AI and machine learning have become increasingly prevalent in our daily lives, even though many of us may not fully realize the extent of our interaction with these technologies. Also, from Facebook’s face tagging to Google Maps and Netflix movie recommendations, AI algorithms are constantly at work. However, effectively implementing and scaling these technologies for critical business document processing in the enterprise setting remains a challenge. Organizations still grapple with large volumes of paper-based records, emails, and unstructured documents, leading to substantial document processing costs.
The Importance of Business Document Processing
- A staggering volume of documents: Invoices alone account for an estimated 550 billion documents annually, with additional invoice-like documents multiplying that volume by 5-15 times.
- Manual processing costs: Manual processing of invoices incurs costs ranging from $5 to $12 per invoice, with 62% of costs attributed to the manual effort.
- Automation as a key driver: Automating Business Document Processing (BDP) is crucial for businesses to reduce labor costs and improve efficiency.
Understanding Business Document Processing
- Strategic machine learning capabilities: Firstly, BDP leverages machine learning to automate and optimize processes, enriching the customer experience across the intelligent suite.
- Extracting semantical information: BDP services automate the extraction of meaningful information from unstructured business documents.
- Automatic processing and enrichment: The extracted information is automatically processed and enriched with relevant business data, making it ready for integration into systems and processes.
Business Document Processing Portfolio
- Document Classification: Classifies unstructured documents based on customer-specific machine learning models, reducing manual effort and speeding up document processing.
- Document Information Extraction: Extracts structured information from unstructured documents and enriches it with existing master data and transactional data.
- Business Entity Recognition: Locates and classifies named entities in unstructured text documents, automating and accelerating the process.
- Business Optical Character Recognition: Extracts text from business documents, detecting document language and utilizing the best OCR model.
Value of Business
- Document Classification: Reduces manual effort and errors, and speeds up document processing by routing documents based on their type.
- Document Information Extraction: Automates and accelerates the extraction of structured information from documents, enabling integration with other solutions for enhanced processes.
- Business Entity Recognition: Automates the detection and classification of named entities, simplifying information retrieval and enhancing search processes.
- Email attachments: Utilizing Document Classification and Document Information Extraction, organizations can classify and extract information from email attachments effectively.
Business document processing workflow Automation Steps
An important first step is to collect documents from various sources, such as emails, file servers, and cloud storage. This procedure is streamlined by using technologies like Robotic Process Automation (RPA) or integration platforms with links to other systems. Errors are minimized and processing times are shortened when cloud storage services like Dropbox, Google Drive, or OneDrive are seamlessly integrated.
The crucial process of obtaining data from documents necessitates giving serious thought to the diversity and complexity of the content. Accurate extraction from complicated, unstructured texts is ensured by using cutting-edge technologies such as Intelligent Document Processing (IDP), which makes use of machine learning and natural language processing (NLP).
Prior to any further processing, data must be validated to ensure accuracy. Although certain aspects of data validation can be automated, there are situations in which human validation or approval is required.
Using Business Rules:
Before integrating extracted data into enterprise systems, some business scenarios may need the use of particular rules. Tasks like data transformation and purification are made easier by automation solutions like RPA or custom scripts, which guarantee that the data complies with business requirements and current systems.
Data Population into Current Systems:
The last stage entails incorporating the processed data into current enterprise systems. Achieving this integration involves employing techniques such as webhooks, RPA, and API integration. Real-time data transfer is facilitated by API interfaces, and data can be pushed by the document processing system on demand through webhooks. When direct API connectivity presents difficulties, especially with desktop-based ERP systems like Infor Visual, RPA comes in handy.
Integration Architecture and Consumption Options
- Services offered via SAP Cloud Platform:
- SAP Cloud Platform delivers the services as re-usable services running on its platform.
- Web services and secure communication: Functionalities are delivered via HTTPS web services, secured by the OAuth 2.0 protocol.
- Consumption options: Ready-to-use services are available for commercial consumption via the Cloud Platform Enterprise Agreement (CPEA). Document Information Extraction is embedded in SAP Concur Invoice and integrated into SAP S/4HANA, SAP S/4HANA Cloud, and SAP ERP as part of specific licenses. Trial options are also available for testing and proof-of-concept purposes.
SAP AI Business Services for Business Document Processing offer a powerful solution to automate and optimize document processing, reducing costs and improving efficiency. Therefore, by leveraging machine learning and automation, organizations can transform unstructured documents into structured information. This is by seamlessly integrating it into their business processes. Through the SAP Cloud Platform, businesses can easily access and consume these services. Which then leading to enhanced productivity and a better customer experience.