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Data Science

RPA vs Hyperautomation | Top Differences

Dr. Jagreet Kaur Gill | 29 August 2024

Introduction

The key difference between RPA and hyper-automation lies in their areas of emphasis. While RPA focuses mainly on robotically automating simple processes, hyper-automation utilizes advanced technologies such as machine learning (ML), artificial intelligence (AL), and robotic process automation (RPA) to automate repetitive, rule-based tasks that were previously handled by humans.

What is Hyperautomation?

Hyper Automation allows automation to do virtual tasks performed by business people by merging different AI technologies with RPA. Hyper automation is an expansion of the processes of traditional business-process automation. It allows companies to combine BI systems, undertake complex needs, and increase human expertise and automation experience. Hyper Automation is increasing with the evolution of automation technologies. The advantages of hyper-automation are lower automation costs, improved IT-business alignment, and improved security and governance.

Hyper Automation encompasses a range of cognitive technologies, tools, and platforms, including AI, ML, business process management (BPM), robotic process automation (RPA), integration platform as a service (iPaaS), packaged software, low-code/no-code tools, and other forms of decision, process, and task automation tools.

Hyperautomation drives cost reduction while simultaneously enhancing efficiency, productivity, and quality, resulting in heightened levels of customer and employee satisfaction. Taken From Article, Hyperautomation and its Latest Trends

What is Robotic Process Automation?

There are a few trends in the IT space generating more interest in Robotic process automation(RPA). RPA technology Provides a huge amount of promises by allowing you to automate manual and repetitive tasks that save you time and money.

  • Robotic: Any entity which can mimic/copy human actions.

  • Process: Any sequence of steps that lead to a meaningful task.

  • Automation: When a task is performed automatically without human intervention.

Robotic Process Automation uses advanced software technology with AI and machine learning capabilities to handle high volumes of repetitive tasks. RPA is a trending automation that can help in Repetitive, Rule-based, Time-Consuming tasks that require more human effort.

It is easy to implement and build, deploy, and manage software robots to mimic human actions easily. Software robots can do things like humans do, such as understand what's on a screen, identify and extract data, navigate systems, and perform a wide range of defined actions and tasks—such as Extract the Data from pdf and save in an Excel file, Extracting the data from any website, Addressing queries, making calculations, maintaining some records, and performing transactions.

The main goal of implementing RPA technology is to reduce the time and effort humans require to perform time-consuming and repetitive tasks. This suggests that when a business uses robotic process automation, it can better use its resources and increase its return on investment.

What are the Benefits of Hyperautomation?

  • Improved Accuracy: Machine Learning(ML) is a powerful tool that enables systems to learn from data, recognize patterns, and make accurate predictions. We can significantly reduce errors and enhance the overall output quality by harnessing its capabilities. In particular, ML plays a pivotal role in refining the accuracy of automation processes, ensuring precise and reliable results.  

  • Speeding Up The Processes: AI and Machine Learning can accelerate processes by reducing time consumption. Automated processes leveraging advanced technology like AI and Machine Learning are often executed more swiftly compared to those reliant on human involvement. This efficiency stems from machines' ability to operate at a faster pace than humans and their lack of need for breaks.

  • Better Decision Making: AI and Machine Learning have the potential to enhance decision-making capabilities. Automated systems incorporating cognitive technologies like AI and ML often possess superior decision-making abilities compared to humans due to their access to vast amounts of data and their ability to process it swiftly. 

Additional Benefits of Hyper Automation introduction-icon
  • Fast (automated) identification of automatable processes.  
  • Efficient automation using artificial intelligence components.  

  • They are enabling the entire organization to automate.  

  • End-to-end automation of complex processes as ultimately as possible.

  • Management of the complete lifecycle of automation.  

Future of Hyperautomation

Hyper Automation is an upcoming technology transformation that will continue to impact companies in almost every industry. People may concentrate on those with a more excellent value for the business, free from repetitive, rule-based, and low-value/low-code jobs. The combination of automation and human involvement enables enterprises to improve client experiences, decrease operational expenses, and enhance profitability.

 

What are the Benefits of Robotic Process Automation?

  • Save time - Many business processes involve repetitive administrative tasks. RPA enables businesses to automate these tasks with rapid, robotic efficiency. This not only allows companies to save time but also empowers employees to focus on more sensitive and complex tasks.
  • Increase ROI - RPA tools excel in handling repetitive tasks more efficiently than humans, thereby enhancing work productivity for your business. This efficiency contributes to one of the key advantages of RPA: its positive impact on ROI. By leveraging robotic process automation, your business can enhance various processes and accumulate substantial qualitative and quantitative data over time. This data aids in more efficient cost management and decision-making.
  • Eliminate human error - In reality, regardless of an individual's skill level in their position, human error and fatigue are always potential considerations. Unlike humans, automated bots in RPA never experience fatigue, ensuring tasks are consistently performed accurately each time.
  • Elevate security - Cybersecurity holds utmost importance for your business, and RPA solutions play a crucial role in fortifying defenses against security threats. RPA enhances security measures by minimizing human interactions with sensitive data and information, thereby mitigating the risk of costly data leaks and breaches. RPA tools contribute to maintaining the security of your business by preventing unauthorized access and executing triggered account logouts as needed. 

Hyper Automation vs. Robotic Process Automation (RPA)

Hyper Automation

Robotic Process Automation

1. Performed by multiple machine learning, packaged software(No code/low code platforms, analytics) & automation tools.

  • 2. AI-based process automation with cognitive ability can loop humans into the processes.

  • 3. Innovative & efficient operations.

  • 4. All tasks that are automatable will eventually undergo automation

 1. Performed by dedicated RPA automation tools.

    • 2. Focuses on automating repetitive tasks with rule-based logic.

