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.

Machine learning allows companies to tap into information better than ever before. This presents them with new opportunities such as:

• Better customer understanding — ML models can give invaluable knowledge about the customer base, e.g., client preferences, behavior patterns, or purchasing decisions.
• New business models — Data can empower new product features or entire business models. For instance, machine learning drives Netflix’s and Amazon’s recommendation engines, Google ads optimization, and Uber’s driver-rider matching.
• Personalized experience — Insights gained from customer data analysis allow businesses to deliver experiences tailored to the specific needs of their customers.
• Constant self-improvement — ML algorithms get better with every new dataset received. Over time, they will learn how to make more accurate conclusions faster.
• Operational improvements — Inward-focused ML algorithms can help businesses evaluate their processes, find inefficiencies, and develop solutions.
• Tighter security — When paired with the existing security systems, machine learning can boost their accuracy as it learns to recognize threats better and discover vulnerabilities.

Machine learning has nearly unlimited applications, and new ones are constantly discovered. Here are some that are already having an impact across industries.

• Computer vision — Visual formats such as photos or videos have always been problematic for machines. ML allows computers to extract meaningful information from these media and use it in radiology imaging, face recognition, self-driving cars, or IoT devices.
• Speech recognition — Machine Learning enables speech-to-text tools to understand human speech better and translate it into a written format. This technology is already used for voice search, accessible texting, transcription, or in voice assistants like Google Assistant or Siri.
• Fraud prevention — ML models can be taught to identify deviations from standard procedures such as financial transactions. Once an anomaly is detected, it’s automatically flagged as a suspicious activity that requires further investigation. The model will use this data to spot fraudulent activity with greater accuracy. Similarly, ML is used in cybersecurity, e.g., to detect data breaches or malware attacks.
• Suggestion engines — Machine Learning can discover preference trends in past customer choices. Based on this data, the algorithm automatically picks suggestions that correlate with customers’ interests. Businesses can also use this information to develop more effective cross-selling strategies, e.g., by recommending relevant products during checkout.
• Customer service — ML-powered chatbots and virtual assistants can go beyond relying on predefined interaction scripts. In addition to answering common questions about shipping or availability, they can provide personalized recommendations or cross-sell additional products. Machine learning can also analyze support ticket data and suggest customer help processes and content improvements.

Similarly to RPA, machine learning also has more industry-specific applications. One of the most prominent examples is healthcare and diagnostics. For instance, Google’s Deep Learning ML program detected breast cancer with an 89% accuracy rate. In another test, a machine learning algorithm achieved 92% accuracy in predicting the mortality of COVID-19 patients.

As you can see, the two technologies share the same overarching goal: imitating humans to streamline business operations. However, they aim to do that from a different angle. While RPA focuses on replicating human behavior, ML solutions imitate how we think and learn.

An important factor to consider when comparing robotic process automation vs. machine learning is how they work. RPA uses rules created by business experts and RPA consultants to perform specific workflows. Once these rules are defined, RPA runs the sequence repeatedly. Thus, any improvements require constant performance monitoring and reprogramming.

As for ML, only the initial, generic algorithm is built by developers. After that, the algorithm develops logical models and self-learns using the training data it receives. Over time, ML models become more precise and efficient on their own. The tricky part is getting there—before the ML model becomes near-autonomous, it requires supervision and tweaks during training.

These characteristics affect the type of data RPA and ML can use. On its own, RPA works best with standardized, structured records like spreadsheets and relational databases. On the other hand, machine learning can use any data as long as it’s provided with high-quality training material. That’s why cleaning and preparing the initial data is key when implementing ML.

The capabilities of each solution dictate the tasks they are best at. RPA excels at manual, low-value, repeatable tasks that follow clear conditions and use structured data. Increased efficiency eventually brings an ROI and starts generating savings. On the other hand, ML is used to analyze datasets in search of patterns that can be turned into valuable insights or improvements to existing processes.

If there’s one similarity between RPA and ML, it’s their usefulness. Although both work differently and serve different purposes, they can be used by businesses from any industry.

ML and RPA are quite different, each with strengths and limitations. What if we combined them to complement each other and form an even better, diverse automation strategy?

Solutions that use RPA and AI (including ML) are known as intelligent or smart automation tools. Smart automation systems combine the best of both worlds: RPA’s efficiency and ML’s learning abilities.

When paired with ML, RPA can learn from exceptions and its own mistakes to self-improve without reprogramming. Intelligent automation systems can dynamically find and develop solutions to their problems. By analyzing existing processes, ML can help businesses find workflows where RPA would have the most impact.

Another benefit of intelligent automation lies in the ability to work with unstructured information. Normally, RPA is limited to standardized data formats. With the aid of ML, it can be taught to recognize and extract data contained in images, videos, emails, and other unstructured data sources.

This relationship isn’t one-sided, though. Machine learning relies on large volumes of well-prepared data to learn how to make correct conclusions and identify patterns. RPA can quickly capture, clean, and update these training datasets so that ML can achieve higher accuracy in a shorter time.

We’ve discussed how ML and RPA can be used in any industry. Smart automation is no different.

Should you choose RPA, ML, or implement both jointly? All have their benefits, but ultimately, finding the best solution for your company depends on your processes, goals, employees, and other features that form its unique character.

Luckily, you don’t have to make this decision on your own. At Flobotics, we’re happy to share our automation expertise and help our clients through innovative solutions. Get in touch to discuss all available options and receive a free proof of concept to see how automation can transform your business.