Hyperautomation is a concept involving the use of an ecosystem of advanced automation technologies to augment enterprises' use of human intelligence. The aim is to create increasingly automated business processes so that better-informed and more agile organizations can capitalize on data and insights for more efficient decision-making. These advanced technologies include:

The emerging concept has different names: While Gartner coined the term "hyperautomation," Forrester refers to the concept as digital process automation (DPA) and IDC calls it intelligent process automation (IPA).

How does hyperautomation work?

Rather than referring to one single, out-of-the-box technology or tool, hyperautomation centers on adding more intelligence and applying a broader set of tools than in the past. In theory, since no single technology can replace humans, hyperautomation needs to combine many tools in order to encourage organizations to consider strategic ways to derive the maximum benefits of the technologies they use in their businesses and to automate, simplify, discover, design, measure and manage workflows and processes across the enterprise.

Hyperautomation processes enable business experts and front-line workers with little or no coding ability to automate low-level digital tasks. Previously un-automatable tasks can be automated so that human capabilities can be focused on higher-level tasks, such as making decisions, interpreting tallies of structured data and unstructured data, and applying common sense.

Additionally, hyperautomation often produces a Digital twin of the organization (DTO). A virtual representation of a product or workflow across its lifecycle, a DTO enables "organizations to visualize how functions, processes and key performance indicators interact to drive value," according to Gartner. As an important part of the hyperautomation process, the DTO provides "real-time, continuous intelligence about the organization and driving significant business opportunities." 

A hyperautomation, or digital process automation, platform can sit directly on top of the technologies companies already have. The gateway to hyperautomation is RPA, another emerging concept, which applies software with AI and machine learning functions to handle high-volume, repeatable tasks that previously required humans to perform. In addition to RPA, hyperautomation includes business process management suites (BPMS/intelligent BPMS), integration platform as a service (iPaaS) and insight engines.

Hyperautomation vs. automation

In contrast, traditional automation capabilities involve AI and robotic devices programmed to perform particular tasks. The International Society of Automation defines the term as "the creation and application of technology to monitor and control the production and delivery of products and services."

Hyperautomation focuses on two sides of the same coin -- incorporating AI (analysis of historical data) and automation tools (including rule-based action on real-time data). When combined with human efforts on more complex tasks, the entire workflow stays informed and can employ data efficiently and the workforce's agility is improved.

Benefits and challenges

Hyperautomation helps enable the workforce, transform organizations through business and IT alignment, use AI to enable end-to-end automation and provide crucial insight into ROI from automation. This development of people-centric smart workplaces leads to increased efficiencies and cost savings. Gartner predicts that, by 2024, organizations will cut operational costs by 30% through a combination of hyperautomation technologies and redesigned operational processes.

By augmenting the work of human beings with automated technology and tools, hyperautomation aims to promote sustainability while saving money and generating more revenue. However, gathering the right tools and applying them effectively is challenging for organizations that need to understand the breadth of automation mechanisms, how they relate to one another and how they can be combined and coordinated. As the use of hyperautomation expands, software must be interoperable, and when new technologies are introduced, having systems that are plug-and-play can help organizations scale their operations efficiently.

In data-intensive fields, particularly those related to the Internet of Things (IoT), hyperautomation may prove beneficial in the future. For now, many companies seeking to tap IoT opportunities need first to focus on device connectivity and data collection challenges. The next step is using analytics and AI technology to extract business insights from the data -- opening potential opportunities for hyperautomation.


The tools themselves are only part of the hyperautomation equation. Consider the case of social media and customer retention: A company can depend on tools that leverage RPA and machine learning to produce reports and pull data from social platforms to determine customer sentiment, and information will be readily available for the marketing team. However, the marketing team will need to apply these insights to consider what type of campaigns, promotions and incentives to incorporate into a business plan to retain satisfied customers and potentially address those who are dissatisfied. 

As automation increasingly takes over lower-level and repetitive tasks, business leaders, such as chief financial officers (CFOs), can gain greater insights from automated reports and apply logic to identify possible risks and opportunities more quickly and efficiently.

Advanced automation technologies and techniques should be applied across organizations, people and processes. If an enterprise launches a product quickly and digital process automation metrics show strong customer demand for it, then the product can be rapidly scaled to help the company grow its revenue, but if advanced analysis shows that the product fails to gain traction among customers, the company can drop it quickly and minimize its losses.

This was last updated in June 2020

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