Gartner predicts that hyperautomation will be among the top strategic technology trends in 2022. According to the company’s research, in 2022 the hyperautomation market is expected to exceed $596 billion, which is a 24% increase when compared to 2021. Forbes refers to hyperautomation as “the next digital frontier” and highlights the need to set up a solid digital foundation before implementing this new concept within organizations. If you are wondering how hyperautomation works and how it will help your business to complete processes faster, read on.
What is hyperautomation?
Coined by Gartner in 2020, hyperautomation refers to the “end-to-end automation beyond robotic process automation (RPA) by combining complementary technologies to augment business processes.” Simply put, hyperautomation is a concept that supports the identification of processes and opportunities to automate them with the help of artificial intelligence, low-code application platforms, machine learning, and robotic process automation, among other technologies, tools, and platforms.
How does hyperautomation work?
Hyperautomation is more than an assortment of tools. It is the practice through which processes that need automation are identified. It drives agility as it uses a range of tools to reuse automated processes and reduce repetitive tasks. Hyperautomation typically refers to the following automation steps:
Discover and analyze
Organization leaders are often confused about which end-to-end processes need automation. By identifying opportunities and productive gaps, components such as cognitive technologies, AI, or Machine Learning (ML) can be used to maximize the hyperautomation of business processes effectively. Process discovery and documentation should make it easier for businesses to identify which AI or ML capabilities they require.
For instance, machine learning focuses on deep learning and supervised/unsupervised algorithms. Natural language processing focuses on machine translation, content extraction, responses, or text generation.
Process discovery tools can help businesses identify which processes require hyperautomation and will increase business value.
Design and automate
Once processes are selected for automation, they are modelled in order to understand the flow from start to finish. Exception scenarios are identified to get the most out of automation. Some processes or parts of processes can be automated first and then built upon to save time.
The development of automation implementation can be carried out in phases to avoid any issues.
Data plays a crucial role in determining the success of hyperautomation. Inaccuracy of data can compromise the results. Both algorithms and ML require accurate and properly labelled data that can be analyzed to provide the most accurate results possible.
Integrations are another important aspect of design; running pilots to evaluate the efficiency or performance of a process in real time allows businesses to decide which processes should or should not be included. This stage also allows business leaders to communicate the long-term goal with stakeholders to plan ahead.
Measure, monitor, and reassess
Using key metrics and constantly monitoring processes is key to the project’s success. Hyperautomation requires management to be aligned with new processes and systems. Measuring, monitoring, and reassessing priorities ensures that your business is keeping up with the competition or digital trends. Plus, it ensures that you can exploit hyperautomation to its fullest.
Measurements can help you identify issues at the earliest opportunity, while metrics can help you evaluate which processes provide higher return on investment (ROI).
Hyperautomation or automation: which is better?
Hyperautomation is an extension of automation as it deals with more advanced technologies and consequently a larger range of tools that can be automated. Both intelligent automation and hyperautomation refer to streamlining processes using advanced technologies that result in improved customer interactions.
Hyperautomation differs from automation as it is more of a business-driven approach that is implemented in organizations to identify, evaluate, and automate business and IT processes. Automation, on the other hand, is less of a concept and is limited to test automation and RPA, both of which allow for quicker and more efficient processes.
To get a clear grasp of whether automation or hyperautomation is better for your organization, we need to compare them based on a few parameters.
- Technologies utilized: unlike automation, which is RPA-based and task-oriented, hyperautomation encompasses ML, packaged software, NLP, and other automation tools.
- Scope: hyperautomation is known as a mix of systems, technologies, and platforms, whereas automation is typically conducted from a single platform.
- Outcome: automation allows you to make some business processes more efficient, whereas hyperautomation takes it to the next level by evaluating all processes and identifying those that can be effectively automated.
- Coverage: hyperautomation allows you to find everything that can be automated to reduce manual processes and optimize workflows.
Benefits of hyperautomation
Hyperautomation helps create a digital twin (a digital representation of a process or organization) of businesses where interactions between processes, KPIs, and functions become visible. The digital twin becomes part of the hyperautomation process and, hence, provides real-time and continual observation about the business.
Hyperautomation optimizes productivity and innovation. It makes processes more efficient and converts them into higher-quality products or services along with rapid delivery to customers. The implementation of hyperautomation means that employees can be more productive and use their skills for problem solving and innovation.
The integration of intelligence tools together with RPA enables the handling of data found, making it easier to identify automation possibilities where RPA alone is not sufficient.
Processes that are automated provide data for insightful process analysis. Process performance can also be evaluated according to ROI and KPIs, which allows improved data-driven decisions.
Forbes recommends considering the benefits of hyperautomation after conducting an extensive audit that covers interdepartmental structures, and the maturity and goals of the business in terms of technology. Organizational culture, implementation speed, and external factors such as competition are other elements that need to be considered before adopting hyperautomation.
