Differentiating Between Intelligent Automation and Hyperautomation

5 min read

A comparison of two types of digital process automation — intelligent automation and hyperautomation.

The speed of business today requires agility and efficiency that can only be achieved through automation. IDC forecasts that the worldwide economic impact of converged AI-powered automation across all lines of business and IT functions will be close to $3 trillion by the end of 2022.

With experts discussing the benefits of two types of digital process automation — intelligent automation and hyperautomation — many others are led to question the difference between the two. Are they simply interchangeable terms or is there a discernable difference between these two concepts and the benefits they bring to businesses?

Intelligent automation (IA) and hyperautomation are both contributors to the explosion of the use of AI-powered automation platforms and automation tools across the business and IT landscape. They both refer to the use of automation to streamline processes using advanced technologies and enhancements. In doing so, these tools help improve the quality of automation results and the quality of customer interactions.

However, there are distinct differences.

In short, intelligent automation is comprised of robotic process automation (RPA), artificial intelligence (AI) and machine learning (ML). Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Therefore, intelligent automation is often used within hyperautomation efforts.

Learn how organizations that invest in AI-powered automation reap big payoffs.

What is intelligent automation?

Intelligent automation involves the use of automation technologies like robotic process automation (RPA), artificial intelligence (AI), machine learning (ML) and business process management (BPM) to streamline and scale tasks and decision-making across organizations.

Intelligent automation has received a favorable response from the market because it simplifies processes, improves operational efficiencies and frees up employees’ time to focus on what matters most. It can also tackle complex tasks in real time and drastically streamline workflows, unlocking new possibilities to create value and achieve sustained growth.

The benefits of intelligent automation

Intelligent automation (IA) creates optimization and drives business success through streamlining business processes using artificial intelligence and core automation technologies. IA enables businesses to do the following:

  • Reduce costs through workforce augmentation and productivity improvements.
  • Increase process and data accuracy as well as quality through consistent processes, approaches and Intelligent Business Process Management (IBPM).
  • Improve the customer experience by providing faster response times, higher quality products and more accurate answers to inquiries.
  • Address compliance and regulations with a consistent approach to security, reporting and many other processes.
  • Respond to market and environmental changes. (Fluctuating markets and workforce behaviors, resulting from disruptors such as the COVID-19 pandemic, are a forcing function for change in many areas of business.)

Putting intelligent automation to work: A real-world use case

Any environment can benefit from streamlining manual processes and task automation. From healthcare to finance to manufacturing and beyond, the use of intelligent automation can provide benefits that improve the customer experience and impact the bottom line.

One particular example of IA at work is the U.S. Department of Veterans Affairs. Processing claims manually was a tremendous burden that required several hundred people to sort mail and enter data into databases. As with many human-intensive processes, this was error-prone and expensive.

The department adopted IA to automate its business processes using advanced technology like RPA bots. This approach reduced the turnaround time by 90%, saving time and satisfying customers with increased speed and accuracy.

What is hyperautomation?

Hyperautomation is the act of automating everything in an organization that can be automated. The intent of hyperautomation is to streamline processes across an organization using intelligent process automation (IPA), which includes AI, RPA and other technologies to run without human intervention.

Hyperautomation often employs other technologies — such as optical character recognition (OCR), intelligent document processing (IDP) and natural language processing (NLP) — to provide higher-quality automation using data from various sources. Digital twin or digital twin organization (DTO) are often used for modeling to improve operations and evaluate the impact of automation.

Hyperautomation is considered by Gartner to be one of the top-10 strategic technology trends. According to Gartner, 85% of participants will “either increase or sustain their organization’s hyperautomation investments over the next 12 months, and over 56% already have four or more concurrent hyperautomation initiatives.”

Gartner considers hyperautomation to be a necessity in today’s market. “Hyperautomation is rapidly shifting from an option to a condition of survival,” ranking “outdated work processes as the No. 1 workforce issue.”

Benefits of hyperautomation

Hyperautomation provides many benefits to organizations looking to transform their business. It streamlines business processes by eliminating repetitive tasks and automating manual ones. Hyperautomation also enables an organization to complete tasks with consistency, accuracy and speed, and reduce costs.

Ultimately, hyperautomation fuels digital transformation by streamlining internal business and technology processes. Hyperautomation improves the customer experience through faster response times, more accurate results, faster time to market, and many other positive results that directly impact the customer and user experience.

Take a deeper dive into the benefits and challenges of hyperautomation.

Hyperautomation use case

Any organization can benefit from hyperautomation, particularly one that is plagued with inconsistent product quality and inefficient processes or one that is in a competitive environment where getting ahead with higher-quality products and more valuable market interactions equates to gaining market share and improving revenue.

A manufacturing company provides a great example of the breadth and depth of improvements that hyperautomation can afford an organization. Business processes like billing, customer interactions, inventory and payroll can use hyperautomation for Business Process Automation (BPA) to streamline the business on a broad scale.

Using process mining, an organization can get a better picture of its processes and identify which processes would best benefit from AI and automation. In technology and fabrication processes, for example, the use of RPA to automate physical production and supply chain processes, plus BPA to address manufacturing best practices, can have a tremendous effect on the quality and speed of production. Overall, hyperautomation using BPA and RPA to streamline both back- and front-end operations generate an improvement in quality, speed, accuracy and cost for a significant impact on the future of business performance.

Learn more about how to capture the full value of hyperautomation.

IBM AI-powered automation

Start your journey with IBM AI-powered automation and the IBM Cloud Paks® for Automation, which can help you reduce your manual processes by 80%. Built on an open, hybrid platform and embedded with Watson, the new IBM Automation Cloud Paks are the industry’s first integrated suite of domain-specific business and IT software. They include a single, expert system and library of purpose-built automations — pre-trained by experts and drawing on extensive IBM domain knowledge and depth of industry expertise.

AI-powered automation software enables business and IT teams to easily discover how processes run, decide what to automate based on insights from structured and unstructured data and automate and continuously improve workflows that run centrally, in networks and all the way to the edge.

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