Hyperautomation is a combination of artificial intelligence, machine learning, robotic process automation (RPA) and other advanced technology used to strategically streamline any and every process possible in a given organization. This approach enables businesses to not only automate manual workflows, but extract value from them as well.
In a nutshell: hyperautomation automates enterprise automation.
Hyperautomation is a businesss-driven approach organizations take to strategically streamline as many processes as possible. It uses advanced technologies such as AI, machine learning, robotic process automation and no-code or low-code development tools to build a framework that scales automation.
Coined by IT research and advisory firm Gartner in a 2019 report, the concept of hyperautomation came out of not only digital transformation, or the act of taking a business practice from the physical world to the digital one, but intelligent automation as well — where AI is embedded in the digital solutions themselves.
Tech entrepreneur Robb Wilson likens intelligent automation to autonomous vehicles, which comprise a complex system of various technologies to not only keep the car moving safely, but continue improving over time. Wilson is the founder and chief technologist at OneReach. ai, a company focused on conversational AI innovation.
“Intelligent automation continually improves without human intervention using machine learning. True self-driving cars, while in their infancy, are a good touchpoint,” Wilson, who is also the author of recent best-selling book Age of Invisible Machines: A Practical Guide to Creating a Hyperautomated Ecosystem of Intelligent Digital Workers, told Built In. “When applied correctly, intelligent automation can make companies self-driving as well.”
Hyperautomation is the “next evolution of this journey,” Edward Dowgiallo, chief strategy officer of automation tech startup iShift, told Built In.
“Hyperautomation is about using AI to identify further ways of creating efficiencies or automation itself,” he added. “As a business, you need to do these things to keep up with competition.”
How Does Hyperautomation Work?
Typically, the hyperautomation of a given company starts with the creation of a digital twin of the organization which is a virtual representation of how a company’s processes work. The digital twin is automatically generated through a combination of process mining and task mining — enabling the organization to better visualize how certain functions, processes and key performance indicators interact with each other and create value, as well as how new automations could either drive even more value or create new issues.
Once this groundwork is laid, one of the most important ingredients of hyperautomation is robotic process automation software, which programs virtual bots to tackle repetitive, often back-office tasks. This provides human employees with more time to perform more creative or complex tasks for their organization.
The appeal of RPA is that it saves money and increases efficiency, but it isn’t necessarily conducive to scaling, which is why it is paired with other technology. Other tools necessary in a hyperautomation framework include no-code or low-code applications to automate development, artificial intelligence to automate previously inaccessible data and processes, as well as other facets of AI like machine learning, natural language processing, optical character recognition and more.
Technology Used in Hyperautomation
- Process mining and task mining tools to help identify and prioritize opportunities for automation.
- Robotic process automation tools for automating repetitive, back-office tasks.
- No-code and low-code applications to automate development.
- Artificial intelligence and machine learning to automate previously inaccessible data and processes.
Another key component of hyperautomation is conversational AI, which combines natural language processing and machine learning to engage with people in a two-way human-like exchange, Wilson said. Through conversational AI, humans can interact with their machines in a more natural way, essentially rendering those machines “invisible” and “omnipresent.”
“When organizations are in this state of hyperautomation, they enjoy a force-multiplying effect that sends them surging past competitors.”
“In business settings, conversational AI allows organizations to create their own automations,” he continued. “Those automations can be created and iterated on in the space for days, not the months and years typically associated with software development. When organizations are in this state of hyperautomation, they enjoy a force-multiplying effect that sends them surging past competitors.”
Therefore, if a company wants to achieve hyperautomation itself, Wilson said one of its first steps should be to create an “intelligent communication fabric” capable of connecting all of their people, systems and technology. “This is the bedrock of hyperautomation and allows for meaningful conversations between humans and machines.”
Hyperautomation vs. Automation
Put simply: The key difference between automation and hyperautomation is that automation only streamlines repetitive tasks, reducing the need for human intervention, whereas hyperautomation takes it a step further. It adds advanced technology into the mix and puts an intelligent layer of processing on top, making it possible for businesses to not only automate manual workflows, but extract value from them as well.
Traditional approaches to business automation involve zeroing in on highly specific tasks contexts. Workload automation, for example, schedules and manages the tasks related to a particular business process without human intervention. And business process management tools automate tasks within the conditions of a specific workflow.
Hyperautomation vs. Automation
- Automation: Streamlines repetitive tasks, reducing the need for human intervention.
- Hyperautomation: An approach that helps businesses scale their automation efforts and extract more value from it.
