What Is RPA (Robotic Process Automation)?

Employees spend one-third of their time on routine administrative tasks rather than the skilled work they were hired to do. Not surprisingly, 64% of these workers experience burnout once a month or more. Robotic process automation (RPA) can help address this imbalance.

RPA uses software-based virtual robots to automate labor-intensive tasks. It's a key part of the growing digital labor market. Digital agents use RPA to gather the data they need for decisions. By combining process automation with agentic AI frameworks, your teams have more time to spend on highly valuable tasks.

RPA uses software-based robots to automate business processes and reduce error risks. But they're not robots in the physical sense of the word. They're considered robotic because they can be programmed to take specific actions. Common actions include completing forms, extracting data, and moving files.

Consider a law firm carrying out data discovery for a case. Rather than manually sorting through thousands of documents across hundreds of data sources. Instead, the firm can automate this process, saving both time and money.

Process automation can also improve the performance of digital workers. As a result, RPA is sometimes known as "AI automation" for its role in enabling AI-based processes.

This raises an important question: Is RPA just another form of AI? Not exactly.

Artificial intelligence (AI) refers to technologies like machine learning (ML) that mimic human decision-making. Accurate results from AI, however, require high-quality data. If databases contain duplicate entries or errors, results may be inaccurate.

RPA is rules-based software that automates resource-heavy tasks such as collecting and curating data, which sets up AI tools for success. Together, RPA and AI can automate everything from data collection, evaluation, and entry to decision-making.

The core of RPA is simple: Software-based robots mimic common human actions such as completing tables, filling out forms, or using applications.

RPA isn't a monolithic process. Instead, it depends on three components: User interface (UI) interactions, application programming interfaces (APIs), and task scripting.

• UI interactions: UI interactions let users define and apply a set of instructions. For example, workforce automation tools might be programmed to collect and report conditions relevant to regulatory compliance. This could include where data is located, how data is stored, and what its chain of custody looks like.
• APIs: APIs connect RPA solutions to other tools and technologies. These may include enterprise resource planning (ERP) solutions, customer relationship management (CRM) tools, and agentic AI frameworks.
• Scripting to automate tasks: RPAs can be scripted using common programming languages such as Python, C++, and Java. They can also be implemented via low-code, click-based drag and drop tools. Alternatively, there are also pre-scripted solutions.

It's one thing to discuss the theoretical application of RPA, but what does this look like in practice? Several conditions make business processes a good fit for RPA. First are large data volumes that are regularly updated, such as those in spreadsheets. Next is data stored in multiple locations, such as in the cloud and on local storage servers. Finally, RPA offers a way to bridge the gap between the current generation and legacy IT systems.

Let's explore three examples in more detail:

• Spreadsheet data entry: Spreadsheets remain popular for financial data tracking and management but can be cumbersome and time-consuming to update. Using RPA, businesses can automate spreadsheet data reading, writing, and recalculation and create datasets directly from these spreadsheets .
• Database management: Many businesses rely on multiple databases on-site, in co-located data centers, and in the cloud. The result is a fragmented data landscape that requires substantive staff effort to track and validate. RPA is ideally suited for database management, allowing enterprises to collect and curate data from multiple sources.
• Legacy system integration: Legacy systems may not work with modern APIs but still contain critical operational data. Because these solutions automate manual processes typically handled by humans, they're capable of finding and extracting key data from disparate and disconnected solutions.

RPA reduces the time required to carry out business tasks. But this isn't the only benefit for your business. In combination with digital agents and human oversight, robotic process automation offers multiple benefits, including:

Reduction in the time and effort required to complete labor-intensive processes means less money spent on these processes. Beyond basic cost savings, companies also see ROI from RPA as staff have more time to drive business value rather than collect data.

RPA solutions don't deviate from programmed processes. This makes them ideal for tasks that require a combination of repetitive actions and high accuracy. Improved accuracy is also essential for compliance in data handling and security.

Robotic process automation makes it easier for a digital workforce to get the data it needs from legacy CRM solutions and other tools. Consider a customer looking for a tracking update on their package. RPA can direct database tools to find relevant tracking information and relay this data to AI agents, who then inform the customer.

RPA workforce automation tools can help improve the human work experience. Instead of being tasked with data entry, employees can spend their time focused on more strategic and creative work.

It also opens the door for staff reskilling and upskilling. With automated scripts handling manual tasks, businesses can bring employees up to speed with training in responsible AI use or offer more in-depth training in areas such as AI prompt creation. This can lead to internal advancement and promotions, which keeps staff engaged.

The result is threefold: Enhanced productivity, improved morale, and increased opportunities.

RPA can mimic repetitive, predefined human actions while AI can act autonomously to decide the next best action on its own. AI underpins the next generation of digital labor — autonomous agents capable of analyzing data, making decisions, and incorporating generative AI and conversational AI.

