What exactly is Hyperautomation?

Hyperautomation is system & collection of modern technologies that are used to increase automation within business. Hyperautomations ultimate objective is development of system to automate automation of enterprises.

Hyperautomation is new field of technology. comprise following:

• Task mining & process mining tools that help in identifying potential automation opportunities & prioritizing.
• Tools for automation development that can reduce cost & effort of building automation. They comprise robot process automation [ RPA  ] low code or no code development tools, an integration platform as service [  iPaaS  ] for integrations & automation tools for workload.
• Business logic tools to make it simpler to change & reuse automations. This includes smart Business Process Management [ BPM  ] & business rules & decision management.
• Artificial Intelligence [ AI  ] & machine learning algorithms, as well as tools that can be used to extend capabilities of automated systems. variety of software available for this domain includes NLP, natural process of language [  NLP  ] & optical character recognition computer & machine vision chatbots, virtual agents & chatbots.

The term  hyperautomation  was introduced in year 2019 by IT related research company Gartner. This concept is result of an understanding which RPA technology, which is innovative & widely used method for automating computer driven processes is difficult to scale up in business level as well as limited in terms of type of automation it is able to accomplish. Hyperautomation is system for use of different automated technologies either individually or in conjunction & augmented with AI or machine learning.

Hyperautomation is method of study towards automation. practice of hyperautomation includes following elements:

• Finding out what tasks can be automated.
• Selecting right automation tool.
• The goal is to increase agility through reuse of automated procedures.
• Enhancing their capabilities through various kinds of AI & machine learning.

Hyperautomation projects are usually managed by center of excellence [  CoE  ] which helps to drive automation process.

Hyperautomations advantages are cost reductions, in addition to boosting efficiency & productivity. Additionally, it helps companies make use of data that is generated by digital processes. Businesses can utilize that data to enhance their decisions.

What is significance of hyperautomation?

Hyperautomation gives organizations an infrastructure for expanding improving, integrating & enhancing enterprise automation. framework builds upon effectiveness of RPA tools & tackles their weaknesses.

RPA can be attributed to its explosive growth in comparison to other technologies for automation because of its easy usage & its intuitiveness. In particular, since RPA replicates how users interact with software, users are able to automatize portion or even all their tasks through recording procedures they want RPA systems to adhere to. Businesses can utilize same measures they utilize to gauge human performance of employees [  speed & accuracy, for instance  ] in particular in order to gauge RPA effectiveness.

Initial RPA initiatives didnt grow quickly. In beginning, around 13% of companies could manage to expand their initially RPA initiatives, as per 2019 Gartner review. Deloittes Global Outsourcing Survey found that 60% of companies utilized RPA at some point however just 34% utilized it across their entire enterprise. Hyperautomation requires enterprises to consider kinds & maturity of technologies & processes needed to expand automatization initiatives.

According to Gartners definition of hyperautomation. emphasis is on how businesses can create system to automate processes. It is distinct from other frameworks for automation that are focused on improving automation techniques & concepts like Digital Process Automation [  DPA  ] as well as Intelligent Process Automation [  IPA  ] & cognition automation. These frameworks are focused on automation in itself.

Hyperautomation goes back & thinks about ways to improve process of finding potential automation possibilities. Then, it automatically creates relevant automation objects such as bots, scripts or workflows which could utilize DPA, IPA or cognitive components for automation.

Another idea that is complement to hyperautomation is concept that Forrester Research calls digital worker analytics. approach is also focused on process & performance for example, tracking costs in developing, deploying & controlling automations, so that you can compare costs to value provided. It is essential to analyze this data when making decisions about next steps for automation. majority of RPA as well as enterprise automation providers are beginning to incorporate digital worker analytics to their products.

How can hyperautomation be used?

Instead of using specific, standard software or technique hyperautomation focuses on incorporating greater intelligence & utilizing an overall system based approach for expansion of automation. It is method that emphasizes necessity finding ideal compromise between replacing manual work by automation & enhancing complex procedures to reduce steps.

One of most crucial issues is finding out who is accountable for automation & best way it will be accomplished. People working in frontline have advantage to recognize time consuming & repetitive jobs that could be made automated. Business process specialists have advantage to spot opportunities for automation which are managed by variety of employees.

Gartner is first to introduce concept of creating digital twin business [  DTO  ]. It is digital depiction of way business processes function. model of business process is created automatically & then is updated by combination of task & process mining. Process mining analyses enterprise logs of software from applications for business management such as CRM [  customer relationship management  ] [  CRM  ] as well as ERP [  ERP  ] systems in order to create diagram of processes. Task mining employs machine vision software in each desktop computer to build picture of process flows that are spanning multiple different applications.

Task mining & process mining tools are able to produce DTOs. DTO that allows organizations to see how their operations, processes & important performance indicators work together to create value. DTO will help companies determine what new automated processes can do to increase value & create new opportunities, or cause new bottlenecks that have to be dealt with.

Machine learning & AI tools let automations interact with outside world in different ways. As an example optical character recognition [  OCR  ] can be used to automate process of analyze numbers or text from papers or PDF files. Natural language processing is able to extract & organize data from documents. For instance, it can identify company that an invoice comes from & purpose for which it was issued & also automatically integrating information in an accounting software.

Hyperautomation platforms can be built directly over technology that companies have already. RPA is way to incorporate hyperautomation. Every major RPA companies are now offering ability to process mining Digital worker analytics, as well as AI integration.

Different types of low code automation tools, like business process management system [  BPMS  ] Intelligent BPMS as well as iPaaS, low code development tools include capabilities for hyperautomation technologies components.

A variety of automation & AI technology are required for scaling RPA.

