What is RPA in Automation?

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.

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.

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.

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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.

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.