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- Robotic Process Automation (RPA) uses software bots to handle repetitive, rule-based tasks on computers, mimicking how humans click, type, and move through systems to complete digital work.
- It’s best suited for high-volume, structured tasks like data entry, payroll processing, or monitoring systems, but struggles with unstructured data, complex decision-making, and tasks that require human judgment.
- Different types of RPA include unattended bots that run automatically, attended bots that help humans in real-time, hybrid models that mix both approaches, and intelligent RPA that combines automation with AI for smarter actions.
While its popularity is surging, robotic process automation (RPA) has been transforming business processes for years.
RPA is often seen as the perfect use case for AI integration or chatbots in business: the automation of repetitive, mundane tasks.
In this overview, we’ll explain the ins and outs of RPA, including the types of RPA, the tasks it’s best suited for, and how to deploy an RPA solution.
What is RPA?
Robotic process automation (RPA) is a subset of business process automation. It uses software robots or AI agents to automate repetitive, logic-based, human-computer tasks.
Using a combination of automation, computer vision, and machine learning, RPA software is used to automate any rules-based tasks that take place on a computer.
Which tasks can RPA automate?
RPA is best suited to automate repetitive, rule-based tasks that involve structured digital data.
They can be triggered by a human – like an employee instructing an RPA to categorize and file new data – or automatically triggered.
When should I use RPA?
With the rapid rise of AI and AI-adjacent solutions, it can be difficult to know which type of solution is best for an organization’s needs.
There are many tasks that can be automated with RPA, but depending on the level of complexity, it may be wise to choose a more advanced solution.
When to use RPA
RPA is best suited for automating repetitive tasks, integrating legacy systems, handling structured data, and any high-volume task that requires minimal decision-making.
These strengths mean a business should use RPA if they want to automate invoice processing, record reconciliation, navigating through an old mainframe, or processing customer orders.
When to use a different AI solution
There are plenty of tasks that RPA can’t handle well, including:
- Handling unstructured data
- Personalized interactions
- Predictive analytics
- Decision-making
- Continuos learning
So if an organization is looking to use an AI solution to categorize and automatically respond to customer emails, a more appropriate solution would be natural language processing software or conversational AI.
If a business wants an AI solution to predict trends, they should choose a machine learning solution. If they want a solution that interacts with customers automatically, they should opt for an AI chatbot over RPA.
RPA Use Cases
RPA software is widely used – it can applied to any repetitive, logic-based digital task. However, RPA is a better investment when there’s a high volume of these tasks to complete.
Here are a few of the most common applications of RPA solutions:
Price and system monitoring
RPA is ideal for automating the continuous monitoring of prices and system performance. Bots can track pricing changes across platforms, ensuring a company’s pricing remains competitive and up-to-date.
For system monitoring, RPA can check for performance issues and send alerts for any detected anomalies. These types of RPA bots are often running continuously in the background of a software or system.
Payroll processing
Payroll and other forms of organizational accounting are often paid out regularly, making them an easy use case for RPA.
Data management
Most applications of RPA fall under the umbrella term of ‘data management’. Across industries, common RPA data management tasks include:
- Data entry
- Data extraction
- Data validation
- Data migration
- Data cleaning
- Database updates
- Data reconciliation
- Data sorting and categorization
These broad tasks have place in nearly any industry or use case – all that changes is the type of data and how it needs to be handled.
Order fulfillment
Repetitive tasks like order fulfillment – including processing packing and shipping – are perfectly suited to RPA. It requires high accuracy, involves large amounts of data entry and validation, and repeats the same process thousands of times over.
Documentation
RPA can be used to ensure proper compliance and documentation.
For example, a legal firm filling in complex documents may use an RPA solution to automatically fill in correct information, verify completion and accuracy, and ensure that the final documents adhere to regulatory standards.
RPA bots can also archive and retrieve documents and data as requested by human employees, or even create audit trails to track workflows and changes.
Types of RPA
Unattended RPA
Unattended RPA bots operate independently, without human intervention. Instead of being triggered by a human, they can run on schedules, be triggered by specific events, or operate 24/7.
For example, an unattended RPA bot might batch process data at the end of every work day, or automate payroll processes.
Attended RPA
Attended RPA bots work alongside human users and are typically triggered by the user themself. These bots assist with tasks that require human intervention or decision-making.
For example, a customer service representitive might use an attended RPA bot to pull up customer information while on a call.
Hybrid RPA
Hybrid RPA combines attended and unattended RPA. Hybrid RPA bots interact with humans when necessary and operate independently when possible.
These bots are used to create a seamless flow between tasks that require human input and those that can be fully automated.
For example, a hybrid RPA bot might assist and employee during a call, then automatically processes the rest of the task after the call is finished.
