Introduction to Hyperautomation
“Hyperautomation is an effective mix of sets of complementary solutions that can combine functional and process silos to automate and enhance business processes. Gartner defines this term as “the application of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), business process management (BPM), and data mining.” The fact that hyperautomation is included as one of the trends in the Top 10 Strategic Technology Trends for 2022 demonstrates how this notion will revolutionize the insurance industry. Gartner forecasts that by 2024, these essential technologies will reduce operating expenses by at least 30%.
JPMorgan Chase CEO Jamie Dimon says – banks face an “enormous competitive challenge” from Amazon, Apple, and Google. Given that these corporations have access to vast quantities of client data and the means to utilize it, their entry into the financial services market poses a danger to conventional financial institutions.
In addition to the technologies mentioned above, hyperautomation includes voice, deep learning, data mining, and advanced predictive analysis. Integration of these technologies into the current business model will enable end-to-end process automation, resulting in a higher level of service and impact. This is the case across all sectors and markets.
Related article: Hyperautomation in Healthcare: Use Cases, Benefits, and Solutions
How does Hyperautomation work?
A hyperautomation practice entails defining the tasks to be automated, selecting the appropriate automation technologies, fostering agility through the reuse of automated processes, and augmenting their capabilities with various flavors of AI and machine learning. Frequently, hyperautomation programs are coordinated by a center of excellence (CoE) that drives automation initiatives.
The objective of hyperautomation is not only to save money, increase productivity, and achieve efficiency by automating but also to profit from the data collected and generated by digital operations. Organizations can utilize this information to make more informed and timely business decisions.
Instead of referring to a single off-the-shelf technology or solution, hyperautomation emphasizes adding intelligence and applying a systems-based approach to scaling automation activities. The course emphasizes the significance of establishing a balance between automating manual tasks and streamlining complicated processes to reduce steps.
The three main components of hyperautomation are automation, orchestration, and optimization.
The core of any hyperautomation approach is automation. Typically, it is comprised of smaller automation programs and tools that assist with particular activities. For instance, RPA is an automation system, and Hyperautomation combines multiple automation techniques.
Orchestration integrates automation tools into a more comprehensive framework to incorporate all tasks and operate in unison.
Finally, the additional intelligence layer enables optimization through validations and continual learning and facilitates the automation and orchestration processes.
Hyperautomation provides a framework for the strategic deployment of numerous automation technologies individually or in conjunction. These technologies may consist of the following:
- Robotic Process Automation (RPA): the automation of repetitive processes based on a set of predetermined criteria.
- Artificial Intelligence (AI): the capacity of robots to make decisions resembling those of humans by replicating their logical thought processes.
- Machine Learning (ML): algorithms that train machines to learn without human interaction. The computer modifies and adds to the rules as it learns from existing data.
- Big Data: technology that enables the storage, analysis, and management of massive volumes of data to detect trends and develop practical solutions.
- Cobots: collaborative robots that collaborate with humans for human-centered tasks.
- Chatbots: the use of OCR, AI, ML, and NLP to enable a machine to hold a text- or speech-based discussion in real time with a human.
- Information engines, integration platforms as a service (iPaaS), and intelligent business process management suites.
The most common hyperautomation platform includes the following steps:
- Connecting processes, workflows, and environments and establishing a platform from which independent automated processes can operate independently.
- Identifying structured and unstructured data and other inputs from diverse sources and storing them in a self-consistent database for usage by the various automation processes.
- Predicting outcomes such as efficiency and return on investment (ROI) using compiled data from which there is continual operational learning.
Related article: A Complete Guide to No-code Development for 2023
Importance of Hyperautomation
Consumers today favor digital channels such as mobile and internet banking and anticipate a more personalized banking experience. The COVID-19 pandemic has hastened the transition to digital preference. Additionally, nearly half of customers desire customized offers and updates in real-time from their bank. Hyperautomation facilitates the incorporation of AI and machine learning capabilities into automation through pre-built modules obtained from an app store or business repository.
Low-code development tools lower the amount of specialized knowledge necessary to implement automation. Using process mining to detect and automatically generate new automation prototypes, hyperautomation could further ease the development of automation. Currently, these automatically created templates require human improvements to improve quality. Nevertheless, advances in hyperautomation will diminish this physical labor.
Benefits of Hyperautomation
Hyperautomation use cases
A potential alternative use case is process mining software to uncover solutions to shorten order fulfillment times. This would begin by reviewing ERP and CRM data logs to determine why, for example, some orders are completed in four hours while others take four days due to various exceptions. Process analytics could suggest strategies to modify the process to eliminate these delays, such as changing the credit check criteria for established clients. In addition, it may find methods to automate some manual operations that result in delays for other orders. Once this automation is deployed, the automation CoE team will be able to compute the overall cost of deploying these enhancements and monitor the total savings over time.
Back office: On average, retail banks have between 300 and 800 procedures, which can be enhanced using business process management (BPM) solutions that eliminate human error and inefficiencies that negatively affect the client experience. The point, though, is not to apply a bandage to something no longer functional.
Lending: As late as 2021, lending procedures were lengthy and manual-driven. Numerous impediments, including credit checks and employment verifications, affect turnaround times. Automation technology could effortlessly retrieve or approve all pertinent loan data in a few seconds, authenticating consumers from different sources.
