RPA: Robotic Process Automation in organizations

Robotic Process Automation (RPA) has become a strategic element of digital transformation in organizations. Due to its ability to automate repetitive and rule-based tasks quickly, accurately, and in a scalable manner, it has been widely adopted around the world. The global RPA market was valued at approximately $22.8 billion in 2024 and is expected to grow to over $211 billion by 2034.

In a highly competitive environment with pressure on resources, understanding what RPA is has become essential. This technology allows processes to be modernized and accelerates the journey towards digitization.

What is RPA and how does it work?

RPA, or Robotic Process Automation, is a technology that uses software robots to perform repetitive tasks in computer systems. Its adoption has been growing due to its ability to increase efficiency, reduce errors, and free up employees’ time. Simple to implement and compatible with existing systems, RPA stands out from other forms of automation due to its non-invasive approach.

Meaning of RPA

Robotic Process Automation is a technology that allows the automation of repetitive, rule-based tasks performed in digital systems using software robots that mimic human actions. These robots interact with system interfaces in the same way a human user would, but with greater speed, consistency, and without errors.

RPA is widely used to handle administrative tasks such as filling out forms, extracting and inserting data between applications, performing automatic validations, generating reports, or sending notifications. This technology has gained prominence across various industries, contributing to increased operational efficiency and cost reduction.

Unlike traditional automation, which requires deep integration with systems, RPA works at the user interface level, making its implementation faster, less invasive, and easily scalable.

Difference between RPA and other automation technologies

It is common to confuse RPA with other automation technologies, such as workflow automation, custom scripts, or integration tools like APIs (Application Programming Interfaces). However, there are fundamental differences.

Traditional automation is based on direct integrations with systems, requiring technical development and changes to existing infrastructure. This type of approach tends to be less flexible and more time-consuming to implement. Workflow systems, on the other hand, coordinate activities between people and systems based on defined rules, but do not always perform tasks automatically—they often limit themselves to orchestrating the process.

RPA, however, mimics the behavior of a human user, interacting with the graphical interface of applications. This enables process automation without the need to modify the source systems. This feature makes it particularly appealing for organizations with legacy systems or applications that do not communicate with one another.

Additionally, RPA can be enhanced with technologies such as artificial intelligence (AI), machine learning, and natural language processing.

How software robots interact with systems

Software robots (bots) created through RPA platforms perform specific actions in business applications, similar to human users. These actions include:

• Opening applications and navigating menus.
• Filling out form fields.
• Copying and pasting data between systems.
• Reading files, emails, or PDFs.
• Validating information based on predefined rules.
• Performing calculations or data transformations.
• Generating reports and emailing them.

These robots operate based on visual scripts or flowcharts created by business analysts or RPA developers, using intuitive interfaces provided by RPA tools. The automation process can be “attended”, where the robot is triggered by a human user, or “unattended”, where the robot operates autonomously according to predefined schedules or triggers.

This flexibility allows RPA to interact with any system: ERP, CRM, web applications, databases, or both structured and unstructured documents.

Benefits of process automation with RPA

The adoption of Robotic Process Automation is transforming the way organizations operate. By automating repetitive and manual tasks, RPA delivers immediate gains across various areas, fostering excellence in industrial support operations and services.

Increased productivity and cost reduction

By handling manual, repetitive, and time-consuming tasks, RPA robots free up employees to focus on more analytical and strategic activities, significantly boosting team productivity. Processes that once took hours to complete can now be done in minutes or even seconds, without interruptions, 24 hours a day.

This efficiency gain allows organizations to reduce operational costs, avoid unnecessary hires, and scale processes without proportionally increasing human resources. Over the medium term, the investment in RPA typically results in a fast and measurable return.

Error reduction and improved quality

One of the key benefits of RPA is its ability to perform tasks with high precision and reliability, following predefined instructions without deviation. This drastically reduces errors, such as incorrect data entry, calculation failures, or missed steps.

With fewer errors, processes become more robust, and the products or services delivered to customers are more consistent and of higher quality. Furthermore, RPA facilitates traceability of operations, supporting compliance with internal standards and regulatory requirements.

Improved customer service

With the intelligent automation of internal processes, customer response times are shortened, and requests are handled more quickly and consistently. For example, tasks such as opening requests, sending confirmations, analyzing data, or routing complaints can be automated, all with greater speed and without human errors.

