In recent years, companies have increasingly regarded process efficiency as a critical success factor. In this context, hyperautomation emerges as the master key to unlock a more efficient and innovative environment.
According to research published by Gartner, it is estimated that by 2024, 65% of large organizations worldwide will have implemented robust hyperautomation strategies. Therefore, this stands out as one of the key technological trends in the upcoming years.
In this article, we will delve into an analysis of what hyperautomation entails, exploring the origin of the term, and its primary benefits and technologies.
What is hyperautomation?
Hyperautomation is an approach that integrates various advanced technologies, such as Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA), to automate and enhance complex business processes.
In essence, hyperautomation extends beyond the automation of simple tasks. This approach aims to automate a spectrum of intricate processes. It represents the new frontier of process automation capable of handling operations in both the front-end and back-end, including numerous back-office processes.
This facilitates the automation of complex processes, seamlessly integrating different departments and systems. Moreover, this approach serves as a significant ally for companies aiming to stay competitive in the era of digital transformation.
💡 Learn more: Python RPA: understanding the automation culture
The origin of hyperautomation
The term hyperautomation gained prominence through Gartner, a technology research and consulting company. It was first introduced in the company’s 2019 trends report, emphasizing the critical role of integrating various automation technologies to develop more intelligent systems.
Gartner defines hyperautomation as “a disciplined approach that automates complex end-to-end business processes through the implementation of advanced technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and Machine Learning (ML).”
Automation vs. hyperautomation
Automation is the use of technology to perform tasks, usually repetitive and rule-based, without human intervention. It’s often applied to singular tasks and is excellent for streamlining routine, predictable processes.
Hyperautomation, on the other hand, takes this concept further by combining multiple advanced technologies like RPA, AI, and ML. It aims at automating more complex business processes and workflows, not just tasks. Hyperautomation is about creating a more interconnected and intelligent automation ecosystem.
RPA vs. Hyperautomation
RPA (Robotic Process Automation) focuses on automating specific, routine tasks using software bots to mimic human actions. It’s ideal for straightforward, repetitive jobs like data entry. Hyperautomation goes further by integrating RPA with advanced technologies like AI and ML.
When combined, both approaches help automate complex, end-to-end business processes, enabling broader organizational efficiency and more intelligent decision-making.
What are the benefits of hyperautomation?
Hyperautomation can be an excellent option for companies that have high maturity in process automation. For instance, companies that already have robust automation initiatives in place and have a team of RPA developers.
We’ve outlined some advantages of this technology. Check it out:
The primary benefit of hyperautomation is undoubtedly cost reduction. This technology allows different departments to optimize repetitive and manual tasks, while also reducing errors.
Consequently, the whole business can achieve more results in less time, becoming more productive. Moreover, the cost of implementing this strategy can yield considerable returns in the medium or long term.
According to the study conducted by Gartner, hyperautomation is expected to lead to a 30% reduction in operational costs for companies adopting it in their day-to-day operations by 2024.
Smart task execution and resource allocation
Companies that hyperautomate their processes efficiently expedite multiple chains of repetitive and manual tasks through automated systems. The combination of Artificial Intelligence and Machine Learning with other technologies allows for process optimization and acceleration.
With hyperautomation, a significant portion of the workforce engaged in repetitive tasks can be reallocated to perform higher-value and more complex tasks for the company, ensuring significant gains in productivity.
Hyperautomation enables the improvement of your company’s decision-making process, making it more accurate. By employing integration and data automation approaches, such as Big Data, your team can have access to advanced data analysis and processing.
Additionally, hyperautomation can assist your company in forecasting and planning accurately, for instance, processes and costs related to back-office, finance operations, management of external systems, and suppliers.
Ease of system integration
Finally, one of the main benefits of hyperautomation is the ease of integrating automations across different systems, infraestructures and applications.
This process facilitates and simplifies the exchange of information and collaboration between different parts of a company.
It also contributes to better communication between data and the orchestration of automations across different systems and departments. Integration can occur between legacy systems, virtual machines, different infrastructures, web and desktop applications.
Some practical hyperautomation examples and applications for companies include:
- Automated customer support;
- Intelligent document processing;
- Predictive maintenance in manufacturing;
- Financial reporting automation;
- Supply chain optimization;
- HR onboarding and employee management;
What are the technologies of hyperautomation?
