What Is Robotic Process Automation (RPA)?

Michael Chen | Senior Writer | December 18, 2025

In This Article

When it comes to cutting-edge ways that technology harnesses the power of data, robotic process automation, or RPA, isn’t the first thing that comes to mind. But savvy businesses know that RPA is a key tool for optimizing workflows, whether for data entry, operations, or customer service. RPA can connect processes across departments and functions and free staff from manual tasks so they can focus on more critical and creative work.

What Is Robotic Process Automation (RPA)?

RPA is a form of computer-based process automation for workflows with clearly defined rules, inputs and outputs, and process triggers. Repetitive tasks can be accomplished more quickly using RPA versus when performed by people, without the variable of human error. RPA workflows can be defined using integrations with various applications or with no- or low-code tools—some RPA systems can even create scripts by observing a human completing a task. Real-world examples of RPA processes include automated data entry, inventory checks when stock hits certain levels, or processing of simple returns for retailers.

Key Takeaways:

  • RPA provides computer-based automation to process repetitive, rule-based tasks at high volumes without the complication of human error.
  • RPA is different from AI; it cannot self-learn or identify new patterns outside of its given workflow.
  • RPA frees users from repeatable time-consuming tasks, such as frequent data entry or deployment of software updates.
  • RPA is often used when creating AI agents, giving the agent the means to perform repetitive tasks.

RPA Explained

RPA is a technology that uses software robots, or bots, to automate repetitive, rule-based digital tasks previously performed by humans. RPA bots can interact with applications and systems just like a person would. By logging in, navigating screens, clicking buttons, extracting data, filling in forms, and moving files, bots can do things such as processing invoices, managing customer data, and generating reports. RPA increases efficiency, reduces errors, and frees human employees to focus on more complex, value-added activities that require judgment and creativity. And RPA may consume fewer resources versus having an AI system do similar work.

RPA technology works similarly to macros in applications such as Excel. Both operate using a set of rules and triggers for step-by-step task automation. However, RPAs can work across multiple applications and offer features such as conditional logic that help with more complex workflows. When built into a cloud infrastructure, scripts can be created with no- or low-code tools. That makes RPA accessible to business users, who can now create automations for tasks without help from IT.

RPA can be set up as an automated step within a workflow (unattended) or can be called manually (attended). A process can be further automated by combining it with AI agents.

How RPA Works

RPA works by using software bots to mimic how a person would use a computer to complete a task. To start, a business user or developer uses RPA software to record the exact steps to perform a process. The software records the clicks, keystrokes, and data operations made in relevant applications, including email, websites, spreadsheets, and business software like ERP. This recording creates a step-by-step script or workflow. A human expert can then refine this script, adding rules, loops, and logic to handle potential variations and decisions.

Once the workflow is defined, the bot is ready to be put to work. It can be scheduled to run at specific times or triggered by an event. For example, say it’s time to welcome a new employee. The bot automatically executes the scripted steps for an onboarding process exactly as a person would, but generally faster and without errors. It can collect the new hire’s information from recruitment systems; create user accounts, email addresses, and system access credentials; send welcome emails and how-to instructions for provisioning devices or resources; and generate any required compliance forms. If the bot is unable to complete a process from start to finish, it can route the transaction for human intervention.

Core Technologies Used

A common way to build a basic bot is by having RPA software “watch” and record a human’s actions. Companies can also deploy task mining tools that record user interactions—clicks, keystrokes, and data entry—across various applications to find repetitive tasks that are prime candidates for RPA. Process mining tools go a step further, analyzing event logs from enterprise systems to visualize entire end-to-end processes and help determine which might deliver a solid return on the automation investment.

For more complex automations, routines might be developed using a programming language like Python or JavaScript. These languages can use APIs to connect with systems for importing/exporting data, optical character recognition and object detection for processes that involve scanned documents, and integration with AI agents. This is where RPA evolves into intelligent automation, with AI allowing for handling of less structured data and simple decision-making.

RPA tools themselves can use no- and low-code tools for scripting, and if they are built into a cloud infrastructure, scripts can work across a broad set of data sources. In fact, RPA in the cloud is a major trend. The cloud improves scalability and makes it easier for bots to connect to a wide range of applications and data sources.

