Robotic Process Automation vs. Machine Learning: Differences, Similarities, and Synergies

In the early 2000s, Robotic Process Automation (RPA) was a budding tool that relied on established technologies, such as screen scraping or workflow automation. Over the years, RPA has become the go-to automation tool used in all industries. The RPA market has achieved a total size of about USD 4 billion and is projected to surpass USD 13 billion by 2030.

Today, we’re experiencing a similar revolution as more and more companies adopt AI technologies such as Machine Learning (ML). They do that for a good reason—73% of business leaders believe that ML can double the productivity of their workers.

Thinking of Robotic Process Automation vs. Machine Learning intertwining increasingly, it’s important to know what they are, how they differ, and how you can benefit from them. This article discusses the topic of robotic process automation vs. machine learning to provide you with everything you need to know before you get started with either—or both.

In the case of Robotic Process Automation, the name says it all: it’s a solution that uses robots or bots, i.e., pre-programmed algorithms, to automate business processes.

By mimicking human behavior and combining it with their machine-like efficiency, RPA bots can quickly handle data-heavy, mundane tasks. These include data entry, copy-pasting records, compiling reports, and more.

This speed doesn’t come at the expense of accuracy. On the contrary, a well-implemented RPA bot with high-quality data sources can achieve a near-zero margin of error. Machines don’t get distracted, which eliminates common mistakes like typos.

RPA works best for tasks based on pre-defined rules and using structured data such as spreadsheets, databases, forms, etc. Another important aspect is volume—the true power of automation is its efficiency, so companies can capitalize on RPA the most by delegating bots to high-volume processes.

If all that sounds like RPA is a menace to human employees, don’t worry. RPA excels at the same tasks human workers despise most—mundane, repetitive processes that feel like they could be performed by… a machine.

These jobs are crucial to the everyday functioning of your business, but they take your specialists’ focus away from more creative and complex tasks. By teaming up with bots, human employees can spend more time on self-development, seeking new business opportunities, or finding operational improvements. That’s why it’s better to think of RPA as a tool rather than a replacement for your workforce.

Relaying repetitive and time-consuming tasks to RPA bots offers a range of benefits. What are they?

1. Increased productivity — Bots can perform repetitive workflows much faster than human employees and assist them in other tasks, increasing overall productivity.
2. Reduced costs — Higher efficiency lowers operational costs and brings cumulative savings in the long run.
3. Optimized processes — RPA eliminates errors and streamlines workflows, leading to faster project completion.
4. Fewer errors — Thanks to their accuracy, RPA bots help maintain data consistency and avoid costly mistakes.
5. Better compliance — RPA follows preset rules with 100% accuracy, minimizing compliance risk.
6. Improved customer experience — Automation helps reduce response times and solve customer service queries faster.
7. Higher employee satisfaction — RPA relieves employees from the workflows they dislike and improves their performance at more fulfilling tasks.
8. Data-based decision-making — By cleaning datasets and making them more accessible, RPA gives all the information to make better decisions.

RPA is a flexible solution that can be customized for various processes. This includes generic workflows that are vital for any business, regardless of the industry:

1. Invoice processing — Manual invoice processing takes time and is prone to errors. Bots can fulfill invoice-related subtasks like data extraction and entry or error reconciliation quickly and without mistakes.
2. HR processes — RPA can handle time-consuming tasks such as payroll, recruitment, onboarding, or data verification. This helps HR departments avoid delays caused by errors while increasing employee satisfaction.
3. Customer service — RPA bots can handle less complex interaction scenarios autonomously or assist your agents in solving more complex ones faster. Bots and voice assistants are also available around the clock.
4. Data operations — Whether it’s CRM, ERS, ERP, or other software, automation can greatly improve the data flow between all your systems. RPA bots will keep your datasets up-to-date and easy to find and retrieve.
5. Sales orders — Sales reps spend hours moving data between various systems, verifying it, and creating sales orders. RPA bots relieve them from all these tasks, allowing them to focus on prospecting leads instead.

However, RPA usability doesn’t end here, as the technology can also serve more industry-specific purposes. Examples of sectors where RPA has long been used with great success include healthcare, finance and banking, insurance, supply chain, and manufacturing.

Machine Learning (ML) is a subset of artificial intelligence. AI is concerned with developing technologies allowing machines to think like humans. ML is specifically focused on imitating our ability to learn.

Unlike RPA, ML algorithms aren’t programmed to follow specific patterns. Instead, they use historical data to build logical models. Then, these models can be fed new data to make predictions, find patterns, improve performance, or draw conclusions. The more data is put into the model, the more accurate it becomes. The ultimate goal is to solve advanced problems that require analyzing rather than processing data.

But isn’t data analysis something we can do by ourselves? This is correct. However, the problem arises when we consider how much information there is. We produce over 1 trillion MB of data every day. Of course, businesses only make use of a fraction of this data. Still, it’s hard to imagine how one would sort such volumes manually, let alone make sense of them.

At the same time, success stories of companies like Spotify or Uber show that big data can be a true treasure trove for companies that can use it correctly. That’s where ML steps in.