Additionally, by freeing teams from administrative tasks, RPA enables them to focus more on personalized customer service, contributing to a more satisfying and differentiated customer experience. In increasingly competitive markets, this advantage can be a critical factor in customer retention.

RPA Implementation: Steps and best practices

Successfully adopting Robotic Process Automation requires more than just choosing a technology. To create real value, it’s crucial to adopt a structured approach—beginning with the accurate identification of processes, followed by the right platform selection, and culminating in seamless integration and scalability of the solution.

Identifying processes for automation

The first step in implementing RPA is selecting processes that are suitable for automation. Not all processes are appropriate. Priority should be given to those that have the following characteristics:

• Repetitive, rule-based, with few exceptions.
• High volume and frequent or with predefined triggers.
• Well-defined inputs and outputs.
• Time-consuming or prone to manual errors.

To support this analysis, it is recommended to use tools like process mapping and prioritization matrices.
A good practice is to start with a simple pilot project that delivers visible impact to demonstrate the value of RPA and facilitate internal acceptance.

Choosing the RPA platform

Once the processes to be automated are defined, the next step is to choose the RPA software that best fits the organization’s needs. Some of the most relevant criteria include:

• Compatibility with existing systems.
• Ease of use and programming (low-code/no-code).
• Management, monitoring, and security features.
• Integration capabilities with AI and machine learning.
• Total cost of ownership and licensing model.

Market-leading platforms include UiPath, Automation Anywhere, and Microsoft Power Automate, each with strengths in different contexts.

It is advisable to conduct proof of concept trials before making the final selection, involving IT teams and business users to ensure alignment with operational goals.

Integration with existing systems and scalability

One of the advantages of RPA is its ability to operate on legacy systems through the user interface. However, to ensure the reliability of automation, it is crucial to ensure good integration with existing systems, even if that integration is indirect. Some best practices include:

• Creating a stable technical environment for robot execution (infrastructure, VMs – Virtual Machines, permissions).
• Avoiding frequent changes in the interfaces of automated systems.
• Documenting automation flows and validating exceptions.

As the organization matures in RPA, planning for scalability becomes essential. This includes:

• Establishing a Center of Excellence to manage automation.
• Defining development, testing, and maintenance standards for bots.
• Monitoring performance indicators.
•  Integrating RPA with other technologies (AI, OCR, chatbots).

Well-managed scalability allows the organization to maximize the benefits of RPA and make automation an integral part of its digital transformation strategy.

Challenges and limitations of automation with RPA

Despite the clear advantages of RPA, its implementation is not without challenges. Many organizations face obstacles that could jeopardize the success of automation if not correctly anticipated and managed.

Change management and employee engagement

One of the main challenges in adopting RPA is gaining acceptance from teams. Automating business processes can be perceived as a threat to job security. If this perception is not properly addressed, it can lead to resistance to change.

To mitigate this risk, it is essential to:

• Communicate transparently the goals of automation, emphasizing that the purpose is to enhance the value of human work.
• Involve employees from the beginning of the process, especially in identifying tasks to automate.
• Provide training and reskilling to allow professionals to transition into higher-value roles.
• Celebrate the achievements gained through RPA, highlighting team contributions.

Change management should be seen as a pillar of the RPA strategy, not as an accessory element.

Difficulty in selecting processes and structuring data

One of the main barriers to effective RPA implementation is the difficulty in identifying, mapping, and optimizing processes in a structured way. Many organizations lack clear visibility into how tasks are performed, where bottlenecks or redundancies exist, or which processes are priorities for automation.

Furthermore, even when processes are correctly identified, automation is only effective if the data is well-structured. However, much of the information used in daily business operations is in unstructured formats, making integration with RPA bots difficult.

To overcome these challenges, it is recommended to:

• Map processes using tools like SIPOC and process mapping, carried out by multidisciplinary teams, to understand actual workflows and identify inefficiencies.
• Involve multidisciplinary teams in analyzing and improving processes before automation to ensure that RPA is applied to efficient, sustainable workflows aligned with business goals.
• Implement document automation tools that can extract, organize, and deliver relevant data from unstructured sources like emails, PDFs, or scanned images, expanding the scope of automation.

