What Is Intelligent Process Automation?

Katherine Manning February 8, 2021 Automation Robotics Process Automation

Intelligent Process Automation

Automation is currently a hot topic in the business world as a productivity driver — facilitating more free time for human workers to focus on higher-cognitive roles. Further, since many industries are increasingly subject to government regulations, automation helps address the need for data quality, operational resilience, cybersecurity, and auditability. As consumers become more tech-savvy, automated processes offer great opportunities for business growth and resiliency. Perhaps your organization has been searching for the right processes and asking, “Should we automate this?”

Robotic process automation (RPA) has provided businesses with the potential to transition beyond essential task improvement to operating within new paradigms. However, now we have Intelligent Process Automation (IPA) driven by artificial intelligence designed to evolve and scale with a business.

For example, you have created a team to prioritize data analytics projects, and maybe you’ve initiated your RPA journey. It’s at this time where you may ask, “Is there a way we can add intelligence to this automation?”

Technology has come far in the past decade- making business processes more effective. We started with six-sigma and moved on to business process management (BPM). These toolsets played an integral role in optimizing manual, paper-based processes such as reviewing patient records, student applications, invoices, and more. Automation is now part of decision-making and workflow for rules-based systems. All business process trends favor advanced automation. Automation improves resiliency. According to Gartner:

“Whether a pandemic or a recession, volatility exists in the world. Organizations that are prepared to pivot and adapt will weather all types of disruptions.”

What is IPA?

Intelligent process automation adds intelligence to automation, or it offers the functional benefits of RPA combined with artificial intelligence (AI), natural language processing (NLP), and optical character recognition (OCR).

As a result, IPA can mimic human learning and activities such as critical analysis. So then, IPA’s differentiator from RPA is that it can evolve.

At times, process automation can drive digital transformation. For instance, RPA can deliver functional benefits almost instantaneously. It automates large volumes of repetitive work, equating to a bot workforce with the capacity to work 24/7. Thus, human team members can work on higher-value tasks. In some instances, human workers can finally do what they were hired to do or focus on upskilling. Moreover, there are minimal obstacles to deployment, and RPA offers the potential for quick wins and quantifiable returns.

However, on its own, RPA is not transformational. If a variance is added to any process, scalability is impacted. So, RPA can handle a wide variety of tasks, but the upward potential is limited.

What is RPA?

In the simplest terms, RPA is a technology that creates programmed virtual “bots” to execute repetitive, rules-based business tasks. RPA bots can perform any predefined and time-intensive task. As a result, RPA can relieve human workers of heavy workloads and save thousands of hours in productivity and labor costs. RPA ensures faster implementation and cycle time of business processes with fewer errors, which can induce immediate and strategic ROI.

Concerning intelligence, RPA is at the lower end of the spectrum as it is a programmed virtual agent. Therefore, RPA is useful for collecting data and executing fundamental analysis. It also helps to take care of the low-hanging and time-consuming tasks such as handling preliminary work for large data sets.

Complement RPA with IPA

Both RPA and IPA can automate a wide variety of processes, but you also get the ability to respond to variances. When you bridge RPA functions into AI, you get dynamic interactions with transfer learning. The concept of transfer learning is where technology can use the model from one process and apply it to another without human intervention or more programming. Besides, transfer learning can happen in real-time. Then, IPA can handle both volume and variance; it self-manages and reduces time spent on human intervention or programming.

Yet, IPA doesn’t just automate your processes and streamline your workflows. It can make them more intelligent and offer detailed insights for making better business decisions. Humans can access more sophisticated machine interaction with IPA and deliver business solutions that would not manifest with human workers alone.

IPA adoption

IPA focuses on learning and improving processes with every interaction. Yet, when adopting IPA, it’s critical to identify the right business cases for automation. Strategy and purpose are necessary. Answers to the following questions should help:

  • Is the activity too time-consuming for your human employees?
  • Is the process complex, involving multiple steps and human interaction?
  • Are there other options for streamlining the process?

Where can you implement IPA? Let’s take a look at a few real-world use cases:

Insurance verification and billing

In any healthcare environment, insurance verification is a lengthy and painful process that is mostly manual and has the potential for a bevy of costly errors. If information data is not correctly verified, it can lead to delayed payments, rejections, and more. On the other hand, IPA can utilize Intelligent Document Processing (IDP) to extract and analyze data. Moreover, IPA can interface with a wide range of systems to send and receive pertinent data throughout the organization.

Quote-to-Cash

Traditional sales processes are filled with data entry errors and multiple customer complaints about turnaround time, channels, customer service, and more. You can simplify the sales cycle using the Quote-to-cash system and automation. Since much of the data is structured, IPA can use NLP and OCR to extract data from emails, orders, invoices, and faxes. RPA bots can deliver collected data from one system to another. Therefore, you can enhance forecasting with better data analysis while mitigating errors.

Which technology is better for your business? 

Both.  RPA and IPA are not competitive; they are complementary technologies. IPA takes over where RPA faces limits. You can use RPA to address exchange points between segments of the workflow. Nonetheless, to retain a competitive advantage, businesses should review their processes and determine where RPA and IPA make the most sense and where it may add value.

 

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