6 Robotic Process Automation Examples to Inspire IT Leaders

For many years, robotic process automation (RPA) software was the primary focus of automation strategy. Although AI has stolen the spotlight, RPA remains essential for maximizing automation opportunities. This post explores examples of RPA use cases across industries. 

But first, what is RPA? Robotic process automation uses software robots to offload routine tasks. RPA bots interact with systems in the same way humans would (such as clicking buttons or entering data), but do so faster and at greater scale. RPA works best for repeatable and rule-based tasks. For more complex processes, cognitive automation via AI works better. However, both tools are essential for an intelligent automation strategy. 

Organizations across industries use RPA to streamline key business processes. Read on to discover six examples of how industries can use RPA.

Robotic process automation can be used across industries and departments to improve processes. Here are a few examples:

Consider a real-world example from an Appian customer: An international financial services company used RPA to build comprehensive data reports that let them better spot changes in compliance and financial indicators.

This company struggled to integrate financial data from news websites into their reporting. These websites lacked APIs, so bank employees used manual data entry to build reports. Manually collecting and transforming data were time-consuming tasks prone to human error. 

They improved using a process platform, and RPA was a critical element of the solution: 

• Software bots pull data from financial systems and websites and convert it into spreadsheet templates.

• A process orchestration application formats the data into easily-readable reports and presentations.

• A secure process platform integrates RPA into a hybrid IT infrastructure, ensuring data privacy and compliance.

This RPA-driven process dramatically reduces manual labor and increases reporting accuracy. The firm boosted operational efficiency while upholding financial regulations.

Employee onboarding goes on at every company. And without strong automation solutions, it’s a time-intensive process with multiple repetitive tasks. Automating these administrative tasks boosts employee productivity while reducing manual errors. When an employee accepts an offer and sets a start date, a digital system can use RPA to trigger the next steps in the onboarding process.  Rather than handling every critical onboarding task, recruiters and HR workers only have to address problems or exceptions.

Here’s what it could look like:

• The recruiter enters the start date in the onboarding workflow. The workflow uses automation to create a new hire portal and notifies the new employee via email. The new hire enters their information and uploads the necessary forms.

• Business rules (another form of automation) based on the employee’s role and department inform how IT sets up their computer and permissions. 

• The company’s payroll software is a legacy system that does not offer an application programming interface (API). So, an RPA bot connects to the payroll software, to set up direct deposit with the employee’s banking information. 

• Another automation capability added to the workflow using low-code triggers an email to the facilities manager to assign a workspace and security credentials. 

Voila! The employee is all set up and ready with minimal work from the HR team. Notice that only one of these steps involved RPA. That’s because RPA is great for some tasks, but for more complex processes, it works best in partnership with intelligent other automation capabilities.

You could also use RPA to streamline these HR use cases:

• Recruiting

• Offboarding processes

• Payroll

• Absence/PTO management

• HRIS integrations

• Compliance and privacy

• Benefits management

• Succession planning

• Employee lifecycle management

• Employee compliance management

RPA can also streamline the billing process. For example, take a global customer with a high volume of invoices across countries. They have to validate the currencies, amounts, and tax rates for each geography. Increasing pressures like shorter payment terms spur them to make the process faster and more accurate. Automating this process would enhance customer satisfaction by ensuring timely and error-free billing.

This company could build an RPA bot to process the invoices. Here’s how how it would work:

• Finance sends customer invoices to the billing team.

• A bot downloads them, scans them and captures the data, validates the information, and submits the invoice to the right customer.

• Once completed, the bot generates a final report for the billing team’s control and audit.

This speeds up the team’s invoice processing time and average payment collection time and improves invoice accuracy for each collection cycle.

You can also use RPA for other finance processes:

• Know Your Customer (KYC)
• Loan processing
• Same-day fund transfers
• Account opening/closure
• Customer lifecycle management
• Building profit and loss reports
• Building cash flow statements
• Creating account statements
• Distributing account statements

Many enterprise organizations have multiple legacy databases storing customer information. Without RPA, an employee might have to repeatedly search for customer information in one database, then manually add it to another. This type of work is monotonous and doesn’t require analytical thinking—but it’s crucial to the company’s operations. Here’s how RPA could improve the process:

• The bot logs into the legacy system. (Because this legacy database doesn’t have an available API, an RPA bot is a great solution.)