    • 3. Often implemented for specific tasks or processes rather than end-to-end automation.

    • 4. Emphasizes automating tasks that are readily automatable, contributing to ongoing operational improvement.

Use Case of Hyperautomation in Different Industries

  1. Banking - Banks can leverage hyper-automation across multiple areas, including regulatory compliance, marketing, sales, distribution, customer service, payments, loans, and office operations. Intelligent character recognition enables banks to enhance their "Know Your Customer" processes and compliance by converting manually written customer information into electronic formats for quicker analysis and response.
  2. Healthcare - Hyperautomation is used in the healthcare industry to create intelligent billing processes by collecting and consolidating billing details from various departments without human intervention. An instance includes AI and RPA identifying medical policy coverage and conditions, with intelligent chatbots facilitating and automating bill submissions. Voice recognition swiftly transcribes speech into text, managing thousands of cases concurrently. This fusion of intelligent processes enhances both back-office and customer-facing operations, elevating the overall customer experience and operational efficiencies.
  3. Call Centers and Customer Service - RPA and AI are transforming call centers by automating repetitive tasks, like mouse clicks and application launches, improving efficiency. Agents can access comprehensive customer profiles from multiple systems simultaneously, eliminating the need to switch screens during calls. The technology extends beyond call centers to enhance project automation and package tracking, optimizing operations and efficiency.  

Case Study for Hyperautomation

  1. Based on a report published by Deloitte in 2019, AI, ML, and intelligent automation are among the top 10 Industry 4.0 technologies that profoundly impact significant organizations globally.

  2. We recently had the chance to assist a prominent bank in Indonesia in addressing an issue related to the cheque clearance process. The challenge was to expedite the verification of signature authenticity on cheques, a task prone to time-consuming manual errors often overlooked by clerks. Inaccuracies in signature verification could lead to transaction delays, causing operational disruptions and impacting the overall customer experience. Leveraging Hyperautomation, a solution was crafted to overcome this challenge.

  3. Hyper-automation presents significant advantages. As per Coherent Market Insights, the global hyper-automation market is projected to grow at a compound annual growth rate (CAGR) of 18.9% from 2020 to 2027, with the extensive digitalization of obsolete manufacturing facilities serving as a primary driver for this growth.

Adoption of Hyperautomation in Industries

  • Research indicates that Hyperautomation reached a value of around USD 9 billion in 2021 and is expected to reach approximately USD 26.5 billion by 2028.

  • The Hyperautomation market is expected to grow annually at a CAGR of around 23.5 % by 2022-2028.

  • The industry is expanding as a result of growing demand for automation in various sectors and ongoing advancements in technology, which are driving market growth. North America is expected to contribute 41.6% to this growth.

An Enterprise AI Chatbot Platform provides a comprehensive solution for businesses to create, deploy, and manage chatbots. Taken From Article, Enterprise AI Chatbot Platform

Use Case of RPA in Different Industries

  1. RPA in Finance and Accounting: RPA is extensively utilized in finance and accounting processes. It automates tasks like financial statement audits and accelerates accounts receivable and payable functions. With its ability to handle large data volumes efficiently, RPA offers significant cost savings. Implementing intelligent automation in finance and accounting enhances transparency and accuracy, improving overall business operations.

  2. Invoice Processing: Invoice processing could be more varied and varied, especially if the invoices are received or generated in various formats. RPA can handle the time-consuming task easily while ensuring the correctness and forwarding the invoices to the approving authority in less time. Overall accounts payables and receivables can be automated with an RPA in the finance industry. The maker and checker process can be almost eliminated as the machine can automatically perform the tasks that match the invoices with the relevant POs.

  3. Automatically Bill Generation: Helps the finance team generate automatic bills and manage them with RPA. It helps enter accurate and timely billing details in the system, which helps the team save time significantly without any errors. It is easy to deploy and causes minimal disruption to the system.

  4. Inventory Management: The essence of business processes revolves around control, particularly the necessity to monitor inventory levels for ensuring a consistent product supply. RPA bots can handle the labor-intensive tasks, thereby assisting in managing dead stock and stock-outs, enhancing lead times, and optimizing storage expenses.

Adoption of Robotic Process Automation in Industries

  • Research shows 81% of companies invest in RPA technology to achieve their financial goals.

  • As stated by Computer Economics 2020, the adoption rates of RPA are rising from 12% in 2019 to 20% in 2021.

  • The adoption of RPA in finance is expected to increase approximately 12.7 times within the next 2-3 years. Similarly, RPA utilization in Human Resources is projected to grow over 3.7 times, while in procurement, it is expected to expand by 4.3 times compared to its current size. Additionally, GBS is anticipated to grow by 5.2 times within the same timeframe.

  • The collaborative robotics market is anticipated to achieve a value of $12 billion by the year 2025, as projected by MarketsAndMarkets.

Conclusion

In conclusion, adopting hyper-automation in businesses brings numerous benefits and enables improved operations. The data collected through automation helps identify further automation opportunities, allowing organizations to prioritize their implementation. Hyper automation combines various tools such as AI, ML, and robotics, empowers workforces, aligns business and IT, and provides valuable insights into return on investment. It's crucial to emphasize that the objective of RPA isn't job replacement but rather productivity enhancement, enabling employees to concentrate on tasks of higher value. Robots are viewed as partners that elevate our responsibilities rather than supplanting them.

 

 

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dr-jagreet-gill

Dr. Jagreet Kaur Gill

Chief Research Officer and Head of AI and Quantum

Dr. Jagreet Kaur Gill specializing in Generative AI for synthetic data, Conversational AI, and Intelligent Document Processing. With a focus on responsible AI frameworks, compliance, and data governance, she drives innovation and transparency in AI implementation

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