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RPA vs. AI

What’s the difference between robotic process automation (RPA) and artificial intelligence (AI)? Learn the key facts in plain terms.

In this age of digital transformation, companies that want to grow and stay competitive must automate their business processes. Automation helps free your team members to focus on what they do best: cognitively complex, dynamic work. But do you need robotic process automation (RPA) or artificial intelligence (AI)? The differences between RPA and AI still cause confusion for some.

You need to understand the strengths and weaknesses of RPA and AI as automation technologies—and be able to explain them to others—before you can shape a holistic automation strategy. Let’s take a closer look at RPA vs. AI. 

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What is RPA and how does it work?

RPA imitates the way humans interact with a computer when they’re doing simple, high-volume, and repetitive tasks. With RPA, a software robot can work on a wide range of simple processes such as clicking around a user interface, browsing the web and collecting data, logging in to a desktop, and even entering inputs on a keyboard. This means RPA can take over the kind of tasks that many humans would call drudge work while boosting operational efficiency for the wider organization.

RPA imitates the way humans interact with a computer when they’re doing simple, high-volume, and repetitive tasks.

An RPA example.

Let’s say your company receives thousands of orders a month, each needing to be manually entered into your ERP system by a human. Writing an RPA bot to perform the data entry action will have impactful benefits:

  • The RPA bot can execute these actions much faster than human employees can.

  • RPA is less error prone, as it is programmatically driven.

  • The person who was previously doing this task can shift their focus to non-tedious work.

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When RPA does not fit.

So, what’s the catch with RPA? Well, writing a robot to mimic human behavior is not always the most efficient way to automate. For example, let’s say this same ERP was more modern and had an application programming interface (API) that allowed you to send the order data digitally via an integration. This would be much more efficient than RPA, more performant, and wouldn’t be at risk of breaking if the ERP user interface were to change as a result of an update. Not so for RPA. 

Knowing when RPA is not needed or not ideal reinforces its value when it is needed. If there is no API, which is the case for a lot of legacy systems, mainframes, and websites, then RPA becomes a necessary technology for automating that workflow. Your best bet at achieving a high ROI with RPA is picking high-volume, mundane tasks with repetitive steps that cannot be automated via a direct computer-to-computer integration.

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What is AI vs. RPA?

What is AI, then? And how does it compare to RPA? A helpful way of thinking of AI is as cognitive automation, or automation of human thought.  If RPA imitates what a person does, AI imitates how a person thinks. AI can be used for making cognitive decisions, like classifying incoming emails to be routed to different support groups, predicting fraud in insurance claims, or even suggesting clauses for contract awards.

 If RPA imitates what a person does, AI imitates how a person thinks. 

In some cases, an AI tool has its own set of eyes via computer vision technology, which helps it make sense of things visually as a human would. This is helpful when dealing with unstructured data or information that hasn’t been stored in a database or made usable to computer applications. One thing we took for granted in our RPA order entry story was the fact that the order data was already structured in a way that’s easy for a database to read and ready for use by a robot or API integration. Very often order data comes in an unstructured format, such as a PDF or even a handwritten document. These are not yet ready to be used by a robot or integration. Data preparation work is required.

AI vs. RPA vs. ML.

Machine learning (ML) refers to how a digital system “learns” from real data that it examines using patterns or rules. ML is used to solve problems and analyze data sets, complementing the work of AI tools. 

In the order entry example we just discussed, how could ML help? Well, machine learning models could help train AI to understand the structure and key-value pairings of data on an order form, then extract that data from the document and convert it into a structured format that the robot or integration can use to enter into the ERP. You can also set up logic to send the document straight through if the AI was confident about the extraction with no human interaction needed. 

One challenge with AI often boils down to the toil of building and maintaining machine learning models to execute your predictions and data extractions. A more specialized model can perform better but will apply to fewer use cases, whereas a more generalized model can perform less optimally but apply to more use cases. As with most automations, you will achieve a better ROI from machine learning by using it in high-volume workflows.

A commonly misunderstood difference between AI and RPA is that while many AI tools use technology like machine learning and neural networks to get smarter over time as they gain data and experience, RPA tools do not. RPA tools maintain consistent software bots for performing unchanging tasks not involving intelligent decision making. 

RPA, AI, and your automation strategy.

For organizations shaping automation strategy, RPA vs. AI is not an either/or decision—it’s a matter of applying the right tool to the right problem. You’ll need to apply RPA to some parts of your business process and AI to others. That’s why RPA is a critical capability of an AI-powered process platform, also known as a hyperautomation platform, which helps enterprises automate entire business processes end to end for greater operational efficiency. This kind of process platform streamlines complex processes—such as managing the customer lifecycle in banking, optimizing supply chain operations, or speeding up insurance underwriting—processes that involve multiple people, departments, and systems.

An AI-powered process platform also brings other capabilities to the table, including intelligent document processing (IDP), workflow orchestration, and a data fabric that connects data sets across disparate software systems, whether they’re on-premises or in the cloud, and creates a complete view. Think of data fabric as a virtualized data layer. When comparing process platforms, seek out one that includes low-code technologies that speed software development and ease collaboration with business teams.

The bottom line: process automation helps you make workflows more efficient and productive across an entire organization rather than just automating in individual corners with an RPA bot. 

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