RPA robots are really good at conducting those systematic and rule based type processes that exist in most offices.
Intelligence can also be added to an RPA process, by integrating Artificial Intelligence (AI), to extend the number of use cases possible with RPA.
One way of describing RPA use cases is to group them into the following categories:
Copy and Paste
Data Extraction
and Input
These RPA automations all involve ‘Bridging the Gap’ between software applications, with a copy-and-paste type actions.
For example, copying data from a spreadsheet or software application, and pasting it into another software application, such as a CRM, a financial package or similar business systems.
Office processes which fall into the ‘Copy-and-Paste’ use-case and can be automated using RPA include:
- Accounts Payable
- Account Receivable
- Creating and Delivering Invoices
- Customer onboarding
- Estimating & Quotations
- Expense Claims processing
- Payroll & Overtime Calculations
- Purchase Orders
- Sales Order Processing
- Time & Attendance
- Website data retrieval (Website scraping)
Copy-and-Paste RPA applications sometimes require Optical Character Recognition (OCR) to read the data from a scanned document, and label it as a specific value e.g. invoice number, subtotal etc
Service Request Triage
Categorisation
and Next Action
These types of RPA processes initially involve some sort of classification by the robot.
What is the customer asking for? What sort of claim is it? What product are they talking about? What sort of email is this?
The process is followed by a relevant next action. For example, retrieving a shipment date, or booking in a service, or emailing relevant paperwork.
Generally, these types of processes follow an 80/20 rule, and the majority of transactions fit into a narrow set of possible requests. Automating this narrow set can dramatically reduce workload and costs.
Office processes which fall into the ‘Service-Request-Triage’ use-case and can be automated using RPA include:
- Customer Service Email processing
- Import/Export Paperwork
- IT Service Desk requests
- Proof of Delivery requests
- Returns/Refund processing
- Shipping Notifications
Service-Request-Triage RPA applications might require a combination of Natural Language Processing (NLP) and/or Machine Learning (ML) to recognise keywords and patterns in unstructured text.
Intelligent Document Processing
Key Details Extraction
and Next Action
Within legal and commercial environments, teams can be bombarded with huge amounts of documents.
Processing these documents with RPA involves extraction of key snippets of information.
For example extracting renewal dates or deadline dates, and then onward processing and next action steps, such as filing or emailing relevant information.
Office processes which fall into the ‘Intelligent-Document-Processing’ use-case and can be automated using RPA include:
- Appeals processing
- Contract management
- Claims Processing
- Legal document processing
- Patient referrals
Intelligent-Document-Processing might require technologies such as Optical Character Recognition (OCR) and Natural Language Processing (NLP) to interrupt unstructured text.