One of the technology developments that will have the biggest effects in the next ten years is hyperautomation. This idea refers to the automation of any business process utilizing a mix of robotic process automation and other cutting-edge technologies, like artificial intelligence and machine learning. It goes far beyond mechanizing repetitive manual operations that are performed by individuals.
We were taught in school that steam engines were utilized to automate the textile industry during the First Industrial Revolution at the end of the 18th century. The internal combustion engine and electricity were added by the Second century later, and process automation and digitalization were introduced by the Third near the close of the 20th century.
The Fourth Industrial Revolution—which introduces robotization, the Internet of Things, and artificial intelligence—hasn’t been with us for very long. a process driving the developed world’s nations back towards reindustrialization in an effort to regain their capacity to manufacture their own consumer goods sustainably. Hyperautomation, also known as intelligent process automation (IPA) or digital process automation (DPA), appears to be one of the technological tendencies in this new environment.
Defining Hyperautomation
Using Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) to increase the automation of business processes (production chains, work flows, marketing processes, etc.) is known as hyperautomation. Almost any monotonous operation may now be automated, and it’s even simple to identify which procedures qualify for automation and build bots to carry them out.
According to Gartner , “Hyperautomation involves combining a variety of tools, such as robotic process automation (RPS) and artificial intelligence (AI), in order to improve business decisions”
Hyperautomation, which removes human intervention from low-value activities and produces data with previously unattainable levels of business intelligence, is another important component of the digital transformation. It may have a significant role in creating flexible organizations that can quickly adjust to change.
How Does Hyperautomation Work?
Gartner claims that the foundation for hyperautomation technology is RPA enhanced by AI and ML. Power and flexibility can be added in areas where they were previously impractical by combining these technologies. Thus, it is now possible to automate operations that were previously impractical, freeing up human resources for more valuable duties like making decisions, analyzing data, and using critical thinking.
Numerous hyperautomation platforms are available, and they can be added to the technologies that businesses now use. As previously said, Robotic process automation technology is the primary platform; however, other options include information engines, intelligent business process management suites (iBPMS), and integration platforms as a service (iPaaS).
A digital twin, or digital twin organization, is a virtual depiction of a product or work flow that mimics how processes interact and illustrates where value is created in real time. Hyperautomation can aid in the creation of these digital twins.
Benefits of Hyperautomation
Hyperautomation offers many benefits for an organization’s performance as well as the welfare of its employees. Among them are:
- The incorporation of disruptive technologies. These include artificial intelligence, machine learning, robotic process automation, and natural language processing solutions. Into the regular operations of the business to improve productivity and lower errors.
- Heightened employee happiness as a result of working in an intelligent workplace and not having to spend time on pointless chores; also, it improves the workforce’s capacity to boost productivity and competitiveness.
- By matching their technology investment and business processes, organizations can undergo digital transformation.
- Decrease in an organization’s operating expenses. Gartner predicts a 30% cost reduction by 2024 when operating procedures are modified and hyperautomation technologies are combined.
- AI and big data technologies enable more efficient decision-making and the extraction of business insights from data.
Automation versus Hyperautomation
Conventional methods of automating enterprises concentrated on the most effective ways to integrate automation in certain situations. These AI and automations were extremely focused on one specific piece of software. For instance, scripts are used in workload automation to automate a large number of extremely repetitive tasks. can automate processes inside a certain workflow.
By employing natural language processing, natural language production, and optical character recognition (OCR) to read and comprehend documents, artificial intelligence development (AI) expands the scope of traditional automation to include more jobs. Using pre-built modules provided through an enterprise repository or app store, hyperautomation facilitates the integration of AI and machine learning capabilities into automations.
Automation creation requires less knowledge thanks to low-code programming technologies. Process mining could be used by hyperautomation to find and automatically create new automation prototypes, thus streamlining the automation development process even further. These days, human improvement is still required to increase the quality of these automatically created templates. However, this manual labor will be reduced as hyperautomation advances.
What drawbacks does hyperautomation present?
Since hyperautomation is a novel idea, businesses are still figuring out how to implement it effectively. Among the most significant obstacles are the following:
Selecting a CoE plan for the company – When it comes to handling large-scale initiatives, some firms may do better with a more centralized approach, while others will benefit more from a federated or distributed strategy.
Tools – Silver-bullet hyperautomation software does not exist. Leading automation providers are growing their hyperautomation capabilities, but businesses will find it difficult to guarantee these products’ interoperability and integration.
Governance and security – Thorough monitoring and analysis of business processes that cross departmental, service, and even national boundaries is beneficial for all hyperautomation programs. Numerous new security and privacy risks may arise as a result. Furthermore, businesses must create the proper safeguards to assess the security flaws in apps that are developed automatically.
Immature metrics – We are currently in the early stages of developing tools to evaluate the prospective value and cost of automations.
Augmentation by hand is necessary – Building solid automations at scale still requires a significant amount of physical labor, which must be budgeted for.
Acquiring human support – The majority of automation providers promote the myth that hyperautomation would complement humans rather than replace them, while in actuality, automation has an impact on some tasks that were previously performed by humans. For these initiatives to succeed, workers must be persuaded that robots won’t replace them in their current roles. Knowledge workers may also object to the different monitoring technologies used in hyperautomation projects if they believe that their data may be misused.
Conclusion
Because AI sets Digital Workers apart from traditional automation techniques, RPA and AI are necessary components of hyperautomation.
Hyperautomation also provides another special advantage by revealing and automating data and processes that were previously unavailable: the development of a digital twin of the company (DTO). In what way does that assist? The previously hidden relationships between processes, functions, and key performance indicators are made transparent by a DTO. Envision monitoring the generation of corporate value as it occurs—or does not occur—and using the intelligence to quickly react and spot new opportunities.