Hyperautomation vs. automation

Traditional methods of enterprise automation were focused on how to use automation in an enumeration. They were more dependent on specific component of software. Like, for instance, work automation employs scripts that automate repetitive process of many. Automated tasks can be automated within context of particular workflow.

AI augments automation in traditional sense to perform more functions like making use of OCR to scan documents as well as natural language processing in order to process them, or natural language generation for providing human beings with summaries. Hyperautomation allows you to integrate AI & machine learning features in automation using pre built components that are available through app store or an enterprise repository.

Development tools that use low code reduce knowledge required for creating automatizations. Hyperautomation may speed up process of automation further by through process mining, which can detect & generate automatic latest automation models. Today, these automated templates require further improved by humans in order to enhance their quality. advancements in automation will decrease this manual labor.

What are advantages of hyperautomation?

The most significant benefits of hyperautomation comprise following benefits:

• Automation with lower costs.
• A better alignment has been achieved Between IT & business units.
• The reduction in shadow IT This increases security & enhances management.
• More widespread adoption of AI & machine learning in business process.
• Increased capability to assess effects of digital transformation strategies.
• Help prioritizing future automation efforts.

In event that companies master hyperautomation in their businesses it is possible to find many different ways that this technique can help improve processes & results.

For social media, retention of customers & experience for customers companies could make use of RPA & machine learning in order to create reports & extract data from social networks for determining customer opinions. company could also create method to make that data accessible to marketing personnel & then develop personalized, in the moment customer communications.

If company introduces new product rapidly & DPA indicators show high desire for it, then product may be swiftly increased to allow company to boost its profits. In contrast, if more sophisticated analysis shows that item does not gain popularity among clients. business could reduce losses by removing product fast.

What are issues associated with hyperautomation?

Hyperautomation is new & companies are at initial stages of working out best way to put it into real world. biggest issues are:

• Selecting right CoE approach for your organization. Some companies may do better using centralized method, while other organizations will be more successful with use of distributed or federated approach for managing big scale projects.
• Theres no one stop hyperautomation application. Even though top automation providers are upgrading their capabilities for hyperautomation however, companies will have to guarantee interoperability & integrate between these systems.
• Security & Governance. Hyperautomation programs can all benefit from thorough monitoring & analysis of processes that cross departments, service providers, as well as country borders. These can create myriad of privacy & security problems. Furthermore, businesses have to create right protections that can be used to identify security weaknesses of automatically produced applications.
• Infancy metrics. Tools for evaluating value & cost of automated systems are just beginning to develop.
• Manual augmentation required. Much manual work remains required & has to be planned when creating solid automations on large scale.
• Getting human buy in. majority of automation companies are pushing their claims that technology is way to augment instead of replacing humans. reality is that technology can affect certain tasks that were previously performed by humans. People must be assured that machines will not do jobs of humans for their efforts to gain traction. Additionally. many monitors used in hyperautomation projects could cause some backlash from those who are who are concerned about possible use of information.

Use cases & examples of hyperautomation

The hyperautomation project typically begins with goal in mind to increase efficiency of process or measure. Two examples are provided of applications & how they could be handled.

Financial services

In case of initial use financial service team could be looking at making invoices more efficient, using least amount of human intervention as well as fewer errors. project might begin with task mining software to monitor way accountants in human form get invoices. data they record & fields they incorporate into other applications. It could be used as basis for creation of basic bot.

This design could be given onto automated CoE team, who will be charged with creating final bot. This might include integration of an OCR engine to enhance capacity to recognize invoices, as well as an NLP engine that interprets name of payer or words on invoice. CoE team also would oversee initial quality control & then conduct an evaluation of amount it took to develop bot as well as amount it has saved. data will help in determining best way to approach different automation possibilities.

Order fulfillment

A different scenario could involve use of process mining software to find ways to cut down on delivery times for orders. It would begin by looking at ERP & CRM logs of data to determine reasons why certain orders can be fulfilled in just four hours, whereas other orders take longer due to various reasons.

Process analytics could identify methods to modify processes which could reduce time it takes to process orders for example, altering credit check requirements to customers with long standing relationship. They could also find ways to streamline routine processes that lead to delays on different orders. After these processes have been implemented then CoE team will be able to determine cost for improvements & then track savings total over time.

Hyperautomation vendors

None of vendors provide all purpose high end technology for hyperautomation. Yet, many automation providers are expanding their range of tools to accommodate greater variety of hyperautomation options as well as strategic technological developments.

A few of companies that are expanding their range of automation capabilities are:

• ABBYY, renowned OCR supplier, has widened its tools portfolio to provide range of smart business process automation features. It has range of methods of mining through various platforms like ABBYY Timeline.
• Automation Anywhere developed process mining & task mining features to automatically generate bots.
• In year 2020 Process mining company Celonis purchased Czech company Integromat for purpose of expanding its capabilities for automation.
• IBM purchased process mining business MyInvenio in 2021, allowing it to incorporate processes mining in its tools for automation.
• Microsoft has expanded capabilities of hyperautomation with its Power Automate line of RPA tools, as well as its Process Advisor tool for process mining.
• Nintex bought Kryon in 2022. It was one of very first companies that integrated process discovery into tools it offers.
• Financial software company SS&C purchased Blue Prism in 2022, to enhance its processes mining capabilities, which were created as part of joint venture with Celonis.
• UiPath first bought Process Gold & StepShot in year 2019 to expand capabilities of its process mining. company later acquired API Integration Platform Cloud Elements in 2021 & Re:infer one year later to enhance its NLP & text mining capabilities. It renamed platform UiPath Communications Mining. aim was to expand companys RPA products through AI as well as API capabilities.

Together with other technologies, such as cloud computing, mobile platforms as well as machine learning & hyperautomation is just one of many components in an overall digital transformation process. Discover how CIOs as well as others IT executives have been driving this digital transformation in their companies.