Automating an inefficient process or poorly structured data only perpetuates errors and creates technical dependencies. Therefore, process discovery and optimization, as well as data structuring, should be key steps in preparing for any RPA initiative.

Maintenance, monitoring, and security

After implementing the robots, it is crucial to ensure they continue to function correctly over time. However, bots may fail if there are changes to system interfaces (e.g., moved fields, renamed buttons) or changes to business processes not reflected in the automation. For this reason, it is essential to implement mechanisms for:

• Continuous monitoring of bot performance.
• Automatic alerts in case of failure.
• Preventive maintenance and regular testing.

At the same time, information security must be a priority. Bots operate with access to sensitive data, so it is necessary to:

• Define secure access profiles and credentials.
• Log and audit all actions performed.
• Ensure RPA complies with cybersecurity and privacy standards, such as GDPR.

Organizations that do not rigorously address these issues risk losing control over their automated processes or even experiencing security incidents.

RPA use cases across different sectors

Companies across various sectors have widely adopted Robotic Process Automation technology. Below are use cases of RPA in different areas and industries, with real examples of companies leveraging this solution.

RPA in the financial sector

In the financial sector, many tasks are repetitive, rule-based, and high-volume, making them ideal candidates for RPA. It’s no surprise that financial functions lead in the adoption of this technology. It is estimated that about 35% of all RPA use cases occur in financial functions. Standard processes automated with RPA in this sector include:

• Accounts payable and receivable: issuing and processing invoices, payments to suppliers, and collections from customers.
• Reconciliations and financial closing: bank account reconciliation, verification of accounting records, and monthly/yearly account closing.
• Client onboarding and compliance: opening new bank accounts, KYC (Know Your Customer) verification, and other regulatory compliance checks.
• Reporting and audits: generating financial reports, extracting and aggregating data for auditing and reporting, ensuring accuracy and speed.

Automating these processes enables teams to reduce errors, accelerate financial cycles, and free up time for more analytical activities.

RPA in human resources

In the Human Resources department, RPA allows the automation of administrative and repetitive tasks, freeing up teams to focus on more strategic roles. Key applications include:

• Payroll processing and management of vacations and absences.
• Employee onboarding.
• Updating HR system data.
• Preparing contractual documentation.

Large companies have been implementing RPA in their employee onboarding processes to speed up and standardize this critical phase. For example, before the first day, new hires automatically receive checklists and informational materials sent by software robots. At the same time, managers are notified and receive specific instructions to ensure effective onboarding. This approach has significantly reduced administrative time for HR teams and improved the engagement and satisfaction levels of new hires from day one.

RPA in customer service

The combination of RPA with Artificial Intelligence enables quick responses to simple requests and forwards more complex issues to human operators. Key applications include:

• Automating responses to frequent requests.
• Updating customer data across systems.
• Routing cases based on predefined rules.
• Automatically opening tickets and support requests.
• Integrating with chatbots for immediate action execution.

Automation in these processes significantly improves the speed and consistency of customer service, reducing response time and increasing customer satisfaction.

RPA in Global Business Services (GBS)

The adoption of technologies such as Robotic Process Automation has strongly driven the transformation of Global Business Services (GBS). In Global Business Services consultancy projects, many organizations are rethinking their operational models, evolving GBS from transactional centers to true centers of excellence and strategic business partners. RPA plays an essential role in automating administrative tasks. Key RPA applications in GBS include:

• Data extraction, validation, and transfer between ERP and CRM systems.
• Automated processing of internal requests (e.g., procurement, HR, or IT).
• Financial reconciliations and cross-compliance control.
• Generating and distributing performance reports by unit or geography.
• Automating the internal and external customer support cycle.

This technology accelerates the evolution of corporate centers, freeing up resources for analytical and decision-support activities, and contributing to improved service quality to various business units. Combined with AI and workflow platforms, RPA is a key element in building more agile and digital GBS.

RPA in the public sector

The adoption of RPA in the public sector has been growing, driven by the need to modernize services, increase administrative efficiency, and better meet citizen expectations. Process automation enables human resources to focus on tasks with higher public value, significantly contributing to improvement initiatives in the public sector.