But hyperautomation is more than just simple automation — it involves intelligence, integration, digitization, optimization as well as automation. And hyperautomation does not simply stop when it brings all these tools together. It also ensures that all these programs and software can work in harmony with each other, resulting in the streamlining of more complex tasks that were previously considered not automatable.
Much of this is done with the help of artificial intelligence, which extends traditional automation to take on more tasks. For instance, optical character recognition can be used to automate the reading of documents, while natural language processing automates the understanding of them. Meanwhile, natural language generation automatically provides digestible summaries of those documents to humans. Hyperautomation makes it easier to incorporate this technology into an existing automation system.
“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 analysts wrote in the 2019 report. “Hyperautomation refers to an effective combination of complementary set of tools that can integrate functional and process silos to automate and augment business processes.”
Benefits of Hyperautomation
As companies continue to adopt and master hyperautomation, there are all kinds of ways their businesses can benefit.
Streamlining Operations and Saving Money
Hyperautomation helps companies extract insights and knowledge from their data more efficiently, and it streamlines the rote, time-consuming tasks humans don’t typically want to do themselves. It also helps save money — the recent Gartner study found that, by 2024, organizations will lower their operational costs by 30 percent by combining hyperautomation technology with redesigned operational processes.
Enabling Intelligent Workflows
Perhaps most importantly though, hyperautomation can bring a new level of intelligence to an organization’s existing workflow, allowing it to not only harness the power of automation, but extract real value from it.
But, of course, no technology is perfect. So, before embarking on a hyperautomation journey of their own, it’s important for companies to understand some challenges that commonly come with this approach.
The Challenges of Hyperautomation
- Its complexity makes it difficult to implement the technology successfully.
- It isn't always easy to actually measure a given approach's success.
- The constantly growing marketplace of relevant tools can be overwhelming, making it difficult to find a good fit for a given company.
- It requires a lot of change within the company itself, sometimes requiring the breaking down and rebuilding of entire departments.
Difficult and Time Consuming to Implement
First, achieving hyperautomation is a complex journey, and there’s a danger in companies “trying to jump to the end of the story too quickly,” iShift’s Dowgiallo said. “There is a progression that must happen to get to hyperautomation. First, you must create a successful digital product people use that you collect data from. Then you need to mine your data and develop a capability to use data in your organization.”
As soon as this happens, they can start building AI, so long as they have realistic use cases where the AI can be successful.
“Once you are successful at making predictions with AI and have built some tools in your digital products, you will be ready for hyperautomation, where you are optimizing and letting data help predict new needs for services or further optimizations,” Dowgiallo continued. “AI is not new, but a lot of companies fail to have data capabilities to succeed with hyperautomation.”
Hard to Measure Success
Another challenge with hyperautomation is how difficult it is to actually measure a given approach’s success, or even develop and adhere to a realistic approach to begin with.
An Overwhelming Marketplace of Tools
The marketplace for tools available in this space is growing all the time, so keeping track of everything that’s available (and picking the ones that are a good fit) can be a daunting task from companies looking to implement hyperautomation themselves.
Change Can Require Rebuilding
The biggest challenge with hyperautomation, according to OneRead.ai’s Wilson, is the level of change that is required to achieve it. But he encourages companies to “embrace” that change, and to lean into the discomfort that comes with it.
“Organizations that have spent the last decade or so unifying the back end of their operations now have the opportunity to unify their front end as well with a single conversational interface, closing a technology loop and creating an ecosystem primed for hyperautomation,” he said. “Getting it right might require ripping the guts out of entire departments and rebuilding them anew. This is a big and scary risk, but there is hope.”
Hyperautomation Use Cases
Many companies are tapping into hyperautomation’s potential. Its ability to cut costs, increase productivity, enable compliance and more has led to a surge of implementation across a variety of industries.
Like automation, hyperautomation can be used in virtually any industry. And, in some cases, the technology is customizable or already customized for the needs of that particular sector. As a matter of fact, according to a recent study by Gartner, 80 percent of hyperautomation offerings will have “limited industry-specific depth” by 2024 — meaning, in just a couple years, we should expect to see an onslaught of hyperautomation solutions with industry-specific functionality.
- Enhancing patient experience and increasing compliance in healthcare.
- Simplifying the extraction, verification, sorting and storage of data.
- Streamlining the recruitment process.
- Maximizing productivity and reducing errors in the industrial industry.
- Reducing costs and improving customer experience in the insurance industry.
Now that we’ve established an understanding of what hyperautomation is and how it works, let’s dive into the ways it is being applied in specific industries and areas of tech.
