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November 22, 2022

Robotic Process Automation (RPA) and Artificial Intelligence (AI)


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

With companies pushing towards digitization, Artificial Intelligence (AI) and Robotics Process Automation have proven to be game changers – they have the potential to revolutionize and improve all aspects of the business throughout the supply chain.

The global RPA market size hit $7.11 billion in 2021 and is expected to reach 43.52 billion in 2029, growing at a CAGR of 23.4% [1]. Likewise, the AI global market size was estimated at $93.5 billion in 2021 and is expected to grow at a CAGR of 38.1% between 2022 and 2030[2].

By leveraging these technologies, companies can streamline business processes, improve customer satisfaction, and reduce operational costs. But despite their numerous similarities, AI and RPA have different capabilities and use cases throughout an organization’s supply chain.

This article will explore the intricacies of RPA and artificial intelligence, focusing on their similarities and differences and how they could benefit your business.

What is RPA (Robotic Process Automation)?

Robotics Process Automation (RPA) is software designed to emulate human actions to complete rule-based tasks. By ‘understanding’ what’s on the screen, completing the right keystrokes, and identifying and extracting data, RPA software can automatically complete continuous, repetitive, and clearly defined tasks that would otherwise require manual execution.

robotic automation, laptop, arm, typing on keyboard, wires

Companies can use RPA tools to develop and configure bespoke ‘robots’ to process transactions, trigger responses, manipulate data, and communicate with other systems. The result is a streamlined and expedited business process with limited human resource requirements. This enables businesses to dedicate their time to other high-value business operations and customer service, which could potentially scale the business.

RPA has a wide variety of use cases in numerous industries, including the financial sector, insurance, and telecommunication. Typically, any business that relies on massive amounts of data to complete intricate and time-sensitive operations can benefit from RPA.

Is RPA Artificial Intelligence?

Artificial intelligence is an outcome-focused, data-driven technology that simulates human intelligence. Essentially, AI derives cognitive decisions by combining automation with machine learning, language processing, algorithm mutation, and hypothesis generation to produce analytics and create insights the same way a human would or even better.

On the other hand, RPA is a rule-based software designed to perform tasks the same way a human would. Therefore, despite their seemingly similar characteristics, RPA has no ‘intelligence.’
robot arm, women and men are smiling, artificial intelligence
That said, both technologies could offer plenty of benefits when combined. When integrated into an RPA system, AI could automate various business workflows and assist with more complex jobs.

Numerous businesses are already using this combination to streamline their workflows. And, as time goes on, the relationship between AI and RPA will grow even closer. That means that although they’re currently being used as separate technologies, future RPA software may come equipped with AI capabilities.

RPA and AI: The Difference

Before we get to the differences, it is important to note that RPA is under the AI umbrella. AI basically covers everything from process-driven technologies like robotic desktop automation (RDA) and RPA to increasingly complex technologies like machine learning and AI software with deductive analytics. Here are a few notable differences between AI and RPA:

Process-centric versus data-centric

One of the most notable differences between RPA and AI lies in their focus. As a process-driven technology, RPA focuses on automating repetitive, rule-based tasks that mostly require interaction with multiple disparate systems.

To implement RPA systems, you need to use process discovery workshops as a prerequisite to mapping out existing ‘as-is’ processes in order to document them in the Process Definition Document (PDD) [3].

Conversely, AI is all about high-quality data. AI implementation typically involves finding sufficient data to train machine Learning (ML) algorithms. Generally, businesses select an appropriate ML algorithm, then train it sufficiently with relevant data to recognize and analyze data sets faster and more accurately than humans.

robot arms, robot process automation, packaging lines, sensor tablet

Doing versus thinking

At its core, RPA is associated with doing, whereas AI acts like the human brain through a ‘thinking’ process that allows it to make decisions based on available data. Therefore, RPA is more suited to situations where organizations need to automate repetitive, rule-based processes.

On the other hand, AI is best suited to ‘intelligent’ applications where organizations need to automate complex processes, analyze data, and derive analytics.

How do RPA and AI work together?

In a bid to boost process automation, many businesses are combining the capabilities of RPA and artificial intelligence. By combining the two technologies, businesses can effectively address the shortcomings of using AI and RPOA separately.

For instance, if a business needs to process information from invoices or other documents that are only available as images, RPA may not be able to handle the task because it has limited image recognition capabilities. That said, RPA could still handle such tasks, but then all the documents would have looked the same and had the same structure.

artificial intelligence, robotics automation, detail

However, by combining Robotic Process Automation with AI, the business can overcome such hurdles since AI can analyze and respond to changes in the images’ structure.

Likewise, RPA can enhance the capabilities of AI in the data collection process. AI needs huge amounts of data to work effectively. Unfortunately, this data is quite difficult to collect, especially in large organizations with multiple departments.

To get this data, IT departments often have to log into multiple disparate systems and generate reports within certain parameters. Unfortunately, this process is tedious and time-consuming, thus leading to excessive workloads for human employees. But, by using RPA and AI concurrently, organizations can use RPA to perform the data collection processes automatically, which then passes the data to the AI system for processing and analysis.

How can RPA and AI increase business efficiency?

Using RPA systems embedded with AI capabilities can enhance business efficiency exponentially. AI uses collected data to streamline business processes and increase product value and customer satisfaction. Likewise, RPA boosts process automation based on structured data.

Each technology provides value on its own, but combining the two provides additional value in creating solutions that use a technological knowledge base to streamline processes and interactions between applications. The result is faster and more accurate solutions that could help the business by:

Increasing productivity

Automated applications and processes run faster. Organizations can benefit greatly by automating applications and processes and leveraging AI capabilities like decision-making and forecasts based on data collected from multiple sources in real time.

Take Deloitte, for instance. The financial giant recently used IBM’s tools to create bots designed to automate the generation of monthly management reports. As a result, the company has seen tremendous timesaving benefits and operational efficiency [4].

tablet, statistics, chart performance, robot automation, spare parts creation

Improving accuracy

Combining AI’s analytical and predictive capabilities with the process automation capabilities of RPA ensures better decision-making and less reliance on human intervention. This results in more precise results.

Manual data entry, for example, often results in numerous errors, which could affect the quality of a company’s decision-making process. By removing the human element from the equation, a business could benefit from improved accuracy and faster execution of tasks.

Reducing operational costs

Intelligent automation can result in huge cost-saving benefits, ranging from 40% to 70%, with payback spread across several months to several years [5]. Companies already leveraging intelligent automation solutions by combing AI and RPA capabilities have seen a 27% reduction in operational costs from implementation to date [6].

Improving customer experience

Organizations that use technology are in a position to understand their customers’ needs better, produce higher-quality products and communicate more effectively. The result is improved customer experience, which leads to better customer retention.

Final thoughts

AI and RPA are some of the most sought-after technologies across all sectors. Traditionally, these technologies were used separately, but recent times have seen a spur in AI-embedded RPA software that takes the technologies’ capability to a whole other level. The result is better process automation, cost-saving benefits, and improved customer experience.


[1] Robotic Process Automation Market. URL: Accessed November 11, 2022

[2] AI Market. URL: Accessed November 11, 2022

[3] What is pdd-sdd in RPA. URL: . Accessed November 11, 2022

[4] Case Studies: Deloitte. URL: Accessed November 11, 2022

[5] How Much is Intelligence Automation Saving You?. URL: Accessed November 11, 2022

[6] Automation With Intelligence. URL: Accessed November 11, 2022


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