The ultimate goal of hyper-automation is to develop a process for automating enterprise automation. Hyperautomation is a framework and set of advanced technologies for scaling automation in the enterprise.
The following advanced technologies are employed in hyper-automation:
Tools for finding and prioritizing automation opportunities include process mining and task mining.
Structure automation development tools that cut down on the time and money it takes to automate a building. RPA, no-code/low-code development tools, integration iPaaS, and workload automation technologies are among them.
Intelligent business process management, decision management, and business rules management are examples of business logic technologies that make it easier to adapt and reuse automation.
Tools for increasing the capabilities of automation using AI and machine learning. Natural language processing (NLP), optical character recognition (OCR), machine vision, virtual agents, and chatbots are among the techniques available in this field.
Gartner, an IT research and advisory group, invented the phrase hyper-automation in 2019. The idea stems from the realization that robotic process automation (RPA), a relatively new and hugely popular way to automate computer-based processes, is difficult to scale at the corporate level and has limitations in terms of the types of automation it can do. Hyperautomation is a paradigm for deploying several automation technologies (including RPA) strategically, either alone or in tandem and augmenting them using AI and machine learning.
Hyperautomation refers to a method of automating that has been thoroughly researched. Hyperautomation entails determining which tasks to automate, selecting appropriate automation technologies, increasing agility through the reuse of automated processes, and enhancing their capabilities utilizing various AI and machine learning flavors. Hyperautomation projects are frequently organized through a center of excellence (CoE) that aids in the automation process.
Hyperautomation’s goal is to take advantage of the data collected and generated by digitized processes, not just to save money, increase productivity, and achieve efficiencies through automating automation. Companies can use that data to make more informed and timely business decisions.
Why is hyper-automation important?
Hyperautomation provides a framework for enterprises to expand, integrate, and optimize enterprise automation. It builds on the successes of RPA tools while also addressing their flaws.
RPA’s quick adoption is due to its ease of use and intuitive nature, as compared to other automation technologies. Employees can automate some or all of their work by documenting how they perform a task, for example, because RPA mimics how people interact with programs. Because bots mimic human activities, firms can measure the automated work tasks for speed, accuracy, and other criteria that are used to evaluate employee performance on the same jobs.
However, early RPA initiatives had a significant problem for enterprise use: the technology was difficult to scale. According to a Gartner report from 2019, just approximately 13% of businesses were able to grow early RPA initiatives. Enterprises must consider the types and maturity of technology and processes required to grow automation initiatives as a result of hyper-automation.
The focus of hyper-automation, according to Gartner, is on how businesses can create a mechanism for automating automation. This distinguishes hyper-automation from other automation frameworks that merely aim to improve automation tools, as well as automation ideas such as digital process automation (DPA), intelligent process automation (IPA), and cognitive automation, which are all concerned with automation itself.
Hyperautomation takes a step back to evaluate how to speed up the process of discovering automation possibilities and then automatically generating the necessary automation artifacts, such as bots, scripts, or workflows that may use DPA, IPA, or cognitive automation components.
Digital worker analytics, a supplement to hyper-automation, focuses on performance and process: for example, how to measure the cost of building, installing, and managing automation to compare the cost to the value delivered, according to Forrester Research. This research is critical for determining where future automation efforts should be focused. The majority of RPA and enterprise automation companies are now including digital worker analytics in their products.
Hyperautomation focuses on adding more intelligence and applying a larger systems-based approach to growing automation efforts, rather than referring to a particular, out-of-the-box technology or application. The method emphasizes the significance of finding the correct balance between automating manual tasks and streamlining complex processes to reduce steps.
The question of who should be in charge of automation and how it should be done is crucial. Frontline workers are more likely to notice tedious chores that could be automated. Experts in business processes are better positioned to spot the potential for automation in situations where numerous people are involved.
The concept of a digital twin of an organization was suggested by Gartner (DTO). This is a simulation of how business operations operate. A mix of process mining and task mining is used to automatically construct and update the process representation. Process mining is a method of constructing a representation of process flows by analyzing enterprise software logs from business management software such as CRM and ERP systems. Task mining creates a view of processes that span many programs using machine vision software that runs on each user’s desktop.
Process and task mining technologies may produce a DTO for you automatically, allowing you to see how functions, processes, and key performance indicators interact to drive value. The DTO can assist businesses in determining how new automation brings value, open up new opportunities, or create new bottlenecks that must be handled.
Automations can interact with the world in more ways thanks to AI and machine learning components. OCR, for example, enables an automated procedure to extract text or numbers from paper or PDF documents. Natural language processing can extract and organize data from documents, such as determining whose company an invoice is from and what it is for, and automatically capturing this information into the accounting system.
A hyper-automation platform can be built directly on top of existing technology. RPA is one of the first steps toward hyper-automation, and all of the major RPA vendors are now supporting process mining, digital worker analytics, and AI integration.
Other low-code automation platforms, such as business process management suites (BPMS/intelligent BPMS), integration platform as a service (iPaaS), and low-code development tools, are also incorporating more hyper-automation technology components.
