The Influence of Hyper Automation on the IT Industry
Midhun Menon P
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Hyper automation is simply the extension of legacy business process automation beyond specific processes. For example, hyper-automation, which combines AI tools and RPA, automates nearly any repetitive action performed by business users.
It even automates the already automated processes by identifying dynamic business processes and developing bots to automate them. As a result, Gartner named hyper-automation one of the top ten strategic technology developments of the year.
Hyper automation is a mechanism for true digital transformation, with tools such as Robotic Process Automation (RPA), Machine Learning (ML), and Artificial Intelligence (AI) collaborating to automate complex business processes, including those that previously required subject matter experts.
What exactly is hyper automation and how does it work?
Hyper automation combines various aspects of process automation by combining tools and technology to increase the potential for task automation.
It begins with Robotic Process Automation (RPA) and then expands with artificial intelligence (AI), process mining, analytics, and other advanced tools to increase automation potential.
The goal is to automate more and more knowledge labor while also involving all employees in the transformation.
Hyper automation helps to automate your business to its maximum Potential
Machine Learning (ML), Natural Language Processing (NLP), Intelligent Optical Character Recognition (OCR), and AI computer vision are some of the artificial intelligence skills that enable robots to read, see and process more data.
If you are still unsure about the effectiveness or necessity of investing in RPA, read on to learn what experts have to say:
Engaging the workforce
This includes methods for allowing everyone in a company, including subject matter experts, business analysts, and business users, in addition to the usual RPA developers and testers, to participate in automation.
Businesses use process discovery tools to delve into the inner workings of teams and reveal what can be optimized and what should be automated.
Advanced analytics aid in calculating and quantifying the return on investment (ROI) and the impact of automation on key business outcomes.
Rather than referring to a specific, out-of-the-box technology or application, hyper-automation focuses on adding more intelligence and applying a larger systems-based approach to growing automation efforts. Furthermore, the method emphasizes the importance of striking a balance between automating manual tasks and streamlining complex processes to reduce steps.
It is critical to consider who should be in charge of automation and how it should be carried out. Frontline workers, for example, are more likely to notice tedious tasks that could be automated. Experts in business processes, on the other hand, are better positioned to identify the potential for automation in situations involving multiple people.
Concept of the Digital Twin
Gartner proposed the concept of an organization’s digital twin (DTO). This is a simulation of how business operations work and the mechanisms that support their services and deliveries. A combination of process mining and task mining is used to automatically construct and update the process representation. Process mining is a technique for representing process flows by analyzing enterprise software logs from business management software such as CRM and ERP systems. Using machine vision software that runs on each user’s desktop, task mining creates a view of processes that span many programs.
Process and task mining technologies may generate a DTO for the client automatically, allowing them to see how functions, processes, and key performance indicators interact to generate value. The DTO can help businesses determine how new automation adds value, creates new opportunities, or introduces new bottlenecks that must be addressed.
The Longer Path to Adoption of Digitization
Because of AI and machine learning components, automation can interact with the world in more ways. For example, optical character recognition (OCR) allows an automated procedure to extract text or numbers from paper or PDF documents. Similarly, natural language processing can extract and organize data from documents, such as determining who issued an invoice and what it was for and automatically entering this information into an accounting system.
On top of existing technology, a hyper-automation platform can be built on top of it. RPA is the first step toward hyper-automation, and all major RPA vendors now support process mining, digital worker analytics, and AI integration.
Other low-code automation platforms are incorporating more hyper-automation technology components, such as business process management suites (BPMS/intelligent BPMS), integration platform as a service (iPaaS), and low-code development tools.
What are the challenges that come with Hyper-Automation?
Hyper automation is a new concept, and businesses are still figuring out how to implement it. Some of the most significant challenges are as follows:
Choosing a company’s core strategy
Some organizations will benefit from a more centralized approach to large-scale handling initiatives, whereas others will benefit from a federated or distributed approach.
Tools
Hyper automation software is not a panacea. Even though prominent automation suppliers are expanding their hyper-automation capabilities, businesses will face challenges in ensuring compatibility and integration between these solutions.
Governance and safety
Hyper automation initiatives benefit from in-depth monitoring and analysis of business processes that span multiple departments, services, and even country borders. This could cause a slew of new security and privacy issues. Additionally, businesses must implement appropriate safeguards for assessing the security vulnerabilities of automatically generated apps.
Metrics that are still in their early stages
Automation cost and value estimation software are still in their infancy.
Manual augmentation is required
According to a Forrester survey, only about 40% to 60% of automation code can be written automatically using existing tools. A significant amount of manual effort is still required when creating robust automation at scale, and this must be budgeted for.
Getting people to buy in
The majority of automation vendors promote the notion that hyper-automation will supplement rather than replace humans, but the truth is that automation will eliminate tasks that were previously performed by humans. Workers must be convinced that robots will not take their jobs for these measures to be effective. Furthermore, many monitoring technologies used in hyper-automation initiatives may elicit a negative reaction from knowledge workers concerned about data exploitation.
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