Hyperautomation:
What is it, and how
does it affect
key industries?

In the wake of digital transformation, enterprises have automated multiple tasks. But many of these processes have found it challenging to scale. Hyperautomation seeks to solve this problem by providing a framework for automation and by incorporating additional technologies to deliver results at scale.

For example, consider the mortgage evaluation process where the prospective customer has to submit a range of documents for review. Making sure the customer has sent the right information could be a cumbersome process prone to errors, one that is ripe for automation. Robotic process automation (RPA) has tried its hand at this but found it difficult to scale. That's because the technology requires customers to send in rigidly structured data, with information filled out digitally. Signatures, for example, might need to be done digitally but the real world does not function that way.

Hyperautomation fills this void, using technologies like optical character recognition (OCR) and natural language processing (NLP), which can work with unstructured data (like handwritten documents) and scale automation of mundane tasks such as document verification for loan approvals in the finance sector and handling insurance claims for insurance companies.

The Gartner Glossary defines hyperautomation as "a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. This kind of automation involves the orchestrated use of multiple technologies, tools or platforms."¹

The key is that this approach delivers a framework for breaking down operations to recognize opportunities for automation and determine which technologies can tackle those individual components. In a sense, it automates the process of automation.

The process works in three stages in an endless loop:

1. Discovering and evaluating the opportunities for automation
2. Easing the process of automation and executing it
3. Leveraging advanced technologies to scale implementation

In the first step, enterprises break down each task into bite-sized pieces that are easily tackled by automation. Process mining, task mining and process analytics help understand the workflow for different tasks—A, then B and C—so they can address each sub-step in turn.

Next comes the first rung of automation with processes like RPA. Such early iterations are important not just to see what they can and cannot achieve but to also develop an automation framework that lays the right foundation. By starting here, employees also understand how automation can augment the work that they do, liberating them to focus on other tasks. At this stage, employees can also use no-code/low-code platforms to create automation processes on their own, without having to lean on IT. Manufacturing workers, for example, can teach a collaborative robot how to perform a simple task simply by tracing its arms along a prescribed path. Similarly, drag-and-drop menus allow workers to create mini automation tasks, much like creating an email filter to automatically archive incoming messages.

Once enterprises have set automation in motion, they will be able to tell where additional technologies such as artificial intelligence (AI), optical character recognition (OCR), natural language processing (NLP) and computer vision can fill in the gaps and scale automation. Especially in asset-driven industries, the deployment of the Internet of Things (IoT) at scale is looking to leverage AI at the edge, close to the source of data, with an expected boom in edge AI, as reported in a recent study by Markets and Markets on the edge AI software market. Understanding the key performance indicators (KPIs) to be measured and configuring the right hardware and software combination to truly realize the potential of IoT is one fruitful task that hyperautomation can deliver.

One of the challenges for enterprises is understanding and breaking down their processes thoroughly enough for automation to do its job. Knowing which technologies to incorporate, and when, is also critical for success. For example, using optical character recognition to read signatures in healthcare proxies can remove that particular (if small) roadblock to automated processing of healthcare forms.

While the end goals for both are usually the same, automation is loosely coupled to a technology, as Allied Market Research shows. On the other hand, hyperautomation can run across a swath of different processes and recruit a variety of technologies as needed.

The key here is the scale of execution. The latter also delivers a framework and processes for automation, so it is all-encompassing. It makes automation a part of an enterprise's DNA, something that routinely kicks into place as needed, instead of having to reinvent the wheel every time. With hyperautomation at scale, the workflow requires minimal human intervention, with all information readily understood by machines, and moving through the system freely.

This scalable kind of automation can work in practically every industry where paperwork and repetitive tasks are wasting employee talent and not effectively enhancing operational efficiency. A few use cases include:

Financial institutions can leverage this business approach to process mortgage loan applications for speedier approvals. RPA has helped to a certain extent, but hyperautomation can scale the efficiencies even more broadly.

The healthcare sector is routinely buried in paperwork and faces the additional challenge of being bound by governance rules regarding how data can be used. Speech-recognition technology (SRT) coupled with NLP is already being applied to dictated clinical records, and AI-enabled, computer-suggested coding is driving faster, more automated billing of care encounters. Whether it’s the processing of insurance claims, lab tests, or prescription refills documentation can be made easier with this more scalable way to automate healthcare office processes.

Automatically optimizing route scheduling to align with service level agreements is an example of hyperautomation in transportation. You can help lower freight risks and access greater visibility into goods and fleet locations by doing so.

Retail outlets are already working with chatbots to resolve customer questions faster and with an easier touch. The challenge is when routine automation cannot solve low-level tasks. Hyperautomation can help in these cases by using a combination of AI and computer vision (for scanning product defects, for example). 

In manufacturing and other asset-driven industries like utilities and transportation, hyperautomation derives from gaining and acting on insights from assets in real time. The first step of process mining can help create a digital twin (or virtual model), which captures live interdependencies between systems, components, and people. Continuous monitoring of processes and equipment through IoT-embedded devices can feed information into the digital twin. Information from a variety of assets routes to the centralized platform for easy visibility into operations. Manufacturers and other enterprises can implement a "lights out" strategy, letting machines run without intervention unless absolutely needed.

The increasing availability of AI and edge analytics, acting on IoT data, could help spur the use of scalable automation in these industries. AI-enabled chips and distributed computing, which enable decision-making at the source of the data, could help increase the utility of IoT. IoT-driven insights could increase the role that hyperautomation can play in asset-driven industries.

Leveraging this business approach can deliver a wide range of benefits for enterprises. These include:

As with regular automation, hyperautomation, too, can liberate employees from repetitive, lesser-value tasks and help them use their talent more strategically. Employees themselves surface problems they would rather see tackled through automation.

Automation could often be an afterthought, assigned under IT silos. Hyperautomation is a continuous process and recruits all employees in the process. It acts like another employee moving all operations toward continuous efficiencies and integrating IT more fully as one of the central pillars of the enterprise.

The scaling of operations usually requires scaled-up talent as well. In a low-touch, scalable automation model, enterprises can increase the reach of their processes by using a good mix of automation and human talent, allowing them to achieve more robust business outcomes, faster.