Automating a business has made many processes more accurate, cost-effective, and profitable. The growing use of automation is made possible with the use of Intelligent Document Processing (IDP) and Robotic Process Automation (RPA) which are among the most powerful automation-enabling technologies. Different tools cater to different needs, thus assisting in the digital transformation of the business in different ways. Together, they form Integrated Automation Solutions. This article will elaborate on the distinctions between IDP and RPA, as well as the advantages to key business processes by merging the two functions.
Table of Contents
What is RPA?
Robotic Process Automation (RPA) is a software technology that automates monotonous tasks according to set rules and procedures by using software robots (bots). These bots replicate human activity within digital systems, including retrieving and inputting information, data entry, data migration, and activating workflows.
Key Features of RPA:
- Rule-Based Automation: RPA performs designated functions without any human involvement, according to predetermined guidelines.
- User Interface Interaction: Bots take over application interactions the same way people do so there is no need for complicated system integrations.
- Data Handling: RPA is capable of cross-platform data exchange, thereby minimizing the manual labour required and mitigating mistakes.
- Process Standardization: It guarantees uniform performance of the same tasks in different workflows.
Common Use Cases of RPA:
- Invoice Processing
- Generation of Reports
- Data Entry and Migration
- Customer Service Automation (Email Response, ChatBot)
- Compliance Monitoring
Although RPA is particularly good at automating structured processes, it is not as efficient when working with unstructured data like scanned documents, emails, or hand-written notes. This is the point at which Intelligent Document Processing (IDP) steps in.
What is IDP?
Intelligent Document Processing (IDP) uses AI to extract classify and process information from all types of documents whether structured, semi-structured, or unstructured. IDP applies OCR, Natural Language Processing (NLP), Machine Learning, and AI classification technology to transform unstructured documents into structured formatted data.
Key Features of IDP:
- Document Classification: This feature can tell apart and classify the many different documents like invoices, contracts, receipts, etc.
- Optical Character Recognition (OCR): Captures text contained in images or scans of documents.
- Natural Language Processing (NLP): Analyze and/or understand the text to draw meaning out of it.
Common Use Cases of IDP:
- Processing invoices and receipts
- Contract reviews and compliance analysis
- Claims management processing for insurance
- Reconciling the bank statements
- HR Documents processing (CV, onboarding documents, etc.)
Although IDP is powerful when it comes to extracting and processing data, it does not automate entire workflows. This is the reason combining it with RPA makes a powerful automation ecosystem.
Key Differences Between IDP and RPA
| Feature | RPA | IDP |
| Focus | Task automation | Data extraction and document processing |
| Data Type | Structured data | Structured, semi-structured, and unstructured data |
| Technology | Rule-based bots | AI, ML, OCR, NLP |
| Interaction | Works with UI and applications | Extracts and processes document data |
| Examples | Automating repetitive tasks like data entry and report generation | Extracting and classifying information from invoices, emails, and PDFs |
How IDP and RPA Work Together
Even though IDP and RPA represent different technologies, their combinatory use enhances effectiveness. Here’s their teamwork breakdown for business improvement:
- Document Ingestion: IDP captures and stores documents like emails, scanners, and cloud files.
- Data Extraction and Validation: IDP pulls relevant data using its OCR and NLP. Then the data is corrected using machine learning algorithms to ensure accuracy.
- Data Processing with RPA: After reading and structuring the extracted data, IDP provides it to the RPA bots. The bots then put the information into the necessary business applications, starting needed workflows or doing other assigned tasks.
- Decision Making with AI: AI using IDP can classify and recommend actions for documents, while RPA automatically implements those recommendations.
- End-to-End Workflow Automation: When RPA is integrated with IDP, businesses can automate the entire workflow process of invoice approvals, contract reviews, and even compliance.
Real-World Example: Automating Invoice Processing
Imagine a company that manually handles thousands of invoices. Traditionally, employees are expected to scan invoices, extract relevant information, validate it, and ultimately enter it into an accounting system. This process is quite slow, has features that are prone to error, and is overall very inefficient.
Solution with IDP and RPA:
- IDP Extracts Data: After extracting the invoice number, date, amount, and vendor information, IDP scans invoices and validates them against specific preset conditions.
- RPA Process Automated Data Entry: The data obtained is then supplied to the RPA bot that integrates the data within the accounts and finance package.
- Automated Workflow: RPA activates an approval workflow and sends the invoice to the appropriate department for approval.
- Discrepancy Resolution: Any errors noticed will prompt an invoice to be sent to a human for secondary review and validation.
Having an RPA combined with IDP improves the effectiveness of a company by reducing manual labour, correcting errors, and increasing the rate at which invoices are handled.
Benefits of Combining IDP and RPA
- Higher Accuracy: The error rate correction in Data Processing (IDP) automation is enhanced, and RPA guarantees standardization of processes.
- Improved Efficiency: The processing time is cut down significantly improving productivity as automation takes control of mundane duties.
- Scalability: Companies can increase the documents that are dealt with at a given time without the corresponding increase in staff.
- Cost Savings: Operational expenses are decreased as per the reduced staff work allocation.
- Compliance and Security: Automated processes that are well-structured reduce compliance risks regarding human error, and at the same time ensure that there is adherence to the set regulations.
Conclusion
IDP and RPA work at different levels; however, together they deliver an extraordinary approach to automation. While RPA excels at repetitive activities, IDP is great at analyzing unstructured data for intelligent automation. The combination of these two technologies allows organizations to work with high productivity, accuracy, and volume for data-intensive processes. Organizations looking to optimize their process need to think about how these solutions can be integrated to enhance their automation processes.
