The current speed of doing business requires efficiency and agility that only automation can achieve. In fact, IDC estimates that by the end of 2022, the global economic impact of connected AI-driven automation across different sectors of IT and business functions will reach approximately $3 trillion. The increasing automation rate has resulted in the birth of various automation terminologies. Among them are the commonly mistaken Intelligent Automation (IA) and hyperautomation.
With experts often exploring the benefits of the two digital process automation solutions, others focus on the differences between them. Are IA and hyperautomation the same, or are there any discernible differences between the two concepts? Keep reading to find out.
What is Intelligent Automation?
Before we dive into the differences between intelligent automation and hyperautomation, it’s crucial to understand what each term means. IA, also referred to as cognitive automation, is an automation technology that blends robotic process automation (RPA) with several other technologies, including:
- Natural Language Processing (NLP)
- Intelligent Document Processing (IDP)
- Optical Character Recognition (OCR)
- Machine Learning (ML)
- Artificial Intelligence (AI)
These technologies enable end-to-end automation through smart bots with decision-making capabilities. These bots can manage unstructured and complex inputs, learn, and improve their processes. As a result, they are commonly used to increase efficiency and minimize repetitive, error-prone manual duties.
What is Hyperautomation?
Unlike IA, hyperautomation is an advanced automation approach equipped with cognitive capabilities, enabling people to be involved in the process. Hyperautomation involves automating every automatable aspect of an organization.
The purpose of hyperautomation is to smooth organizational processes using Intelligent Process Automation (IPA) and other technological solutions, such as:
- Digital twins
- Low-code/no-code tools
- Internet of Things (IoT)
- Application Programming Interfaces (APIs)
- Integration platform-as-a-service (IPaaS)
Gartner listed this revolutionary automation solution among the top 10 strategic technology trends. Hyperautomation combines multiple technologies to provide top-quality automation using data from different sources. As a result, it has moved from a choice to a survival condition.
Intelligent Automation (IA) Vs Hyperautomation
Vendors and industry analysts use varying terms to refer to the same thing. IA and hyperautomation are often used interchangeably with IP and cognitive automation. These terms refer to a technology that combines artificial intelligence and robotic process automation to automate complex, unstructured processes.
Using hyperautomation and IA interchangeably may make sense because they involve using multiple automation technologies together to attain higher automation levels. However, several aspects differentiate these two concepts, including:
- Definition
- Maturity level
- Scope/Coverage
- Governance Approach
- Outcome
- Who implements them
- Use cases
Intelligent automation is a particular technological solution used within hyperautomation programs. On the contrary, hyperautomation is defined as a business approach that leverages the power of multiple technologies to automate and streamline various business processes.
Maturity Level
Intelligent automation is scaling and relies on sophisticated AI-powered process automation featuring cognitive abilities. On the other hand, hyperautomation is transforming. While it is a complex AI-driven process automation with cognitive capabilities, just like IA, it can loop people into the process. You cannot loop humans into the intelligent process automation process.
Scope/Coverage
Intelligent automation and hyperautomation significantly differ in the scope they cover. IA assumes higher-function duties that require analysis, decision, reasoning, and judgment. On the contrary, hyperautomation is all-encompassing, implying that every automatable organizational aspect is automated. Therefore, it covers a significantly broader scope than intelligent automation.
Governance Approach
Intelligent automation uses a different governance approach compared to that used in hyperautomation. While IA exclusively uses a process-first governance approach, hyperautomation combines a people-first and process-first approach. Therefore, humans are involved in the process, unlike in IA.
Outcome
Intelligent automation results in efficient complex operations. On the other hand, hyperautomation leads to smart and efficient operations. The broader scope enables hyperautomation to streamline every business process that can be automated, leading to smarter decisions, insights, and outcomes.
Who Implements Them
Intelligent automation is implemented by Information Technology (IT). On the other hand, hyperautomation is enforced by IT and the Democratization of Automation Development.
Use Cases
Having identified the 6 differences between hyperautomation and intelligent automation, we can now highlight real-world use cases of the two concepts. This will help you visualize what IA and hyperautomation look like in practice. Let’s dive into details, shall we?
Intelligent Automation Use Case
Any business environment can gain from streamlining its manual tasks and processes through automation. From manufacturing to finance and healthcare, Intelligent automation offers numerous benefits that improve customer experience and positively influence the bottom line.
One specific instance where intelligent automation is used is the United States Department of Veterans Affairs. Initially, processing claims at the institution was made manually. As a result, there was a tremendous overload, and many people needed to enter data into databases and sort emails. As it was a human-intensive process, the entire process was significantly expensive and error-prone.
As a result, this department integrated intelligent automation, which automated its business processes and tasks using advanced technologies, such as RPA bots. This technological solution minimized the turnaround time by a whopping 90% and enhanced accuracy.
Hyperautomation Use Case
Any company, regardless of size and scope, can gain from hyperautomation. Organizations struggling with inefficient processes, inconsistent product quality, and stiff competition can leverage the power of hyperautomation to streamline their operations, stabilize their product quality, and gain market share.
For example, a manufacturing firm is an excellent example of the depth and breadth of improvements this solution can provide to a company. Processes such as payroll, inventory, customer interactions, and billing can utilize Business Process Automation (BPA) to smooth their operation on a wider scale.
Utilizing process mining can help the company to get a clearer picture of its processes. As a result, it can identify the processes that best fit automation and AI. Generally, hyperautomation improves using solutions like RPA and BPA streamlines front- and back-end operations, improving accuracy, speed, and quality of business performance.
Final Thought
Hyperautomation and intelligent automation are both closely related and beneficial to an organization. However, they exhibit some differences, especially in scope, maturity level, and governance approach. For full-scale automation, go for hyperautomation, and for specific business process automation, go for intelligent automation.
Related: The Impact of Artificial Intelligence on Various Industries
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