Similarly, mortgage approval processes can take up to 50 days. Combining automation with future technologies such as blockchain might automatically validate customer data from many sources or reduce customer churn owing to tiny errors on forms that created delays.
Related article: A Complete Overview of Intelligent Automation
How to choose the right Hyperautomation platform for your business?
Suppose you’re looking for a new hyperautomation platform or tool. In that case, it’s a good idea to look for one that is user-friendly, scalable, and compatible with multiple platforms and operating systems. Purchasing a system incompatible with your organization’s existing systems can incur significant expenses.
The success of hyperautomation is contingent upon locating a technology that communicates well with your employees. Today, most teams are comprised of individuals with diverse talents and experiences; therefore, it is essential to locate a collaborative tool that can be accessed and utilized by all team members. Sadly, most automation solutions demand their customers be able to write and read code. Choosing a technology that overcomes this obstacle can give firms a significant automation head start.
Gartner also refers to this tool selection as “architecting for hyperautomation.” This means that “organizations must be able to change their operations and to support systems in response to changing market demands and competitive threats.” The only way to achieve a hyperautomation future state is through hyper agile working styles and tools.”
Potential trends and developments that could shape hyperautomation in 2023
As more businesses recognize the benefits of hyperautomation, there will likely be increased adoption of the technology. This could be particularly true for industries that have been slow to adopt automation in the past.
Focus on employee empowerment
While hyperautomation can automate many tasks, it is important to recognize that it can create new job roles and employee opportunities. As a result, there may be a greater focus on empowering employees and providing them with the training they need to succeed in a hyperautomated environment.
Expansion of AI and ML capabilities
AI and ML are critical components of hyperautomation, and in 2023, we can expect to see continued expansion of these technologies. This could include the development of more sophisticated algorithms and the integrating of natural language processing (NLP) and computer vision capabilities.
Increased emphasis on security
As businesses become more reliant on hyperautomation, there will likely be a greater emphasis on ensuring the security of data and systems. This could include adopting new security protocols and developing new technologies to protect against cyber attacks.
Integration with blockchain
Blockchain is a technology that enables secure and transparent data sharing, and it is expected that it will become increasingly integrated with hyperautomation in 2023. This could enable businesses to automate processes that involve multiple parties and transactions, such as supply chain management.
Overall, hyperautomation is rapidly evolving, and we expect continued growth and innovation in the years ahead.
Hyperautomation with Autonom8
While hyperautomation and Robotic Process Automation are relatively new technologies, their popularity has been on the rise in recent years, particularly among businesses seeking to save expenses and maximize their resources. This technology is utilized throughout industries and has been shown to improve enterprises and promote growth. Neutrinos Multi-experience Development Platform (MXDP) enables RPA tools to operate effectively by allowing cross-functional teams to connect and collaborate on many apps to deliver end-to-end solutions.
Autonom8 is a low-code platform that facilitates hyperautomation for businesses. One of our platforms, A8Flow, can help you create seamless and integrated workflows within your existing systems to allow customers to complete their journey quickly. We offer automation across industries and have worked with a few leading companies to help them achieve operational excellence and grow their business. You will understand low-code automation, AI, and hyperautomation by reaching out and talking to our experts.
FAQs on Hyperautomation
According to Gartner, RPA augmented with AI and ML becomes the central enabler of hyperautomation. Combining RPA and AI technology provides the capability and flexibility to automate previously impossible processes: those that rely on undocumented, unstructured data inputs. For example, a finance team's objective may be to process bills faster, with less human intervention and fewer errors. A project could begin by monitoring how human accountants receive bills, what data they capture, and what fields they copy and paste into other applications using task mining tools. This could serve as a guide for creating a simple bot. To achieve high levels of productivity, increase accuracy, and reduce human error, hyperautomation can help transform your business. The overall benefits of hyperautomation can be summarized as, Hyperautomation involves the streamlined use of multiple technologies, tools, or platforms, including:
What is Hyperautomation?
What is an example of Hyperautomation?
Why do you need to automate the customer journey?
1]Reduces the cost of automated job functions by 30–40%, with an increase in efficiency
Optimize resources and their time
2] Helps innovate and take newer products/services to the market
3] Boosts overall productivity, thereby achieving high profit
4] 100% regulatory compliance
5] Data security & management
6] Real-time updates
7] Become future-ready
How to automate processes & workflows?
1. Low-code/no-code tools
2. Artificial intelligence (AI)
3. Machine learning (ML)
4. Robotic process automation (RPA), includes Chatbots
5. Business process management (BPM) and intelligent business process management suites (iBPMS)
6. Event-driven software architecture
7. Integration platform as a service (iPaaS) and
8. Other kinds of process and task automation tools
According to Gartner, RPA augmented with AI and ML becomes the central enabler of hyperautomation. Combining RPA and AI technology provides the capability and flexibility to automate previously impossible processes: those that rely on undocumented, unstructured data inputs.
For example, a finance team's objective may be to process bills faster, with less human intervention and fewer errors. A project could begin by monitoring how human accountants receive bills, what data they capture, and what fields they copy and paste into other applications using task mining tools. This could serve as a guide for creating a simple bot.
To achieve high levels of productivity, increase accuracy, and reduce human error, hyperautomation can help transform your business. The overall benefits of hyperautomation can be summarized as,
Hyperautomation involves the streamlined use of multiple technologies, tools, or platforms, including: