRM.
  • Shipping: IPA tools can be used to analyze shipping data to optimize schedules and shipping to reduce bottlenecks, avoid delays, and enhance available resources.


IPA is essential to process automation. But both developments are dependent on other technologies and for automation it is the arrangement of back-end IT processes.

The relationship between business and IT is continuously evolving with IT turning increasingly essential to digital transformation initiatives, business success, and customer satisfaction. This is resulting in IT to align closely with business and end-user requirements, data centers and separate business and IT systems.