Hyperautomation is composed of various technologies that assist in the automation process. Below are the key technologies of hyperautomation:
Robotic Process Automation (RPA)
RPA (Robotic Process Automation) is a technology that utilizes software to automate repetitive and bureaucratic tasks that would typically be performed by humans. This technology employs robots to perform specific actions in applications and systems, mimicking human interaction. In the context of hyperautomation, RPA is increasingly being allied with Machine Learning and Artificial Intelligence models.
Intelligent Character Recognition (ICR)
Intelligent Character Recognition (ICR) is a technology capable of interpreting handwritten or printed characters. This enables the conversion of a physical document into digital format in a matter of minutes. ICR was developed to streamline inteligent document processing (IDP) in organizations dealing with numerous printed documents, such as handwritten forms, receipts, and checks.
Artificial Intelligence (AI)
Artificial Intelligence (AI) is a field of computer science that develops systems that bestow machines with the ability to simulate human intelligence in activities requiring reasoning, learning, and decision-making.
This discipline encompasses diverse fields, such as Machine Learning (ML), Natural Language Processing (NLP), Robotics, and Computer Vision.
Machine Learning is the technology that enables computers to develop learning capabilities. This is achieved through algorithms that allow machines to learn to perform complex tasks independently, without the need for human intervention. In addition to learning processes, machines can enhance themselves and correct errors through this technology.
For hyperautomation, Big Data plays an extremely important role as it aids in real-time data collection, processing analysis. It also assists in decision-making and anomaly detection. This technology provides a comprehensive database to feed Artificial Intelligence systems, making them more reliable and effective.
Finally, chatbots are programs developed to simulate interactions with a human. In the context of hyperautomation, intelligent chatbots can be created not only based on humanly predefined rules. Instead, they can be based on machine learning algorithms and generative artificial intelligence.
In this approach, they are capable of learning on their own, with little human assistance. This technology is already very common in customer service and internal processes of companies. It could be even more useful for automating complex interactions.
How to Implement Hyperautomation?
After understanding what hyperautomation is, its technologies, and benefits, it’s crucial to know how to implement it in your company. Successfully adopting hyperautomation involves several important steps. Here’s a guide:
- Define Objectives: Understand what your company aims to achieve with hyperautomation.
- Conduct Process Mapping: Identify areas where hyperautomation can be beneficial through thorough process mapping.
- Identify hyperautomation tools: Your objectives can be achieved with RPA, artificial intelligence, machine learning, and more.
- Choose Automation Solutions: Select automation systems that meet the specific needs of your business.
- Invest in Training: Empower your team to utilize the new technologies effectively.
- Conduct Assessments: Use performance metrics to understand and adjust the impact of hyperautomation on your objectives.
10 hyperautomation software and tools
If you are a tech leader looking to boost your hyperautomation tech stack, remember to consider your needs and infrastructure.
Some of the leading hyperautomation tools that are widely recognized in the automation industry, covering a range of functionalities from RPA to AI, include:
- UiPath: a comprehensive RPA platform known for its user-friendly interface and extensive capabilities;
- Automation Anywhere: offers a cloud-native, web-based RPA solution with strong AI integration;
- Blue Prism: known for its enterprise-grade RPA solutions, often used in large-scale implementations;
- BotCity: a new entrant in the field, this RPA solution is distinguished for its high-code flexibility, prioritizing autonomy and cost-effectiveness compared to low-code platforms;
- Microsoft Power Automate (formerly Microsoft Flow): Integrates well with Microsoft’s ecosystem and offers a range of automation capabilities;
- Pega Systems: provides a robust platform for business process management and automation;
- Appian: known for its low-code automation platform that allows quick development of applications;
- IBM Watson: Offers AI and machine learning capabilities for more sophisticated automation tasks;
- SAP Intelligent Robotic Process Automation: Designed to integrate seamlessly with other SAP products;
Ready to implement hyperautomation?
We hope this article has made it clear that hyperautomation is not just a trend but a catalyst for transforming corporate processes. The agility and efficiency provided by this approach are crucial for organizational competitiveness.
Or, if you are taking the first steps in robotic process automation (RPA), take advantage of the opportunity to sign up for free on BotCity right now! BotCity offers a free Python code assistant for RPA, along with over 30 RPA frameworks for common automation use cases.