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March 10 2025
Curious about artificial intelligence (AI) and robotic process automation (RPA)? Here’s a straightforward guide.
In today’s digital era, businesses aiming to grow and stay competitive need to automate their processes. Automation frees team members to focus on tasks requiring creativity and complex problem-solving. But the question remains: should you use RPA or AI? Many people still find the distinctions between these two types of automation unclear.
To build an effective automation strategy, it’s essential to understand the unique strengths and limitations of RPA and AI—and to be able to explain these differences clearly. Let’s dive deeper into RPA vs AI.
What is RPA and How Does it Function?
Robotic Process Automation, or RPA, mimics humans’ actions when performing simple, repetitive tasks on a computer. Through RPA, software robots can handle various routine tasks, such as navigating user interfaces, browsing and collecting data from the web, logging into desktops, and even typing inputs on a keyboard. RPA manages the monotonous work that many consider tedious, helping boost overall organizational efficiency.
Example
Imagine your company processes thousands of orders each month, each requiring a human’s manual entry into your ERP system. By developing an RPA bot to automate these data entry tasks, your company could experience several key benefits:
- The RPA bot performs these actions significantly faster than any human worker.
- Since it’s automated, the bot is less likely to make errors.
- The employee who used to handle these entries can now focus on more engaging and meaningful tasks.
This way, RPA speeds up routine work, reduces errors, and allows team members to tackle more strategic responsibilities.
When RPA Isn’t Ideal?
So, are there downsides to using RPA? Yes, in some cases, there are more efficient approaches than programming a robot to mimic human actions. For instance, if the same ERP system had an application programming interface (API) that allowed data to be transferred directly, integrating the API would be faster, more efficient, and less prone to issues. Unlike RPA, API-based integrations aren’t at risk of failing due to user interface changes after an update.
Understanding when RPA isn’t necessary or optimal highlights when it truly adds value. RPA is especially useful when there’s no API option, typical for many older systems, mainframes, and websites. To maximize RPA’s return on investment, it’s best to apply it to high-volume, repetitive tasks with predictable steps that can’t be automated through direct system-to-system connections.
RPA vs. AI
So, what exactly is artificial intelligence (AI), and how does it differ from robotic process automation (RPA)? Think of AI as “cognitive automation” — it automates human thought, whereas RPA automates human actions. While RPA replicates what people do, AI replicates how people think. AI can make decisions, like classifying and routing emails to support groups, identifying possible fraud in insurance claims, or even suggesting specific contract clauses.
Sometimes, AI has a visual component called computer vision, which lets it “see” as humans do. This capability is precious when handling unstructured data—information not already organized for easy computer access. For example, while RPA could directly process structured data from a database or spreadsheet, real-world order data often arrives in unstructured formats like PDFs or handwritten documents, which require more preparation.
RPA vs. AI vs. ML
Machine learning (ML) is how digital systems “learn” from data, identifying patterns to make predictions or draw insights, which complements AI’s capabilities. In our order-entry example, ML could help by training AI to recognize and extract data from an unstructured order form, turning it into a structured format that RPA or other automation tools can process. With sufficient accuracy, AI might even process the document without human oversight.
Building effective ML models is a common challenge with AI. Specialized models, which perform best on specific tasks, may not apply widely, while generalized models handle various tasks but may sacrifice some precision. Like RPA, machine learning yields better results in high-volume applications where repeated predictions and data processing provide significant value.
One key difference between AI and RPA is that while many AI tools grow smarter over time through machine learning and neural networks, RPA tools are static—they execute the same tasks without learning or evolving. RPA’s software bots focus on consistent, rule-based actions that don’t require decision-making, making it ideal for straightforward, repetitive tasks rather than complex, intelligent automation.
RPA, AI, & Your Business Automation Strategy
Designing an automation strategy is not about choosing between RPA and AI—it’s about using the right tool for each part of your business process. RPA fits some tasks, while AI is better suited for others. That’s why RPA is often a key part of an AI-powered process platform, or what’s known as a hyper-automation platform. Hyperautomation platforms allow businesses to automate workflows from start to finish, driving significant operational efficiency. These platforms are especially effective for complex, multi-step processes that touch various departments and systems, like managing the customer journey in banking, streamlining supply chain operations, or accelerating insurance underwriting.
An AI-powered process platform provides additional capabilities beyond task automation. It typically includes intelligent document processing (IDP) for unstructured data, workflow orchestration, and a data fabric. A data fabric acts as a virtualized data layer, linking data from different systems across on-premises or cloud environments to create a comprehensive view of the data landscape. When evaluating process platforms, look for features like low-code tools that simplify software development and encourage collaboration with business teams.
Conclusion
RPA and AI have completely transformed modern business, and it’s easy to see why. In a remarkably short period, they’ve elevated innovation to new heights, revolutionizing every aspect of business operations. By enhancing quality, driving ROI, and achieving efficiencies once thought impossible, they’ve set a new standard. The distinction between the two only highlights the power of combining their strengths—using RPA’s reliability and low-maintenance nature as a strong foundation and then building on it with AI’s advanced, human-like cognitive abilities.
At PureLogics, our deep expertise in RPA and AI has earned us the trust of companies around the globe. We’d love the opportunity to explore your intelligent automation goals and challenges. For more information, feel free to reach out—we’re excited to help bring your vision to life!