Finally, as companies accumulate a lot of bots, they need a way to manage them. Orchestration tools provide centralized control panels that handle tasks like assigning work to available bots, managing credentials, and providing detailed logs and analytics on bot performance.

How AI and RPA Combine

AI can work with RPA in two primary ways. First, an AI agent might use RPA to accomplish its assigned task. For example, if an AI agent’s job is verifying and preparing incoming documents, the agent can examine a spreadsheet to determine if the incoming format is compatible with the organization’s preferred format. If a transformation is required, it can activate an RPA script to do what’s needed.

Second, RPA scripts might include rules for pausing and asking a human or AI agent to intervene when it encounters certain conditions. The default might be to ask for human review and decision-making. However, RPAs can instead ask an AI agent to evaluate the situation and possibly determine how the RPA completes the task.

Examples of AI Automation in Action

Consider the combined use of an AI customer service chatbot agent and an RPA script for handling product returns. The chatbot takes in the return request form from a customer and uses RPA to verify that there is a valid reason for the return. However, the “Reason for Return” drop-down list has an “Other” option with an accompanying text field where the customer can explain the problem. Because this introduces unstructured data without clear next steps, the RPA would typically pause and flag for human review. With AI in the picture, the RPA can call on a large language model (LLM) with access to instances of customers choosing “Other” and seeing how they were handled. The LLM’s analysis can prompt the system to either accept the return, deny the return, or escalate to a human agent.

Benefits of RPA

Systemic automation using RPA creates a wide range of benefits, mainly related to more efficiency and fewer errors. RPA’s inherent flexibility allows for creative integrations, whether on applications for internal operations or customer-facing software. The following summarizes the most common benefits of RPA.

  • Cost savings: RPA gets tasks done faster while freeing up people for higher-value work. Fewer errors mean less time spent hunting down and fixing issues. Furthermore, organizations can easily increase their use of RPA to meet fluctuating demand without the costs associated with hiring and training new employees, then scale back down as needed.
  • Fewer errors: The aforementioned labor-intensive tasks often come with the real-world problem of human error. Consider a data entry workflow designed to import customer information from one form into a larger profile. Manual data entry might involve typing or copy/paste processes, where mistakes are easily made. By always following a predefined script, bots execute processes accurately.
  • Improved efficiency and productivity: RPA is built to automate rule-based steps and workflows. Often, these are labor-intensive, multistep tasks, such as data entry to populate forms or generate reports. When run in a cloud environment with broad access to data, RPA processes can work across the entire enterprise. And bots work 24/7 without breaks and are faster than humans to boot.
  • Strict auditability: Bots are programmed to follow processes precisely, promoting adherence to regulatory standards and internal policies with a clear, auditable trail.

Limitations of RPA

While RPA excels in many situations, it comes with limitations in both integration and function. The following are some of the most common challenges involved with RPA.

  • Limited to repeatable tasks: RPA bots excel at rule-based, repetitive, structured tasks. They are not suitable for processes that require judgment, critical thinking, or creative problem-solving. If a process has many exceptions or relies on unstructured data, such as interpreting the “other” reasons a customer might be returning a product, a standard RPA bot will fail without help from more advanced AI agents.
  • Not adaptive: Changes in menus, interfaces, or systems can disrupt RPA bots and lead to either broken processes or faulty outcomes. For RPA to produce quality results, the environment must be consistent over time. If workflows change because of new software or updated processes, RPA bots will not necessarily be able to adapt on their own. That’s because, while RPA can “learn” a process through manual demonstration, bots generally require manual reprogramming of new rules.
  • Scalability challenges: Because RPA often works in situations without APIs, exporting and scalability are often limited to the unique original circumstance. Creating a more scalable approach for any given RPA function depends on the specificity of both the goal and the software involved, along with the ability to pull back into more generic versions of those steps.
  • Security concerns: Bots may require extensive system access and privileged credentials to operate. Since a single bot may interact with numerous sensitive applications, just one compromised account can become a gateway for attackers to access data, disrupt operations, or commit fraud. Areas to pay close attention to include credential storage, the potential for malicious tampering with a bot’s logic, robust audit trails to track bot activity, and a strong governance framework that enforces strict access controls.
  • Upfront costs: RPA is often used in situations where APIs are unavailable. This means that implementation may come with complex configuration requirements, leading to higher costs for both startup and maintenance. One way around this is to choose cloud services with built-in RPA capabilities that deliver greater reach, collaboration, and functionality without complicated setup.

