RPA vs Hyperautomation: What’s the Difference?

Where RPA started

RPA used robots or ‘bots’, defined as software agents, to interact with applications just like a person would. Without defining specific programming interfaces, a process analyst set which parts of an application a process used, then trained the robot to submit the changes according to that set of rules.   

The applications could be based on Windows applications, web pages, mainframe applications, Java apps or even homegrown applications written using long-extinct technology platforms.

The RPA bots would follow rules to interact with any of these applications, either simple rules such as “create a report and email it” or many-stepped complex rules. These steps might include evaluating specific fields in the application and then following those rules accordingly.

For example, “check the balance of the inventory”. If it’s less than a certain amount, it will issue a warning email and otherwise process the transaction and initiate the transfer to the location listed on the purchase order. 

What RPA is now

RPA still performs these tasks, but now it can be combined with more intelligent technologies to enhance its business uses.

RPA is part of the foundational layer upon which intelligent automation (IA) and hyperautomation are built. These concepts require an RPA platform to permit interaction with the applications, without needing to program the interactions.

Without RPA, automating communications would require many new connectors to enable artificial intelligence (AI) to pull data and initiate actions to the various corporate systems.

Hyperautomation is often confused with intelligent automation; while both use AI-powered automation platforms and tools to streamline business processes, they do have differences.

IA comprises of RPA, artificial intelligence and machine learning for decision-making and scaling automation, whereas hyperautomation is used by organizations to rapidly identify and automate as many business and IT processes as possible. Often, you’ll see IA being used with hyperautomation efforts.

Sometimes referred to as cognitive automation, intelligent automation (IA) links AI with the interactive capabilities of RPA. The two basic concepts that intelligent automation links together are thinking (AI) and doing (RPA). 

Essentially, hyperautomation is about automating everything across an organization that can possibly be automated. It’s meant to streamline workflows using IA and is set to run without human intervention. It can also employ other automation technologies such as optical character recognition (OCR), intelligent document processing (IDP) and natural language processing (NLP).

Hyperautomation with Cognitive Technologies

Automating quickly should not come with the sacrifice of scalability and governance. That’s why organizations automating their processes should employ a range of relevant cognitive technologies and set a strategy, either by using a robotic operating model (ROM) or by developing a business use case with clearly defined goals.

RPA is great at the ‘doing’ and has several capabilities to manage work through rules, yet there are some aspects of executing work that requires thinking before an action can happen.

Cognitive technologies in use

Some of RPA’s ‘thinking’ work involves reading documents, using OCR to pull out data into a form that the computer can use. Then you can use IDP to understand the document type so it can be processed appropriately. 

For example, anyone engaged in processing invoices will tell you that almost every organization has their own format for sending an invoice to their customers. Because of this non-standardized way of sending invoices, companies typically employ people to read the images of these documents and type the information into the accounting systems by hand. 

Depending on the number of invoices, this might require a large staff or a contract with an outsourcing firm to keep up with the volume. By building IA to read and understand the documents using IDP – then processing the results appropriately with RPA – the solution can reduce the amount of time people are engaged in manual efforts such as typing information into the system. 

But IA can be used for so much more than invoice processing. For example, by engaging a natural language processing (NLP) platform, an automated process can read emails that provide insight into support team or chatbot questions, allowing customers to interact in real-time with the company. 

The NLP platform can understand both what the customer is asking for (or the intent of the conversation) as well as the amount of emotional energy (or sentiment) that’s hidden in the word usage of the email or chat conversation. This unlocks the ability to process the message with RPA using specific, frequently used responses that can be tailored to the customer. Alternatively, the conversation could be routed to a person to help manage the interaction in a more personal way to improve the customer’s experience. 

These are just a couple of examples where combining RPA and another technology supports a richer experience. Another example is if you automated the evaluation of purchasing history using AI to recognize patterns in the buying interactions. This could be used to improve marketing campaigns or influence the decision and ability to offer new, related goods and services.

The automated data gathering potential where the RPA platform can feed into one or more AI engines can increase the way that business is optimized both internally, by reviewing historical processing of transactions, and externally, by pulling data from suppliers and logistics organizations to improve supply-chain management. 

What’s the Difference Between RPA and Hyperautomation?