Intelligent RPA
Intelligent RPA — sometimes called intelligent automation — refers to the combination of traditional RPA with artificial intelligence technologies. These can include natural language processing, machine learning, or computer vision.
On its own, RPA is designed to follow strict rules and handle predictable, structured tasks. It works best when the steps are repetitive and the data is clean and consistent.
But with AI added in, RPA bots can start recognizing patterns, interpreting text, and even making context-aware decisions.
What are the benefits of RPA?
Like other AI-adjacent solutions, RPA is rapidly growing in popularity due to its cost-effective and efficient nature. Since repetitive tasks often suffer from human error, RPA can also improve overall accuracy.
Increased productivity
RPA bots can handle repetitive, time-consuming tasks much faster than humans, which frees up employees to focus on more strategic tasks.
Reduced cost
Automating rules-based tasks significantly reduces labor costs for organizations, particularly when an RPA system is scaled, or when RPA is extended across business processes.
Improved accuracy
RPA minimizes human errors in processes like data entry, calculations, and document handling, leading to higher accuracy and fewer costly mistakes.
They can also be programmed to follow regulatory and compliance guidelines strictly, if a company wants to ensure strict documentation of actions.
Higher employee satisfaction
Employees are more satisfied at work when they’re able to focus on engaging tasks, rather than mundane and repetitive ones.
Scalable and adaptable
RPA solutions can be scaled up or down based on business needs, all without significant investments in infrastructure or resources.
And if business processes or regulatory requirements change, there’s no need to re-train employees – an RPA bot can be quickly reconfigured or updated to match the new standards.
Enterprise RPA
Enterprise RPA refers to the deployment of RPA at a large scale within an organization. It’s designed to meet the complex needs of a business and often expands across different business processes.
It involves the implementation of RPA solutions that are robust, secure, scalable, and capable of handling high volumes of transactions across multiple departments or functions.
Just like enterprise chatbots, enterprise RPA must meet any and all data security and privacy requirements within its jurisdiction and use case. These often include the use of audit trails and role-based access controls to comply with internal policies, industry standards, and regulatory requirements.
How to Deploy RPA
RPA mimics the actions that a human would take when interacting with digital systems. It follows a set of instructions so that it can process data, execute transactions, and communicate with other systems.
Here’s a step-by-step breakdown of how to deploy RPA:
1. Identify appropriate tasks
The first step in implementing RPA is identifying the tasks that are repetitive, rule-based, and time-consuming.
These tasks are then mapped out to understand the specific steps involved. This includes detailing the data inputs, decision points, and actions needed to complete the process.
2. Design and build a bot
Once the tasks are mapped, developers or business analysts design the RPA bot. This involves creating scripts or workflows that instruct the bot on exactly what to do.
3. Deploy the bot
After the bot is designed, it is deployed in the environment where it will operate – typically within the company’s existing software applications. The bot interacts with the software just as a human would, navigating interfaces, entering data, and executing commands.
4. Bot execution
When the bot is deployed, it begins to perform the tasks automatically. It can log in to applications, move files, fill out forms, or extract data from documents.
5. Bot monitoring
Once the bot is operational, it can be monitored in real-time through a control dashboard. This allows businesses to track the bot’s performance, manage its activities, and make adjustments as needed.
If an issue arises, such as a system error or a change in the process, the bot can be reconfigured to adapt to the new conditions.
6. Continuous improvement
RPA is not a set-it-and-forget-it solution. Over time, as processes evolve, bots may need to be updated to handle new tasks or changes in existing workflows.
Additionally, as companies become more familiar with RPA, they often find new areas where automation can be applied, leading to continuous expansion within the organization.
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FAQs
1. Is RPA the same as AI? How are they different?
Not quite, RPA follows strict rules to automate repetitive tasks, while AI mimics human intelligence by learning and making decisions. Think of RPA as the doer and AI as the thinker.
2. What distinguishes RPA from traditional automation tools?
RPA mimics how humans interact with software (like clicking, typing, etc.), so it doesn’t need deep system integration like traditional automation. It just works over the existing interfaces.
3. Can RPA be used without coding knowledge?
Yes. Many RPA platforms are no-code or low-code, so business users can create bots using drag-and-drop tools without needing to write scripts.
4. What are the risks or limitations of RPA?
RPA can break if underlying systems change (like UI updates), and it struggles with unstructured data or tasks needing judgment. It’s great for rule-based tasks but not flexible like humans.
5. How can RPA work in conjunction with AI chatbots or voice assistants?
RPA can handle the back-end grunt work triggered by a chatbot, like updating records or sending emails, so the AI handles the conversation, and RPA takes care of the tasks behind the scenes.
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