Types of RPA

There are two main types of RPA, attended and unattended. However, a third hybrid option is gaining popularity as it attempts to provide a balance between efficient automation and complex problem solving requiring human intervention. Let’s look at all three types.

  • Attended RPA
    In attended RPA, the bot is called via human interaction, making it an on-demand tool for use as needed. For example, an RPA bot can be part of a data analyst’s toolbox to run a label conversion process on a data set. Because the analyst wants to inspect the data in raw format first, it is not a fully automatic transformation. When the analyst finishes their inspection, they call the RPA code to automate the transformation part of the task.
  • Unattended RPA
    Unattended RPA allows for a process to be a fully automated part of the workflow. With unattended RPA, the bot is always on and is triggered immediately when related steps are hit during the workflow. An everyday example of basic unattended RPA comes from online retail, where an automated task might generate receipts formatted to send via email and text to a customer upon completing a purchase.
  • Hybrid RPA
    Hybrid RPA combines the benefits of both attended and unattended RPA strategies. In a hybrid situation, bots handle expected tasks and items that fall within specific boundaries and parameters. When outliers and potential problems appear, those items are flagged for human intervention. The reviewer must make a judgment call before moving the task forward in a workflow.

To demonstrate how hybrid RPA can optimize a workflow, take our example customer chatbot using hybrid RPA to optimize the process of authorizing returns. Unattended RPA handles return requests that stay within specific boundaries, such as purchase date, condition, and type of product. However, if the customer enters details that aren’t clearly delineated, the chatbot can flag the task for human intervention to review whether or not a return should be authorized. In this scenario, a large percentage of tasks are automated for maximum efficiency while still providing the option for a human to make a judgement call based on defined factors, like customer lifetime value or whether the product can be easily resold.

Common Misconceptions About RPA

While robotic process automation isn’t as front and center in the cultural lexicon as machine learning and artificial intelligence, it is a powerful tool that many companies depend on. In many ways, RPA, ML, and AI are symbiotic and often overlapping technologies. For IT teams, the key is knowing where to apply each and being aware of two common misconceptions about RPA.

  • RPA is often mistaken for AI decision-making. AI decision-making uses a combination of preset rules, training data, and learned reasoning. With AI, boundaries and processes are self-assessed for improvement and gradual evolution. RPA uses a process that is much more defined by rule-based workflows—reasoning and incremental improvements are not part of the equation. This adherence to workflows means that RPA, unassisted by AI, is not well suited to tasks that require interpretation, inference, and judgment.
  • RPA operates strictly on predefined rules. While RPA processes are often based on static, defined rules, this is not a strict requirement. RPA can also work by recording often repeated actions and then replicating them. For example, if a user continuously repeats steps in a photo editing program to transform the resolution of an image, an RPA tool can record, identify, and implement this process. However, this capability comes with limited adaptability. In this same example, if the software’s UX or menu options drastically change, the RPA script will likely be unable to adapt without human intervention.

RPA vs. AI: Understanding the Difference

The difference between RPA and AI can be analogous to the difference between a technician and an engineer. Both are important to the success of the operation, and both have technical understanding as part of their functions. However, each follows a different set of parameters and goals: A technician follows rules, executes steps, and observes boundaries to execute processes swiftly and accurately. An engineer can do the work of a technician but is able to handle exceptions and deviations and examine the process to see if it can be improved.

  • RPA automates repetitive tasks based on set rules that may be predefined or observed through repetition. The goal of RPA is to automate a repeatable, rule-based process, such as a set of menu clicks, to achieve a specific function. In many ways, RPA is akin to the functionality of a macro in an application like Excel, but RPA processes are more flexible, often easier to create, and more agile when it comes to exporting out of an application. However, RPAs face limitations in adaptability because of their strict adherence to process and rules.
  • AI learns from data and makes decisions based on insights. Like RPA, AI can start with predefined guidance or compile a direction set through observation (training). However, one of the key differences between RPA and AI is how AI uses that first step as merely a starting point; from initial guidance, AI evolves based on a feedback loop of learning and results. This allows AI to learn from patterns in data, thus integrating more nuanced decisions or adding options into a workflow without being explicitly guided to do so.