RPA supports AI and vice versa. Using RPA, digital workers can automate repetitive tasks like data extraction and web scrapping by having bots mimic user keystrokes and mouse clicks. Using AI, teams can quickly build and deploy RPA bots that tackle labor-intensive tasks.

They can work together in this way because robotic process automation is inherently process-driven. Using predefined workflows, RPA handles repetitive, rules-based tasks, and AI excels at creating these workflows.

Any digital labor platform that uses agentic systems needs access to current databases, legacy apps, and incoming datasets. Here's what that looks like in practice.

Robotic process automation tools can extract invoice data and input this data into legacy or homegrown invoicing systems.

This underpins a larger quote-to-cash (QTC) sales process that leverages AI agents to create offers, build proposals, manage contracts, and collect payment. While AI handles the heavy lifting, AI for sales isn't possible without the underlying invoice data collected by RPA.

More data can provide a path for better customer service. Collecting and inputting this data, however, is often time- and resource-intensive. This is because customer data comes in multiple formats — unstructured data — from multiple sources, and is often stored in homegrown databases or spreadsheets. It also helps companies collect and curate this data regardless of origin or location.

Digital labor tools can then leverage this collected data as part of a larger AI for service process that prioritizes personalized interactions and recommendations.

Accurate pricing and product information are required to create effective marketing campaigns. Using RPA, teams can extract pricing data from web pages and store it as a shared resource.

Used as part of a large AI for marketing strategy, this data helps teams build targeted ad campaigns.

Returns remain a cumbersome process for commerce teams. If data isn't regularly and accurately updated across legacy management and procurement systems, companies may encounter frustrated customers or struggle with inaccurate inventory counts. Using RPA to collect and update order information enables AI for commerce by supplying digital agents with the data they need to process returns.

While RPA offers significant advantages for organizations, it also comes with several potential challenges and key considerations. These include identifying processes for workforce automation, managing data quality and integration, navigating ethical governance concerns, and addressing scalability and maintenance needs. Here's a look at each in more detail.

Not every process is a good fit for automation. Depending on the type of data entered, the application used, and the intended outcome, RPA may not be the best fit. Businesses can identify potential process candidates by creating criteria for selecting suitable processes. Common criteria include:

• Structured data sets: Structured data sets are ideal for these types of solutions because they are quantifiable and repeatable. Processes that deal with qualitative data sets that lack consistent formatting are not ideal candidates.
• Software unreachable by APIs: Legacy solutions, purpose-built tools, and those created in-house may not support API connections. RPA, meanwhile, doesn't require external interlinking and instead automates standard operations.
• Labor-intensive processes: These processes can be streamlined using RPA. Consider the collection and entry of financial data that changes daily or hourly. While staff tasked with this data entry task might be able to keep pace, they would have little time left for other projects. Large-scale data collection tasks are much improved by RPA.
• Error-prone processes: Data-heavy processes are often error-prone. Despite best efforts, employees can’t match the accuracy and repeatability of automated​​ tools in data collection and entry. Robotic process automation can both improve the speed of process completion and reduce the risk of data entry errors.

Once you've identified process criteria, you need to find and isolate these processes. Businesses often benefit from using process mapping tools that create visual representations of current workflows and process dependencies. This helps identify where processes exist in your networks, how they're connected to applications and services, and what data they use.

Effective RPA depends on high-quality data and the ability to integrate this data across legacy systems and modern applications. In practice, this poses challenges including:

• Handling inconsistent, incomplete, or outdated data: These tools excel at capturing and inputting data. They aren't, however, capable of making decisions about the quality and consistency of this data. Consider a company that uses a combination of on-site and cloud-based resources to store key data. If these databases contain duplicate information, RPA frameworks will record and enter all instances. This same concern is repeated across incomplete or outdated information — solutions will seek out target data and process it as directed. Handling inconsistent, incomplete, or outdated data requires more in-depth scripting that identifies common features of unusable data. For example, teams can set RPA to ignore data older than a specified date or only record data if it satisfies specific conditions for completeness and accuracy.
• Integrating with legacy systems and modern architectures: Integration poses another potential challenge. This is because legacy systems and modern architectures are distinctly different; what works for one may not work for another. As a result, there's no one-size-fits-all approach to RPAs. While they're a viable way to capture legacy data from systems that don't support APIs, the architecture of these systems is far different than their more modern counterparts. To ensure consistent integration, RPA bots must align with system limitations rather than data expectations.

Robotic process automation can also introduce concerns around ethical data use. For example, if companies lack visibility into what data is being collected and how it's being used, the effect could be compliance or legal challenges.