Hyperautomation vs. automation
Traditional enterprise automation approaches focused on the best way to execute automation in a specific scenario. This automation was tailored to a certain piece of software. Workload automation, for example, employs scripts to automate several extremely repetitive tasks. Tasks can be automated using BPM technologies within the context of a workflow.
AI expands traditional automation to include more jobs, such as reading documents with OCR, understanding them with natural language processing, and providing summaries to humans with natural language generation. Using pre-built modules given via an app store or organizational repository, Hyperautomation makes it easier to include AI and machine learning capabilities into automation.
Low-code development tools lower the amount of programming knowledge needed to implement automation. Hyperautomation could make the creation of automation even easier by employing process mining to find and generate new automation prototypes automatically. To improve quality, these automatically-generated templates must now be modified by people. Improvements in hyper-automation, on the other hand, will lessen this manual effort.
What are the benefits of hyper-automation?
The following are some of the most significant advantages of hyper-automation:
Reduces the cost of automation and increases IT-business alignment
Shadow IT is reduced, which increases security and control.
Improves AI and machine learning adoption incorporate processes
Enhances the capacity to assess the success of digital transformation initiatives.
Aids in the prioritization of future automation efforts
There are numerous ways that organizations can employ hyper-automation to improve company operations as they grasp the discipline.
A corporation might utilize RPA and machine learning to create reports and extract data from social sites to gauge customer sentiment in the area of social media and client retention. It may devise a method for making that data readily available to the marketing department, which would then be able to build real-time, targeted customer campaigns.
If a company swiftly develops a product and digital process automation measurements show high customer demand, the product can be quickly scaled to help the company gain revenue. If the advanced analysis reveals that the product isn’t gaining momentum with buyers, the corporation can cut costs by discontinuing it soon.
What are the challenges of hyper-automation?
Hyperautomation is a relatively new concept, and businesses are still working out how to put it into effect. The following are some of the most significant obstacles:
Choosing an organization’s CoE strategy. Some businesses will benefit from a more centralized strategy for handling large-scale initiatives, while others will benefit from a federated or distributed approach.
Tools. Hyperautomation software isn’t a silver bullet. Even though prominent automation suppliers are extending their hyper-automation capabilities, companies will face challenges ensuring compatibility and integration between these solutions.
Governance and security. In-depth monitoring and analysis of business processes that span several departments, services, and even country borders can benefit all hyper-automation initiatives. This could result in a slew of new security and privacy concerns. Furthermore, businesses must build suitable safeguards for assessing the security vulnerabilities of automatically generated apps.
Metrics that are still in their infancy. Automation cost and value assessment tools are still in their infancy.
Manual augmentation is necessary. According to a Forrester survey, only roughly 40% to 60% of the code for automation might be written automatically using existing tools. When creating robust automation at scale, a significant amount of manual effort is still required, and this must be factored into the budget.
Getting people to buy-in. The majority of automation suppliers promote the idea that hyper automation will complement rather than replace humans, but the truth is that automation will eliminate tasks that were previously performed by humans. For these measures to succeed, workers must be convinced that robots will not take their employment. Furthermore, the many monitoring technologies employed in hyper-automation initiatives may cause a backlash from knowledge workers concerned about data exploitation.
Typically, a hyper-automation project begins with the specific purpose of improving a statistic or procedure. Here are two use case scenarios and how they would be handled.
A finance team’s goal in the first use case might be to process bills more quickly, with less human overhead and fewer errors. A project may begin by observing how human accountants receive bills, what data they gather, and what fields they paste into other apps using job mining tools. This could be used as a starting point for creating a basic bot.
This template could then be sent to the CoE team, who would be in charge of creating the final bot. Integration of an OCR engine to improve the ability to read bills and an NLP engine to interpret the payee or terms in the invoice are examples of this. Initially, the CoE team would be in charge of quality control, followed by an evaluation of how much it cost to construct the bot and how much it saved. This information could aid in the prioritization of further automation opportunities.
Another application could be to employ process mining software to find ways to shorten order fulfillment times. This would begin with an examination of ERP and CRM data logs to determine why certain orders are fulfilled in four hours while others take four days due to a variety of exceptions. Process analytics could reveal methods to improve the process and cut down on delays, such as altering credit check criteria for long-term customers. It may also uncover opportunities to automate some manual operations that create delays in other orders. The automation CoE team could compute the overall cost of making these enhancements and measure the total savings over time once this automation is applied.
There are currently no companies that provide comprehensive hyper-automation technology. Various automation providers, on the other hand, are broadening their toolkits to enable a larger range of hyper-automation capabilities.
The following vendors are diversifying their automation portfolios:
To expand its process mining capabilities, UiPath purchased Process Gold and StepShot.
Automation Anywhere has been working on its process and task mining skills to generate bots automatically.
Blue Prism has established a cooperation with Celonis after building its internal process mining capabilities.
Celonis, a major process mining vendor, has acquired Integromat to increase its automation capabilities.
With its Power Automate series of RPA tools and Process Advisor for process mining, Microsoft has been gradually developing its hyper-automation capabilities.
Kryon was one of the earliest manufacturers of intelligent automation systems that include process discovery directly in their products.
ABBYY has long been a leading OCR vendor, and it has steadily expanded its toolkit to enable a wide range of intelligent processes.
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