Now that we have looked at the core technologies behind it, let’s explore the typical workflow of an IDP solution and how it delivers impact:

Document Capture & Classification

IDP starts by capturing documents from various sources like emails, portals, folders, ECM systems or even postal mail. Documents may be in any format – PDF, Word, Excel sheets, scanned images, handwritten notes etc.  

Powerful classification algorithms then interpret the document type (for example – invoice, ID card, contract), subject matter, and attributes based on the text, images, layout, fields and metadata within the document.

Accurate classification ensures downstream processes have the right context to extract valuable information. Models continuously improve at this with more documents.

Information Extraction 

Once classified, domain-specific information extraction algorithms parse the documents using OCR, NLP and ML to identify and capture relevant data fields. 

For instance, key fields like invoice numbers, invoice date, amounts due and vendor names will be lifted from an accounts payable invoice document. Extraction accuracy is critical here.

In-built as well as custom AI data extraction templates can be used here for common document use cases. Users can also train custom models when needed for complex documents.

Data Validation

The extracted data then goes through an additional quality check to validate accuracy before further usage. Checks include data type validation, mandatory field checks, cross-field checks, database lookups, duplicate checks and more based on rules configured.

This step minimizes downstream issues due to bad data. Both automated and manual review of corrections are enabled here before data gets processed further.

Data Processing & Export

Once validated, the extracted data gets structured, processed and exported into desired systems like ERPs, databases, data warehouses, business intelligence tools etc.

Common processing needs like data transformation or enrichment that may be needed before exporting data is configured here. APIs and integrations automate system connectivity.

Analytics & Continuous Learning

With continuous ingestion of higher volumes of documents, IDP solutions become smarter over time using machine learning algorithms. Natural language models also keep improving with more contextual data. 

Ongoing accuracy analysis helps fine-tune processes. Dashboard analytics provide insights that help optimize document workflows. Any new document types can be learned dynamically through pre-built or custom models.

By continuously self-learning from past documents without much human intervention, IDP solutions sustain accuracy while adapting to new document types. This enables scalability across document volumes in enterprises.