Organizations must navigate concerns such as:

• Ensuring transparency and accountability: In the event of audits or compliance evaluations, organizations must be able to show where data was captured, how it was obtained, and what changes it has undergone. Failure to demonstrate this transparency could result in fines or sanctions. Ensuring transparency starts with RPA accessibility. Whether built in-house or provided by a third party, teams must have access to RPA infrastructure. Accountability also plays a role. In practice, this means assigning staff — typically a manager or C-suite member — to oversee creation, deployment, and integration. This accountability helps ensure that transparency remains a priority.
• Managing privacy and security risks: RPA tools may also introduce potential privacy and security risks. Consider a process automation tool designed to retrieve and enter customer data. If this data is not properly anonymized, or if these processes link anonymous data to identifiable source information, companies could inadvertently put customer data at risk. Similarly, if process automation tools aren't bound by the same security requirements as other IT services, they may introduce the possibility of outsider attacks or insider threats.

To ensure tools keep pace with growing data volumes and remain reliable, two considerations are critical:

• Developing strategies to scale RPA programs: These processes are scalable but not infinite. Solutions built in-house can struggle to handle cloud databases as data volumes rise, and those that rely on local server stacks may experience performance issues as data demands increase. To combat these challenges, your company needs a plan. This might include purchasing additional storage and server space or extending capacity into the cloud. There's no "right" choice here — what works for one company may not work for another — but it's critical to have a plan in place.
• Providing continuous support for updates and bots: It's important to provide continuous support for software updates and RPA functions. If you're using a third-party or managed service, this support often comes standard. If not, you need to regularly evaluate performance and seek out any available updates.

Deployment practices make or break the success of robotic process automation. These best practices can help streamline the process:

Stakeholder engagement is the starting point for any robotic process automation deployment. First, assign ownership of the project to a relevant C-suite member. The buck stops here — all aspects of RPA integration go through this individual. Next, bring in IT teams, front-line users, and any other teams that will interact with RPA, and provide an outline of key objectives. Communicate potential benefits and discuss likely timelines.

Last — and perhaps most importantly — is taking the time to address concerns. Ask teams what they're worried about, where they see possible roadblocks, and how they believe these problems can be addressed. Skip this step at your peril; failure to engage stakeholders can hamper widespread adoption.

Defined processes and clear policies are critical to ensure the consistent application of RPA. Start by creating a center of excellence (CoE) that defines best practices, identifies possible missteps, and provides actionable advice to address issues.

Next, take the time to develop data governance models and best practices. Governance can be simple — for example, data should always be encrypted and access should be limited — or it may be complex, such as alignment with evolving compliance legislation such as GDPR, CCPA, and guidance from FINRA. Oversight is also critical. While RPA provides the foundation for advanced workforce automation, it's not self-correcting or self-evaluating. Regular evaluation and testing are critical to ensure accurate output.

Not all vendors are created equal. When selecting a partner, consider:

• Technical capabilities and user-friendliness: RPA tools should combine in-depth technical capabilities and user-friendly features. MuleSoft RPA is an example — the solution can help accelerate delivery with end-to-end automation for every team.
• Pricing models and support services: It's also important to find solutions with transparent pricing models and reliable support services. Hidden costs can add up if vendors aren't upfront about pricing, and a lack of support can leave your business struggling to make the most of RPA.
• AI integration and security features: RPA underpins AI efficacy. As a result, organizations should look for vendors that include seamless integration with AI frameworks such as large language models (LLMs) and natural language processing (NLP). This provides a balance of speed and safety.

While it's tempting to roll out RPA at scale once tools are in hand, it's better to start with pilot projects. Given the complex nature of IT environments, it's impossible to know the impact of new tools until they're deployed. If unexpected errors happen, addressing small issues is easier than handling widespread failures.

Aim to identify the most high-impact, low-risk projects to automate. Subfeatures, such as process mining , can help pinpoint potential process candidates. Once RPA is up and running, measure its impact with metrics — how much time has been saved? Is the data accurate and complete? What feedback do users have about the process?

RPA isn't a static solution. As data volumes increase and data sources evolve, tools must keep pace. To monitor and optimize performance, start by measuring the efficacy over time. If speed and/or accuracy decline, perform a root-cause analysis to identify the primary problem, rather than treating the symptoms.

Robotic process automation lets your business gather more data, more quickly, and do so from any source. Unlike its AI or cloud-driven counterparts, RPA is rooted in simple scripting, making it ideal for applications that aren't inherently connected.

Properly applied, it can transform business operations. Connecting with legacy applications means RPA can significantly expand data availability. By sorting through mountains of data, RPA can free up time for staff to take on new projects or develop new initiatives. Plus, by bringing more data to the surface more quickly, RPA sets the stage for improved AI-driven decision-making.

AI solutions such as Agentforce are the brains of digital labor — RPA is the muscle. It does the heavy lifting by collecting and returning data from multiple sources to inform AI actions. Put simply, the better your RPA, the smarter your AI.

Realizing RPA's potential requires a measured and targeted approach. Businesses are best served by user-friendly, well-supported, and secure solutions — as a result, careful evaluation of available options is critical. Function also outpaces form. While it's tempting to deploy business-wide, you're better served by bringing in stakeholders early, addressing potential concerns, and starting with small-scale deployments.