What is IDP?
IDP combines AI-powered tools like natural language processing and machine learning with traditional capture methods such as optical character recognition (OCR) to extract data from unstructured and structured sources—then formats the data for easier analysis.
Intelligent Document Processing achieves mastery over unstructured document data, which can account for more than 80–90% of enterprise data globally. By recognizing, extracting, classifying, and structuring data for use in agentic AI projects, across workflows, and in data lakehouses, IDP reduces risk and gives a much clearer picture of business operations for strategic decision-making.
Takeaways
- IDP modernizes traditional capture technology with AI capabilities.
- IDP looks beyond individual characters to understand words, sentences, and context—leading to much more accurate results.
- IDP roots out hidden data, or captures data regardless of schema.
- IDP fuels agentic AI, workflow, and data analysis by structuring semantic data in a variety of formats (including JSON and Markdown).
Intelligent Document Processing Stats and Facts
- Enterprise data generated: ~318zb
- Unstructured enterprise data: ~90%
- Data already living in a repository: ~47%
Structured Data vs. Unstructured Data
Unlike structured data (which fits tidily into predefined formats like tables and form fields), unstructured data lacks a clear, organized format and can come in a variety of forms:
- Multimedia files: Images, audio, and video parsed to text
- Social media content and data
- Web page content
- Physical documents or e-files
When variation occurs in format or schema, or when data lives outside easily defined fields (such as rich media), traditional OCR methods may miss data unless specifically trained for each source. That’s highly inefficient and unsuitable for most organizations.
How Does IDP Work?
Intelligent Document Processing combines traditional OCR with additional AI capabilities (like machine learning and natural language processing) to dramatically improve document understanding by recognizing context. This improves first-pass data capture and classification by spotting and labeling data regardless of where it lives on a page or how it’s shared in rich media. Classification can proceed with near 100% accuracy.
Why is Human-in-the-Loop Validation Still Important?
IDP uses machine learning, but it still needs feedback. During validation, the system is told when it’s wrong and learns from its mistakes. Data is classified and given a confidence rating. In most cases, the confidence will be near 100%. But exceptions persist—these are moments when IDP says: “I’m pretty sure this is a purchase order, but I’m not 100% sure. Can you verify?” Validating exceptions steadily improves IDP’s performance, reducing mistakes over time.
Benefits of IDP With Human-in-the-Loop
IDP Reduces the Risk of AI Project Failure
As enterprises generate over 80% of the world’s data (global market insights), most of that data is created to support agentic AI experiences. But AI is only as smart as the data it’s given. Errors in data can be catastrophic for AI agents and the people making decisions with them. With unstructured data accounting for up to 90% (IDC) of enterprise data, there’s a lot of room for error.
IDP Enables Straight-Through Batch Processing and Workflow Automation
IDP can quickly sort large batches of files, separate documents by type (without separator sheets or barcodes), then extract, classify, and route data down workflow streams to the people that depend on it. This can be scheduled, manual, or triggered automatically as documents flow into the organization.
IDP Tidies Document Data for Data Lakehouse Integration
IDP takes unstructured document data and provides structure in new formats like JSON (critical for data lakehouses) and Markdown (the language of AI). Without this capability, querying newly captured data would be terribly tedious.
The post “What is Intelligent Document Processing (IDP)?” was originally published on keymarkinc.com.
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