What is hyperautomation?

There is considerable confusion in the market regarding automation software, with terms like Robotic Process Automation (RPA), Intelligent Automation (IA), and hyperautomation frequently interchanged and misunderstood. These terms have been developed and propagated by analysts, software vendors, and solution integrators, each aiming to carve out their niche.

But what do these phrases mean? And how should you be using them when discussing implementing them within your business? This guide will walk you through the terminology, the key differences, potential benefits and how they can be combined to maximum effectiveness.

Hyperautomation – A combination of emerging technologies

Hyperautomation is the enhancement of the automation of business processes—such as production chains, workflows, and marketing—by integrating technologies like Artificial Intelligence (AI), RPA and various others. This enables the automation of almost any repetitive task and helps identify which processes can be automated, utilising bots to execute them.

Hyperautomation is crucial for digital transformation because it removes human involvement in low-value tasks and provides unprecedented business intelligence through data. It plays a pivotal role in developing agile organisations that can quickly adapt to changes in a volatile global market.

Key ingredients of hyperautomation

Hyperautomation integrates various technologies to optimise business processes:

• RPA: Configures software to automate repetitive, structured tasks in digital systems. Can be supervised with a human in the loop (attended) or run in the background (unattended) and usually constructed by RPA experts.
• AI: development of computer systems capable of performing tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and learning from experience.
• Big Data: Stores, analyses, and manages vast amounts of data to identify patterns and develop optimal solutions.
• Cobots: Short for ‘collaborative robots’, work alongside humans safely, equipped with sensors and advanced programming. They’re used in various industries for tasks like assembly and assistance, enhancing efficiency and safety.
• Chatbots: AI-powered systems that engage in real-time text or speech conversations with humans for automated support.

Robotic Process Automation vs. Hyperautomation

The origins of RPA

RPA began by using software agents, or ‘bots,’ to interact with applications just as a human would. These ‘bots’ were engineered to perform specific tasks based on predefined rules without needing custom programming interfaces to drive applications. They could work with various application types, from Windows applications and web pages to mainframe and Java apps, even those developed with obsolete technologies.

The evolution of RPA

Today, RPA has evolved and can now be integrated with more advanced technologies to enhance its utility in business processes. RPA forms the foundational layer for both IA and Hyperautomation, enabling interaction with applications without requiring extensive programming.

In the absence of RPA, automating tasks across various systems would require multiple connectors for IA to extract data and initiate actions. Hyperautomation expedites this process by integrating RPA and AI, swiftly revolutionising business operations.

Differences between RPA and hyperautomation

• RPA: Focuses on automating specific tasks using bots. It’s a component of hyperautomation, working alongside automated workflows, machine learning, AI, and low-code platforms.
• Hyperautomation: Encompasses a broader transformation of organisational operations. It accelerates the deployment of new capabilities while ensuring governance and security, redesigning work processes to leverage emerging technology fully.

In essence, RPA handles the ‘doing’ part of automation, while Hyperautomation focuses on integrating and optimising these actions across the organisation using advanced technologies.

The role of RPA in hyperautomation

RPA is crucial in hyperautomation, interacting with AI to create an evolving, efficient system. For instance, in a banking system, RPA pulls transaction data, which AI evaluates for fraud patterns. If fraud is detected, RPA can take swift action, such as freezing transactions and alerting relevant parties.

Distinguishing between hyperautomation and intelligent automation

While hyperautomation and IA both leverage AI-powered platforms to streamline processes, they serve different purposes:

• IA: Combines RPA and AI to make decisions and scale automation across processes.
• Hyperautomation: Aims to quickly identify and automate as many business and IT processes as possible. It uses IA tools and additional technologies like Optical Character Recognition (OCR), Intelligent Document Processing (IDP), and Natural Language Processing (NLP) to run without human intervention.

Hyperautomation is essentially about automating everything that can be automated within an organisation, streamlining workflows to function autonomously and driving digital transformation. By identifying automation candidates and rapidly deploying solutions, a situation of hyperautomation is achieved.

Hyperautomation with cognitive technologies

Scalability and governance are critical when rapidly automating processes. Organisations should employ various cognitive technologies and adopt strategies like a Robotic Operating Model (ROM) or develop clear business use cases to ensure a robust rollout that can rapidly grow.

RPA excels at task execution, but some actions require cognitive abilities. For example, OCR is needed to convert document images into usable data, which IDP then processes appropriately, such as reading company names and outstanding payments from invoices. NLP can read emails and understand customer intent and sentiment, enabling RPA to respond with tailored messages or route conversations to human agents for a personal touch.

The future of work with automation

As automation technologies advance, RPA, IA, and hyperautomation will continue to evolve, supported by better AI engines. Organisations should think big when building their automation strategies to stay competitive. Combining RPA and AI offers a powerful synergy, extracting and evaluating information to optimise business processes internally and externally.
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