| 5 MIN READ

What is Hyperautomation? It’s Orchestration and Automation.

Avatar Written by

What is Hyperautomation?

Hyperautomation is an enterprise strategy aimed at automating and transforming as many business and IT processes as possible through the use of artificial intelligence (AI) and machine learning (ML) and the coordination of multiple automation tools.

While hyperautomation is a relatively new term, Wipro made headlines in 2016 when then-CEO Neemuchwala announced a plan to consolidate its automation tools and invest in hyperautomation.

Then, Gartner released its report, “Top 10 Strategic Technology Trends for 2020”, and suddenly everyone in the industry was talking about hyperautomation.

Hyperautomation is a relatively knew term, popularized by Gartner and Wipro.
Google Trends result for Hyperautomation traffic since 2015.

Being that hyperautomation is still a new term, there’s some confusion about what it means and how it differs from intelligent process automation (IPA) –or intelligent automation– and digital process automation (DPA).

We’ll clear that up shortly. For now, here’s Gartner’s take on the term they more or less coined:

“Hyperautomation refers to an effective combination of complementary sets of tools that can integrate functional and process silos to automate and augment business processes.”

Why is Hyperautomation Important?

Most organizations have an assortment of automation tools for both IT and business. Many of these tools are script-based, or rule-based, or designed for narrow use cases (ie, onboarding automation). These tools offer automation for simple tasks on an ad hoc basis. If the HR team or marketing team needs to automate a set of tasks, then that team implements a new tool.

The problem with this approach is that A) the organization becomes a patchwork of siloed automation, and B) that patchwork is full of holes. This makes it difficult and expensive for IT to develop automated, end-to-end processes that support business initiatives.

That is the major difference between automation and hyperautomation: automation is your basic task automation whereas hyperautomation is a unified approach that seeks to automate and coordinate as much as possible, in order to transform processes (digital transformation) and to create new ones that would have been otherwise impossible using traditional methods.

“By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.”

Gartner, Top 10 Strategic Technology Trends for 2020: Hyperautomation

In order to reduce costs and improve productivity, organizations are moving towards unified, enterprise-wide automation strategies, increasingly with the help of AI and ML tools.

Additional benefits?

Organizations that automate as many routine, mundane tasks as possible have a much better shot at attracting high-value professionals.

How Does Hyperautomation Work?

Hyperautomation has three main components:

  1. Automation
  2. Orchestration
  3. Optimization

In order to get a hyperautomation strategy off the ground, an enterprise needs a strong automation foundation. This can and usually does include robotic process automation (RPA) for automating basic tasks, IT automation tools for data warehousing, plus a handful of other automation solutions that are necessary to accommodate various teams and departments.

The automation tools sit atop of the orchestration tools. For instance a workload automation solution, an enterprise job scheduling solution, or an intelligent business process management solution (iBPMS). These solutions should provide the extensibility necessary to integrate data from both the automation tools and other divergent technologies deployed across the enterprise.

Then, somewhere along the line, artificial intelligence (AI) and machine learning (ML) need to be introduced. These technologies are increasingly packaged into enterprise orchestration solutions, making it possible for organizations to:

  • Quickly optimize existing processes by identifying delays and slack time
  • Implement changes proactively to prevent failures
  • Discover missed automation opportunities
  • Application of advanced technologies such as natural language processing (NLP), optical character recognition (OCR), advanced analytics, and digital twins (DTO) in order to create innovative new processes

Intelligent Automation vs. Hyperautomation

At close glance, intelligent automation –or intelligent process automation (IPA)– both leverage AI and ML to optimize existing processes. But there are a few key differences.

First, intelligent process automation focuses on a narrow set of tasks, often building on RPA either by combining the two technologies into a single tool. These tools are used to automate and optimize existing tasks.

Hyperautomation on the other hand is not a tool –it’s a unified enterprise strategy or initiative with the ultimate goal of creating and optimizing end-to-end processes that support innovative new business propositions.

“Enterprise architecture and technology innovation leaders lack a defined strategy to scale automation with tactical and strategic goals. They must deliver end-to-end automation beyond RPA by combining complementary technologies to augment business processes. Gartner calls this ‘hyperautomation.’” 

– Gartner, Move Beyond RPA to Deliver Hyperautomation

Benefits of Hyperautomation

By this point, every organization is familiar with the benefits of automation. By reducing human interventions in time-consuming, repetitive tasks, the organization increases efficiency, productivity, and morale –because employees are spending more time working on higher-value, cognitive projects instead of menial tasks.

As mentioned earlier, most organizations take an ad hoc approach to automation that leaves a lot of manual tasks still left to be automated. Those holes in the organization’s “patchwork” automation can be filled by taking a unified, enterprise-wide approach to automation (hyperautomation), using low-code process development and orchestration tools.

Those processes can then be simplified and optimized through the orchestration tool. Processes are then completed in less time, with fewer errors and fewer resources. AI and ML algorithms can then optimize those processes by coordinating computing resources, analyzing historical and real-time data, and taking automated actions to prevent delays.

When the organization has automated as many tasks and processes as possible, it becomes much easier to quickly develop reliable, end-to-end processes that support new or evolving business goals. And, when these processes are run through an enterprise job scheduler or workload automation platform, AI can be used for process mining. By coordinating a range of tools with a single, unified solution, organizations are able to quickly react to evolving business and market demands. That’s the gist of hyperautomation.


Business Needs Are Quickly Evolving.

Explore how you can orchestrate and automate end-to-end processes with ActiveBatch Workload Automation and Enterprise Job Scheduling.

Avatar

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.

Let Us Know What You Thought about this Post.

Leave a Reply

Your email address will not be published. Required fields are marked *

You may also like:

Microsoft Ignite 2019: Azure Updates, Quantum Upgrades, And More

2019 Microsoft Ignite keynote, delivered by CEO Satya Nadella, encompassed a broad range of topics from Tech Intensity, to Azure, Quantum, and more.

Informatica World 2019 Recap: Improving Data for Better AI

This year’s Informatica World featured discussions around the preparation and maintenance of data for use in artificial intelligence and machine learning.