What is Hyperautomation?

December 10, 2019
· 3 min read

Gartner just named hyperautomation as one of its Top 10 Strategic Technology Trends for 2020. This makes it a trend that “enterprises need to consider” as part of their technology plans and which will have a “profound impact on people…across industries and geographies, with significant potential for disruption”.

Hyperautomation hasn’t historically been used in common parlance but has generated huge interest since being identified by Gartner. It helps to know what it is as you plan for 2020 and onwards.

Automation is the use of technology to perform work that originally required human action. Robotic Process Automation (RPA) tools (which your organization probably already used) made it easier to automate processes in the enterprise. At DataRobot, we partner with the leaders in the space, including UiPath, Automation Anywhere and BluePrism.

Historically organisations relied on complex code to automate work, but RPA made it much easier, democratizing coding. Bots now work like a person would, using user interfaces (UIs) of common applications like SAP, SalesForce and Microsoft Office. The main limitation with RPA is that it can only automate simple tasks – RPA needs processes to follow predefined rules with structured data.


Hyperautomation permits organisations to automate more complex work. Gartner defines it as follows:

“Hyperautomation deals with the application of advanced technologies including AI and machine learning to increasingly automate processes and augment humans.”

We are excited about hyperautomation at DataRobot as it aligns to our vision of the future work, and how our customers are supplementing their RPA tools. If RPA democratized rules-based coding, DataRobot has democratized Artificial Intelligence (AI). This more intelligent automation is required to tackle end-to-end processes where people interpret data, make decisions and produce predictions.

Gartner views hyperautomation as an “unavoidable market state” as businesses must rapidly automate “all possible businesses processes” to remain competitive, with several implications that further explain the Hyperautomation trend:

  1. The scope of automation changes. The focus will shift from simple rules-based tasks to knowledge work, with greater return on investment and more dynamic experiences.
  2. A range of tools will be used to manage work. RPA alone is no longer sufficient and  companies need machine learning.
  3. Architecting for agility is required. Organisations need to be able to quickly reconfigure processes as needs evolve, requiring agile working practices and tools.
  4. Workforce engagement is needed. Employees need to reinvent their operations to achieve greater impact, requiring cross-departmental initiatives and better use of partners. 

The role of machine learning is critical as it “explodes the range of hyperautomation possibilities”. It enables the automation of processes that were once deemed exclusively the domain of knowledge workers. Yet according to Gartner, the focus will not be on replacing these workers, but primarily on improving their ability to deliver value. Machine learning will also enable adaptive and intelligence processes, that executive the next best action, instead of the same repeatable sequence.

In subsequent blogs, we’ll explore hyperautomation in further detail, including the best use cases of this more intelligent automation that combines RPA and AI.

About the author
James Lawson
James Lawson

James is responsible for educating the market about Artificial Intelligence, further accelerating adoption, and dispassionately advising executives on how best to achieve value from their transformation initiatives. Before DataRobot, he was WorkFusion’s Global Head of Strategic Markets, a leader in RPA. He is a fellow of the Adam Smith Institute and read Philosophy, Politics and Economics at the University of Oxford.

Meet James Lawson
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