• The bot scans, extracts, and saves data in a file.

• The bot then logs in and uploads the file to the second database, generating a report for employee review.

Automating this task with RPA saves employees hours on manual work and gives them time to focus on other projects. The bot brings them in as needed to review.

Here’s another real-world example from an Appian customer: A leading pet insurance company removed a major barrier to the scalability of their business by automating claims processing. By decreasing their reliance on manual keyword searches and outsourcing to process 200,000 claims per month, they save time and money and improve their potential for growth.  

For this insurer, processing claims was a repetitive task that required human workers to manually search for keywords in medical records. This was unsustainable for such a high volume of claims. With RPA, the process has become significantly more efficient:

• Bots perform keyword searches across medical records with a library of 10,000 potential terms.

• Critical keywords are highlighted, mapped to potential medical conditions, and flagged for review by the data science team.

• RPA integrates with the company’s existing PDF tools, keeping their claims workflow connected.

Let’s say a large, publicly traded company wants to reduce the risk of insider trading. But there are so many trades that it takes the legal team hours to review. They decide to use automation to help reduce cycle time:

• Employees answer a series of questions that determine whether they have insider knowledge. This self-service form eliminates ad hoc email requests.

• Business rules identify and approve all requests that can move forward without human intervention.

• The investment website has no API, so once the trade is approved, an RPA bot logs in with a browser to approve trading.

• The workflow disables trading after the scheduled delay so no team member has to remember to do it manually.

• If there are any exceptions, smart services redirect the task to the legal team for review.

This automated process saves the legal team time, reduces opportunities for manual errors, and ensures a greater level of protection for the company.

As you’ve seen in these RPA examples, companies can use bots to improve processes and increase business efficiency. But RPA plays just one role in a bigger web of automation.

RPA is most effective when handling tasks that meet these criteria:

• Routine processes with few variations or exceptions. If a task is always performed in the same way every time, RPA works well. It’s not a fit for situations that have many variations, like if invoices all need to go to different locations versus to just one database. Otherwise, you’d have to build a new bot to address each new situation.

• High-volume tasks. RPA creates the most value when it takes over on a task that happens a lot, like sending order confirmations by email or processing thousands of stock trade requests. RPA also works well for low-volume tasks where you want to reduce errors or increase compliance.

• Rules-based tasks. Business rules guide RPA bots to know what to do in certain situations. Bots work best when there are a set of consistent rules to follow.

• Tasks existing in well-defined processes. If the environment around the bot is mature and consistent, RPA will work well. When systems and processes are changing, the bot owner will have to make frequent changes to the RPA workflow.

• Tasks with structured data and readable inputs. RPA needs clearly defined data and inputs so the bot can easily search for information. For unstructured or unreadable data, RPA can still work if used with additional automation tools.

Additionally, RPA is a great solution for when you need to connect systems with no API in place. When your company implements a new technology but it can’t connect to your existing systems, RPA can unify these systems quickly without the need to develop new APIs. And eventually, if an API becomes available, you could upgrade that part of your workflow to an integration.

When you face a more complex issue, consider pulling in these other automation tools:

• Business rules. Business rules instruct other elements, such as RPA, how to carry out tasks or deliver information. These rules follow an “if X, then Y” format.
• Artificial intelligence. Use AI to handle more complex tasks. AI is called cognitive automation because it mimics human thought. Adding AI to processes can help you understand documents and extract critical information. Or you can use generative AI to generate email drafts or find personally identifiable information.
• Smart services. Create integrations, actions, steps, and dependencies with smart services. These services can send a push notification or an email, export data, or schedule an activity. Additionally, in a low-code process automation platform, you can easily add them to a workflow.

In tandem with RPA, these automation tools expand your capacity to improve all sorts of processes across an organization, as opposed to just using RPA in isolated silos.