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Hyperautomation vs. Intelligent Automation: The Difference, Explained

Dan O'Keefe, Appian
April 13, 2023

Businesses today know that operational efficiency, effectiveness, and productivity are keys to competitive advantage. And process automation tools are being adopted more and more because they help IT and business leaders turn those goals into reality.

If you’ve spent time researching ways to boost efficiency by taking advantage of automation opportunities, you’ve likely come across the related terms hyperautomation (which is synonymous with process automation) and intelligent automation. You may wonder what the true difference between hyperautomation vs intelligent automation is. They’re both critical concepts to grasp for anyone interested in participating in the automation space.

Here we’ll discuss hyperautomation vs intelligent automation to help you determine which approach will best suit your organization’s needs and your automation journey. 

[ Want to dive deeper into hyperautomation approaches? Get the Gartner® Beyond RPA: Build Your Hyperautomation Technology Portfolio report. ]

Hyperautomation vs. intelligent automation. 

When it comes to automation, you can think of the concept as comprising three levels. Each level builds on the complexity—and power—of the previous one. 

  1. Simple automation: Simple automation refers to the use of one automation technology to solve a simple task. For example, robotic process automation (RPA) might be used to collect data from a webpage or application where no API is available. 
  2. Intelligent automation: Intelligent process automation refers to the blending of RPA and artificial intelligence (AI). RPA handles simple tasks with procedural steps, while AI can handle cognitive tasks based on pre-trained models. For example, invoice processing might use AI-driven intelligent document processing (IDP) to extract data from an invoice, then use a bot to enter the information into a specific software program that lacks an API. 
  3. Hyperautomation: The concept of hyperautomation goes one step further. Hyperautomation refers to the use of all automation tools, including capabilities used to orchestrate tasks between these tools (and human workers). Hyperautomation includes artificial intelligence and RPA but also adds a wider range of tools like decision rules, workflow orchestration, and intelligent business process management (iBPM) into the mix. Hyperautomation efforts allow teams to streamline and automate complete end-to-end processes.

As you can see, intelligent automation vs hyperautomation isn’t necessarily an either/or proposition—it’s actually a progressive ladder of increasingly advanced technologies. 

[ Read our related article: RPA vs. AI vs. low-code. ] 

How is hyperautomation different from regular automation? 

Both simple automation and intelligent automation operate at the task level. They can work wonders for repetitive tasks like data entry (for simple automation) and for more complex yet still isolated, mundane tasks, such as using natural language processing (NLP) in chatbots to improve customer experience. 

But for complex processes or end-to-end business processes, hyperautomation initiatives are the way to go. The orchestration capabilities of hyperautomation allow for complex interactions between automation tools and human workers—and they enable this at the end-to-end level for core business processes. This orchestration layer lets you reap the full benefits of hyperautomation. 

Consider an example of order processing. The steps for orchestrating this full process might include:

  1. A purchase request comes in either digitally or via physical mail. 
  2. Then AI comes into play. AI-driven IDP uses optical character recognition (OCR) to classify the document as a purchase order, then extracts relevant information like order volume, addresses, or account numbers from the document without requiring manual entry. 
  3. Next, the orchestration layer uses business rules to either validate the information or pass it off to a human to look for errors or discrepancies. 
  4. If the purchase order looks good, an RPA bot enters the information into the appropriate systems. If not, the orchestration layer routes the information to an employee for human intervention.
  5. Once all the information has been entered into the systems, AI generates several emails. First, it generates notifications to send to the appropriate departments for order fulfillment. Then, it generates emails to send to the purchase requester with tracking information. 
  6. Finally, the orchestration layer sends a request to use RPA to print off shipping labels for the order fulfillment department. 

This is an abbreviated example—there are other elements of this process that can be automated, such as checking inventory levels, ordering more inventory as needed, sending billing requests, verifying payments, and more. The point is that intelligent automation can automate a small portion here, but to fully automate a process (and grow that automation), you need the essential orchestration components only found in hyperautomation technologies.

What to look for in a hyperautomation platform.

When thinking about hyperautomation vs intelligent automation, the truth is that both are important. However, if you want to reap the full benefits of automation, you’ll want to take a platform approach to process automation that provides multiple automation tools plus the process management tool power of that critical orchestration layer. 

What’s important for building out this end-to-end automation capability? Get the Process Automation Guide: How to Achieve End-to-End Process Excellence.