Examples of hyperautomation in action
Let;s start with the example of credit brokers that usually have intelligent systems and yet have lending systems that are opaque and slow. Through hyperautomation, processes such as cash loans and building up client profiles can be sped up and made simpler, much more transparent elements that are key to customer satisfaction.
AI can also underwrite complex cases within seconds, while the same process would take minutes to be underwritten before hyperautomation. Total automation allows organizations to save time and offer better services through AI that can search, process offers and deliver tailored results to applicants.
Integrating AI, RPA, and NLP enables hyperautomation to conduct triage and open cases according to customer requirements. Thus, emails are classified and sent to the right departments for processing. The request is handled and pulled from the mainframe, which provides an accurate response to the customer’s query. Hyperautomation makes it easier to keep up with customer requirements. All this, with no need for any manual coding!
Other generic examples of hyperautomation include:
- Retail: automated restocking and forecast of stock; first point of contact for customers with an extensive product knowledge base.
- Back-office hyperautomation in areas such as budgeting and payroll management.
- Using optical character recognition (OCR) to understand documents.
It is important to point out that hyperautomation varies according to the requirements of individual cases. For instance, process mining is likely to incorporate ML to identify process issues and transform these inefficiencies into automated workflows, whereas virtual agents are likely to be powered by AI to capitalize on document intelligence or NLP to provide automated customer service.
How to tackle the challenges of hyperautomation
While there are no downsides to hyperautomation per se, there are a few challenges that come with its implementation:
- Setting success metrics.
- Setting up and sticking to a project timeline.
- Acquiring stakeholder and management support due to the novelty of hyperautomation.
- Ensuring that business leaders are constantly monitoring the project.
- Calculating ROI, both tangible and intangible.
It is necessary for business and technical experts who understand the processes and the requirements to be on board with the project from Day 1 to ensure that the project goes smoothly.
By involving all stakeholders in the change process, you can dissipate the concerns of employees and establish the need for automated processes. Recognizing constraints from the beginning will help you mitigate risks. Process mining can be used to evaluate processes and implement process maps.
The aim of adopting hyperautomation is to combine employee skills with system capabilities to reduce and even eliminate repetitive tasks. And yet, we should keep several things in mind when determining the success of hyperautomation:
- Hyperautomation should be linked to the organization’s KPIs.
- ML should enable people to make better and faster decisions.
- Using agile processes will enable teams to enhance hyperautomation.
Hyperautomation is likely to function best in organizations with a proper digital foundation. Organizations that expect hyperautomation to replace total human intervention are bound to fail since some offline operations cannot be automated. Without a well-structured digital strategy in place, the use of the technologies that make up hyperautomation will simply be a financial and cognitive burden to enterprises.
How are low-code environments crucial to hyperautomation?
According to Fabrizio Biscotti, a research vice-president at Gartner, the low-code development platform is “probably the biggest single element that is impacting hyperautomation.” Process-agnostic tools like RPA, LCAP, and AI are said to be among the top tools that will be adopted as they can be utilized for a multitude of business use cases. It is expected that at least 3 process-agnostic hyperautomation software applications will be adopted by businesses by 2024, with low-code being one of the most used in app development.
All this makes low-code solutions central to the success of hyperautomation.
The low-code approach requires minimal coding skills to build both simple and complex applications and business processes. A graphical user interface (GUI) is used to allow users with few coding skills or experts to drag and drop functionalities to build apps.
Low-code development platforms (LCDPs) offer easy integration with multiple systems, such as Jira, SharePoint, Microsoft Dynamics, to name a few. An LCDP also enables users to connect to a variety of databases, ranging from SQL Server to MySQL.
Visual drag-and-drop interfaces make it possible for business analysts to create business apps within days or weeks meaning that employees’ work can be streamlined faster.
Low-code application platforms (LCAP) are closely linked to hyperautomation as both focus on working smarter. By leveraging process intelligence using an LCDP, document and email bots can utilize AI to optimize processes and deliver smarter results.
There are other significant advantages of utilizing low-code platforms to achieve hyperautomation.
LCAP makes it easier to build strategic apps. Automation allows the overall improvement of a team’s productivity. Additionally, using a low-code automation platform will make it easier to cope with skill shortages and deliver your internal IT projects without calling on professional developers.
Furthermore, low-code automation decreases the load of technical debt that is gained with new app builds. An LCAP allows application upgrades and automation without the need to rewrite codes, meaning less time is spent on updates.
- Hyperautomation is a means of combining modern tech tools known as the DigitalOps Toolboox which encompasses RPA, LCAP, decision management suites, workflow engines, and more.
- Low-code development platforms allow developers and non-developers alike to build applications faster. With the practices of hyperautomation, the benefits of low-code platforms are further enhanced through more effective app integrations or mobile applications, amongst other benefits.
- Together with hyperautomation, low-code application platforms make it easier for businesses to shift from traditional software development processes to automated, efficient processes.
- Hyperautomation equips IT teams with modern tools and AI for streamlined, error-free outcomes.