Key applications include:

• Processing licensing requests, subsidies, and social benefits.
• Automatically issuing certificates and notifications.
• Updating and reconciling data between government systems.
• Generating statistical and administrative reports.
• Managing human resources and processing public sector payroll.

In municipal management, RPA has been used to automate tasks such as recording occurrences, validating tax and urban data, and managing public procurement processes.

RPA represents an opportunity to make public administration more digital, efficient, and results-oriented, strengthening societal trust in institutions.

RPA in industry and logistics

In the industry and logistics sectors, RPA is used to optimize support functions, ensuring greater efficiency and system integration. Key applications include:

• Real-time stock updates.
• Order processing and invoicing.
• Automating performance reports.
• Integration between production systems and ERP.

By automating repetitive operational tasks, industrial and logistics companies can achieve excellence in support functions, improving efficiency, reducing errors, and ensuring greater reliability in managing their processes.

RPA in healthcare services

In healthcare services, software robots help manage administrative tasks, allowing professionals to focus on patient care. Key applications include:

• Appointment and exam scheduling.
• Patient data registration and validation.
• Insurance processing and reimbursement requests.
• Automatically sending results and reports.

Large hospitals have implemented RPA to automate critical administrative tasks, such as verifying health insurance and hospital billing. Using specialized platforms, these institutions were able to deploy software robots in just a few weeks, streamlining processes and significantly reducing response times. In addition to operational improvements, automation has generated substantial financial gains. This approach has also improved patient experience and freed teams to focus on higher-value activities in care delivery.

The future of RPA automation in businesses

With technological advancements and the increasing pressure to achieve efficiency and agility, the future of automation lies in greater integration with other technologies, a more strategic vision, and the continuous development of team capabilities.

Trends in hyperautomation and generative AI

One of the emerging trends is the concept of hyperautomation, which combines RPA with advanced technologies such as artificial intelligence, machine learning, process mining, and intelligent document processing (IDP). This approach allows for the automation of not only simple, structured tasks but also more complex processes involving decision-making, unstructured data, and adaptation to variable contexts.

By integrating these technologies, organizations can interpret natural language, extract data from documents such as contracts, invoices, or medical reports, and automatically execute the necessary actions within business systems.

Generative AI, for example, can generate content, summarize information, or support customer service, while RPA acts as the execution element for the defined processes. This technological convergence significantly expands the potential of automation, promoting scalability, autonomy, and intelligence across the organization.

Team training and development of digital skills

The long-term success of RPA largely depends on the preparation of teams. Beyond implementing technology, it is essential to empower employees to identify automation opportunities, understand processes, and collaborate with technical teams in designing effective solutions.

Training in low-code tools and basic automation concepts enables professionals from various areas to participate actively in the digital transformation. At the same time, the role of IT and governance teams must be reinforced to ensure the security, scalability, and quality of implementations.

Sustainable automation requires a combination of robust technology and skilled talent. Investing in the development of digital skills becomes a critical factor for RPA to evolve from a one-off initiative into a strategic capability within the organization.

Still have questions about RPA?

How do RPA and AI (Artificial Intelligence) complement each other?

Although RPA and Artificial Intelligence (AI) are distinct technologies, their combination creates much more powerful solutions, capable of automating not only repetitive tasks but also more complex and dynamic processes.

RPA is ideal for executing tasks based on fixed, structured rules. AI, on the other hand, can handle unstructured data, interpret natural language, recognize patterns, or make decisions based on predictive analytics. When integrated, these technologies complement each other: AI analyzes, interprets, or decides, while RPA executes the corresponding action within business systems.

Is programming required to work with RPA?

Many RPA platforms adopt the low-code or no-code model, allowing non-technical professionals to create and maintain simple automations. However, for more complex or business-critical processes, it is advisable to involve technical profiles, such as developers or IT specialists.

Additionally, during the initial design phase of the solution, the support of specialized consulting can be crucial in identifying the right processes, ensuring technical feasibility, and aligning automation with the organization’s strategic objectives. This initial guidance helps avoid common mistakes and accelerates the return on investment in RPA.

How long does it take to implement an RPA solution?

It depends on the complexity of the process and the maturity of the organization. Simple projects can be implemented in a few weeks, while more complex initiatives require a diagnostic phase, development, testing, change management, and post-implementation monitoring.