You might be wondering, what’s the RPA got to do with hyperautomation

The biggest difference is that RPA focuses on deploying bots to take over and automate simple processes, whereas hyperautomation is a more expansive transformation of how an organization operates and heightens the value of RPA.

RPA

RPA is the ‘doing’ part of automation, focused on handling tasks as they’re assigned. RPA falls under the umbrella of hyperautomation along with automated workflows, ML, AI and low-code application platforms.

Hyperautomation

Hyperautomation allows faster deployment of new capabilities, all while maintaining governance and security. It’s not only focused on task-based performances. It looks for ways to redesign the work being done with technology. It’s another way of unifying your workforce by involving humans to interpret big data and provide higher-value insights.

In addition to supporting decisions that the ‘thinking’ part of IA offers, the ‘doing’ is a critical component that cannot be overlooked. RPA and AI together offer a compelling combination of extracting information from various corporate systems and evaluating that data using powerful algorithms.

Artificial intelligence

But the real magic comes from acting on the decisions that were made by the AI engine. On its own, AI is like a disconnected brain.

Without the capabilities offered by RPA, AI would require special connections to database systems to pull in the data it needs to evaluate, as well as coding for application programming interfaces (API) so that the AI decisions can yield an action. 

What is the Role of RPA in Hyperautomation?

RPA is an essential piece of the hyperautomation puzzle, and it interacts with AI to create a balanced learning system that’s constantly evolving to be more efficient and effective.

In the case of a banking system evaluating transactions for fraud, data is pulled from accounts via RPA to be fed into an AI algorithm. The AI engine evaluates whether the transactions match the pattern with which the customer typically spends their money and whether that customer is breaking any laws by funding illegal activities.

But simply identifying these activities is not enough. 

If a transaction is found to be fraudulent or at least deserving of additional inspection, the AI engine needs to be able to act by stopping the transaction and alerting someone about potential fraud. Those actions might be complex, such as alerting the government, freezing the transaction, communicating with internal examiners and sending a notice to the customer. 

With an RPA system, those actions can be handled quickly and easily by interacting with the various systems inside and outside of the organization, without requiring programming.

Without the ‘doing’ aspect that RPA can offer, the ‘thinking’ component requires a lot more effort. Therefore, intelligent automation and by association, hyperautomation, are a powerful combination of RPA and AI. 

Automation and the Future of Work 

With the advances in automation technologies, clearly RPA, intelligent automation and hyperautomation will continue to evolve as better AI engines support decision-making within the operations of a company.

Starting out may seem daunting, but thinking big will often pay big dividends as your organization builds the layers of automation from RPA through hyperautomation. The biggest risk is not thinking big enough while competitors embrace and adopt these technologies to gain a competitive advantage. 

In addition to supporting decisions that the thinking part of intelligent automation offers, the doing is a critical component that cannot be overlooked. RPA and AI together offer a compelling combination of extracting information from various corporate systems and evaluating that data using powerful algorithms. But the real magic comes from acting on the decisions that were made by the AI engine. On its own, AI is like a disconnected brain. Without the capabilities offered by RPA, AI would require special connections to database systems to pull in the data it needs to evaluate, as well as coding for application programming interfaces (API) so that the AI decisions can yield an action.

For example, in the case of a banking system that is evaluating transactions for fraud, data is pulled from accounts to be fed into an AI algorithm. The AI engine evaluates whether the transactions match the pattern with which the customer typically spends their money, and whether that customer is breaking any laws by funding illegal activities. But simply identifying these activities is not enough.

If a transaction is found to be fraudulent or, at least, deserving of additional inspection, the AI engine would need to be able to act by stopping the transaction and alerting someone about potential fraud. Those actions might be complex, such as alerting the government, freezing the transaction, communicating with internal examiners, and sending a notice to the customer.

With an RPA system, those actions can be handled quickly and easily by interacting with the various systems inside and outside of the organization, without requiring programming. Without the doing aspect that RPA can offer, the thinking component offered by AI requires a lot more effort. Therefore, intelligent automation is a powerful combination of RPA and AI.

Alexis Veenendaal is an associate content writer and editor at SS&C Blue Prism. She’ll tell you all the cool tips and tricks for implementing intelligent automation into your workplace. She has lived and worked internationally as a professional writer and designer for nearly a decade after graduating from the University of Lethbridge for English Literature. Her personal pursuits include authoring books and digital cartography.