Combining RPA with AI for Intelligent Automation

What is intelligent automation? In a nutshell, it’s the integration of automation processes, such as RPA, with AI to maximize benefits from both. This combination allows for the rule-driven efficiency of automation to reduce workloads and manual effort while AI provides autonomous decisions on when to execute those functions. The following offers two examples of intelligent automation:

  • AI-driven data analysis feeding into RPA for automated reporting: RPA scripts can trigger automated generation of reports. What those reports entail, though, can be augmented by using AI-driven data analysis. By using AI to process and analyze data, RPA scripts can create more accurate reports with deeper insights based on real-time data. Using AI and RPA together in this fashion significantly reduces the time and steps involved on both ends of the workflow.
  • Using NLP (natural language processing) to handle unstructured data, which is then processed by RPA bots: When data comes in the form of emails, logs, or another text-based unstructured format, NLP can process and distill the input in ways that work with the application at hand. For example, if the goal is for intelligent automation to put together sentiment analysis from feedback forms, an AI using NLP can first process the text to categorize, tally, and flag highlights; from there, RPA can intake the structured review of the data for report generation.

Key Industries Using RPA

Automation via RPA can be applied broadly across functions and industries to reduce waste, improve performance, and increase accuracy. The following are just some of the ways industries are successfully integrating RPA into their workflows:

  • Finance: The finance industry generates massive flows of structured data, which is where RPA excels. RPA can turn many labor-intensive, high-volume tasks into near-instant automation. Think data entry, document verification, label formatting, and other tasks that use clearly defined rules, steps, and boundaries. In addition to boosting efficiency, using RPA increases accuracy as well by removing the potential for human error from manual data entry.
  • Healthcare: These organizations can apply RPA to both patient-facing and internal operations functions. For patients, automation of practical processes, such as appointment check-in, notifications of lab/scan results, and appointment reminders, can simplify and improve their overall experience. For providers, RPA can expedite consolidation of data across EHR formats and post-discharge management. For operations, RPA can cover the entire spectrum of needs, including shift schedule optimization and supply chain and inventory management.
  • Retail: Whether a seller is online, brick and mortar, or a hybrid, RPA can deliver vast improvements in operations. By using RPA as part of inventory and supply chain management, retailers can keep constant tabs on stock and shipments, allowing managers to make practical adjustments faster. In addition, RPA can automate many customer-facing functions, including processing return/refund requests, creating and sending invoices, and managing marketing emails and texts.

Use Cases of RPA

RPA can be applied across industries to automate business processes, whether for internal operations or customer-facing interactions. The ways organizations use RPA is nearly limitless—any repeatable process with defined steps is fair game. The following are some of the more popular cross-industry use cases of RPA:

  • Customer support: RPA-powered chatbots can handle many common and procedural customer support issues, from record checks to return/refund requests to processing customer feedback, as long as the steps involved are clearly defined and structured. For most organizations, the bulk of customer support tasks use standard workflows, and RPA can manage these so that human staff can focus on more complex issues.
  • HR: HR departments deal with wide-ranging processes that can be significantly optimized through automation. Nearly all aspects of the employee experience create engagements with repeatable HR processes, including initial onboarding, profile data entry, training workflows, and even final exit checklists. RPA functions can automate the bulk of these processes.
  • Software updates: RPAs can significantly lighten IT workloads by automating software update processes across the entire network. With RPAs, systems can check for available updates and execute installation in ways that minimize demands on employee resources and time.

RPA Implementation Challenges

While RPA creates significant opportunities to automate processes and improve organizational efficiency, organizations will likely face several common challenges. Fortunately, RPA is an established technology, and there are proactive strategies to tackle most hurdles.