The healthcare industry is full of contractual obligations to stick to, regulations to comply with and all manner of repetitive processes — making it a prime candidate for hyperautomation, using technology like natural language processing and robotic process automation.
For example, virtual bots can automate the monitoring of quality protocols against scores like the Healthcare Effectiveness Data and Information Set and the Hospital Consumer Assessment of Healthcare Providers and Systems, and ensure that compliance records are accurately maintained and kept up to date. They can also help with tasks like claims processing and inventory management, freeing up human workers to handle the more complex aspects of their jobs — like taking care of patients.
Data is the lifeblood of virtually every company across all industries, holding immense promise for innovation and intelligence. Taking advantage of all of this data is a colossal undertaking, though, simply by virtue of just how much data businesses have access to at any given time. And data is only as good as the insights gained from it. Hyperautomation makes data more accessible in a seamless, less time-consuming way by taking over the more manual aspects of data processing, including extraction, verification, sorting and storage. It can process high volumes of data on a daily basis, as well as perform advanced analytics to generate meaningful insights businesses can then use.
Most recruitment teams are already familiar with the benefits of automation. Afterall, would you rather spend days reading through hundreds of resumes, or have a screening software do it for you in a matter of minutes? Hyperautomation takes this efficiency a step further, handling even more repetitive, time-consuming aspects of the job, such as sorting through spam, spotting potential candidates and filtering through undesirable applications on the bases of pre-fed parameters. Plus, RPA, AI and other hyperautomation frameworks ensure the data stored by businesses about job candidates is fully compliant.
As customers continue to take their interactions with the businesses they patronize online, technology that enables companies to respond to customers more quickly, address their questions or concerns, and proactively deliver effective solutions across all communication channels is of the utmost importance. Therefore, they’ve begun leaning on hyperautomation more and more.
For instance, chatbots — computer programs that use AI to efficiently converse with human users — can serve as the first line of communication between businesses and their customers, and are often powerful enough to resolve most issues without human intervention at all. These chatbots can also easily be integrated with more intelligent technology like business process management and platform-as-a-service software to both improve customer experience and respond quickly to queries.
The industrial sector is perhaps one of hyperautomation’s most enthusiastic adopters. In fact, that aforementioned Gartner study predicts that, by 2025, more than 20 percent of all products will be manufactured, packed, shipped and delivered without being touched by a human — so the person who purchases the product will be the first person to touch it.
Right now, hyperautomation is mainly being used in this space to maximize productivity and reduce errors among all the various connected devices that make up today’s warehouses and industrial facilities. It also takes on aspects of their supply chain and logistics processes, such as onboarding new vendors, processing invoices and managing inventory. Plus, by blending technology like natural language processing and optical character recognition, they can analyze their data more efficiently and derive better insights.
Lead generation — the practice of attracting prospective customers with marketing materials and guiding them toward becoming paying customers — tends to be one of the more cumbersome aspects of the sales process. This is mainly because of people’s general reticence toward sharing personal information with a company they aren’t very familiar with.
But hyperautomation can be used to help businesses more efficiently tap into the IP address and other relevant details of incoming traffic to their website or app, and then track where people go and what they do while they’re on the platform itself. All that data can then be automatically stored and diverted onto a separate outreach platform, where it can be organized depending on whether or not a person from the sales team should be pursuing them as a lead. It also helps companies figure out who they should be advertising to and how based on specific target audiences. With hyperautomation, everything up to the sales rep’s responses can be automated.
The finance industry is a rather complicated one, involving all sorts of calculations, transactions and communications between customers, buyers, fund managers and more. Hyperautomation enables the banks and financial service companies within this space to streamline their otherwise manual, data-intensive operations, while at the same time meeting the stringent and constantly changing regulatory requirements that tend to slow things down.
Hyperautomation solutions like AI, ML and RPA bots also can be used to develop accurate client risk profiles, support fraud identification, conduct ongoing account monitoring and even ensure compliance with legal requirements like “know your customer” and anti-money laundering regulations — both of which have become increasingly important in this space. As online transactions become increasingly common, companies can implement this technology to flag signs of potential money laundering and nip them in the bud, as well as collect and process customer data, conduct post-payment follow-up confirmations and more.
Robotic process automation and artificial intelligence automate the most mundane and complex tasks of insurance operations, from claims management to underwriting and actuarial analysis — all of which requires quite a bit of data processing and analysis. It also enables customer service representatives to quickly aggregate customer and product information, address service requests, cross-sell insurance products and interact with underwriters in real time.