What Is Hyperautomation?
You’ve probably heard the words “hyperautomation” and “hyperproductivity” being used more frequently in the workflow automation market, but what do these words mean? The term “hyperautomation” appeared in October of 2019, taking the top spot on Gartner’s Top 10 Strategic Technology Trends for 2020 list. Yet the concept of hyperautomation is also encompassed within other industry terms. For instance, Forrester refers to it as “digital process automation,” while IDC and others use “intelligent process automation.”
Regardless of which term is used, hyperautomation is a powerful set of digital technologies that will continue to transform organizations across nearly every industry. In this article, we will explore what hyperautomation is, the digital technologies that it consists of, and the many benefits that it offers.
What we’re covering here
- Hyperautomation definition
- How hyperautomation works
- Hyperautomation examples
- Hyperautomation use cases
- Benefits of hyperautomation
What is hyperautomation?
Hyperautomation refers to the use of advanced technologies, like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), to automate tasks that were once completed by humans. Hyperautomation not only refers to the tasks and processes that can be automated but also to the level of automation. It’s often referred to as the next major phase of digital transformation.
It’s important to note that hyperautomation is not meant to entirely replace humans. Rather, through automation, humans are freed from repetitive, mission-critical tasks to focus on ones that are of a higher-value to the organization (ie., the reason you were hired). Together, automation and human involvement help organizations to provide superior customer experiences while reducing operational costs and boosting profitability.
By using a combination of automation technologies, hyperautomation can overcome some of the limitations of approaches that rely on a single automation tool, allowing organizations to move beyond the confines of individual processes and automate nearly any tedious and scalable task.
- Automation requires careful planning and implementation.
- It’s necessary that organizations understand how digital technologies will fit into their new and existing workflows.
- There can be major consequences to introducing automation without considering the role that it will play.
The key components of hyperautomation software
There are several automation technologies that comprise hyperautomation. These include:
- Robotic process automation (RPA)
- Business process management (BPM)
- Artificial intelligence (AI) and/or Machine learning (ML)
- Advanced analytics
Robotic process automation (RPA)
The first wave of automation technologies largely relied on robotic process automation (RPA). RPA involves the use of bots to mimic repetitive human tasks. These processes are rule-based and utilize structured data to complete actions. Unlike artificial intelligence which seeks to simulate the human intellect, RPA focuses solely on human actions. With hyperautomation, digital workers operate alongside humans to deliver unmatched efficiency.
RPA leverages technology like software bots to replicate repetitive human tasks. RPA typically works for tasks that are rule-based, have defined inputs and outputs, are repeatable, and occur often. One limitation of RPA is that it is limited to structured data to complete tasks. Thus, RPA does not have the ability to understand context or learn, nor can it access and make sense of unstructured data sources like images.
Intelligent document processing (IDP)
Moving aside from RPA, intelligent document processing (IDP) is a game-changer for organizations looking to tackle more complex tasks. Unlike RPA, IDP is excellent at sorting through unstructured documents (think receipts, handwritten notes, PDFs, word files, images, and so on). Because of its advanced capabilities, IDP is a more flexible option for your organization.
Business process management (BPM)
Business process automation (BPM) is one of the most important components of hyperautomation. In many ways, it’s the foundation on which any successful automation strategy is built, monitored, and improved. Introducing different digital tools into business processes, especially for those organizations new to automation, can be challenging.
Organizations must create new workflows and test them prior to deploying them to avoid breakdowns that can have disastrous consequences for their businesses. Business process management software is a powerful and simple tool that can be used to manage an organization’s hyperautomation strategies and initiatives.
Artificial intelligence and/or machine learning
Artificial intelligence (AI) is a method of making computers operate in ways that simulate human intelligence. Organizations use AI to carry out specific tasks without being explicitly programmed to do so. Common examples of AI are virtual assistants like Apple’s Siri, Amazon Alexa, and marketing technologies that suggest products you may be interested in based on past behavior. In 2023, about 36% of people use Apple’s Siri, and 25% use Amazon Alexa.
Machine learning (ML), often used synonymously with AI, is a branch of AI that uses computer algorithms to allow systems to automatically improve over time. Organizations use both supervised and unsupervised algorithms to identify patterns in data. Supervised algorithms create inputs and outputs before making predictions on their own. Unsupervised algorithms observe structured data and develop insights from pattern recognition.
AI and ML are powerful automation tools. Yet implementing them can require a significant investment of resources and careful planning to ensure integration with other technologies and processes. For these reasons, achieving hyperautomation requires the strategic deployment of AI and ML.
Hyperautomation offers organizations powerful analytical tools and capabilities, thus overcoming the data limitations of relying on a single automation tool like RPA. While RPA is limited to structured data, hyperautomation technologies can handle both structured and unstructured data. This helps organizations access and analyze data that has traditionally been inaccessible to gain important organizational-level insights.
Hyperautomation can also convert unstructured data into structured data for use with RPA technologies. This relationship is an illustration of how various digital tools work together seamlessly to offer unmatched efficiency.
Benefits of hyperautomation
Hyperautomation offers many benefits and potentially unlimited upside. Some major benefits of hyperautomation include:
- Flexibility. Since hyperautomation relies on a multitude of automation technologies, organizations can move past the limited benefits of a single digital technology. This helps organizations to achieve scale and flexibility in operations.
- Improved employee productivity. By automating time consuming tasks, employees are able to get more done with less resources and serve more valuable roles in organizations.
- Integration. With hyperautomation, organizations can integrate digital technologies across their processes and legacy systems. Stakeholders have better access to data and can communicate seamlessly throughout the organization.
- Improved ROI. Hyperautomation boosts revenue and reduces costs. With powerful analytical tools and capabilities, organizations can optimize the deployment of their resources.
Overall, hyperautomation simplifies your job by reducing manual effort, minimizing errors, improving consistency, providing better task management, and accommodating growth and change. By embracing hyperautomation, you can focus on higher-value activities, enhance productivity, and achieve better outcomes in your work. Chat with us to learn more about BPA and which software is right for you. At the end of the day, your company deserves to find the right fit, even if it isn’t us!