  • Integration with legacy systems: Modern applications generally integrate smoothly with RPA. However, legacy systems—particularly those run on-premises—often pose challenges around inconsistent connection points and varied IT environments. IT staff can proactively run compatibility audits for both applications and RPA systems and, as needed, leverage middleware or custom connectors to bridge the gap between the RPA platform and older or custom applications.
  • Employee resistance and change management: Introduction of automation can create anxiety among employees who fear their jobs are at risk, leading to resistance that can undermine the project. Effective change management is crucial. Leadership should transparently communicate the goals of RPA, emphasizing how it augments human capabilities by taking on repetitive work. This reframes automation as a tool that allows employees to focus on higher-value tasks requiring creativity and critical thinking versus technology meant to displace them.
  • Managing process change and bot maintenance: RPA bots are configured for specific workflows. If an underlying application or process changes—even slightly, like moving a button—the automation can break. This brittleness necessitates ongoing maintenance. Organizations must establish a governance plan for regular bot monitoring and script updates to ensure accuracy and prevent costly downtime.

Best Practices for Successful RPA Implementation

While RPA can be a powerful tool to optimize workflows, a few planning and integration best practices will maximize success. In general, RPA adoption will begin with identifying repeatable and stable tasks within an organization’s workflows. Once some target processes have been identified, there are steps that will help make automation efforts a success.

  1. Integrate configurability: Successful RPA developers minimize the amount of code that locks in specific variables and settings, such as file paths or recipient email addresses. Instead, those should be made easily configurable by process owners so that changes do not require developers to rework code and scripts.
  2. Focus on reusability: Many steps in RPA workflows are generic and repeatable, such as logins, email notifications, and format changes. By building modularity into RPA coding, those pieces can be exported as the foundation for other functions. Doing so both speeds up future development of RPA processes while creating organizational standards for repeatability and access.
  3. Expect and allow for errors: Because RPAs are rule-based, unexpected deviations or application changes can derail a process. While some errors are inevitably show-stoppers, others are manageable with modern RPA software—if teams build in appropriate responses for a range of common and expected glitches, such as server timeouts. Steps might include skipping to the next item in the queue while logging details about the error for human review.
  4. Plan for AI integration: With AI on board, RPA can handle more complex, unstructured data and better understand context and manage exceptions. This unlocks the ability to automate more end-to-end processes, such as those that involve processing a variety of invoice formats or interpreting customer sentiment in emails.

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We’re at the cusp of a new era of RPA-driven productivity thanks to AI. While RPA has always excelled at automating repetitive, structured tasks by mimicking human actions, AI-infused RPA can do much more. The challenge for companies now is to think bigger about where to apply RPA. Consider pilot projects to demonstrate the value of today’s RPA technology, bring departmental leadership on board, and plan for the technology to be a key enabler of agentic AI.

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RPA FAQs

Can RPA be used to automate unstructured data tasks?

While RPA works best with set rules across structured data, you can expand its use cases. However, to work with unstructured data—text, video, images—you’ll require other tools to process and output structured definitions for RPA systems to use. For example, NLP models can process unstructured text data to assign categories and tags, which RPA can then use for generating a report. Similarly, an image of a document can use optical character recognition (OCR) to convert the document’s table into structured data, which then becomes part of RPA analysis.

What are the main considerations when scaling RPA across an enterprise?

RPA can scale throughout an enterprise, but doing so requires thoughtful execution. Many variables determine RPA’s success, including the types of RPA tools purchased, volume of automation opportunities, the interconnectivity of existing data, processing resources, and ability to monitor bot maintenance. To start, enterprises should perform an organizationwide analysis of processes for automation opportunities, then align that with their company’s other IT tools and resources. On a more micro scale, RPA development teams should keep modularity, reusability, and flexible settings in mind. This allows for exporting of RPA scripts while easing assessments of resource use, integration, and overall scalability.

How can RPA be integrated with other automation technologies?

RPA can integrate with other automation technologies, a combination often referred to as intelligent automation, in many different ways. With agentic AI, RPA can be a tool in the agent’s toolbox for achieving a goal. In workflows, RPA can call on an AI model for a decision to a complex or inconclusive input prior to moving forward. In other use cases, AI models can perform analytics or unstructured data analysis before feeding into the more structured RPA workflow for report generation.

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