Data extraction with AI: How to turn documents into structured business data

What is data extraction with AI?

Data extraction with AI is the process of identifying, extracting, and structuring information from documents automatically.

Instead of manually reviewing documents and entering data into business systems, AI captures the relevant information and converts it into a format that applications can use.

The extracted information can include invoice numbers, supplier details, contract dates, customer information, order data, or any other business-critical content. Once extracted, the data can be validated, transferred to business systems, and used to trigger workflows.

What role does OCR software play in data extraction?

OCR (optical character recognition) converts text from scanned documents, PDFs, and images into machine-readable text. This allows information stored in documents to be captured and processed digitally instead of manually.

OCR provides the foundation for data extraction by making document content accessible to both users and business systems.

What role does Artificial Intelligence play in the data extraction process?

AI builds on the text captured by OCR. It identifies document types, understands context, and extracts the information relevant to a business process.

For example, when an invoice enters the system, AI can automatically identify information such as the supplier, invoice number, invoice date, and total amount. The extracted data is then structured and prepared for validation, workflows, and business applications.

Most business processes depend on information stored in documents. Before that information can be used, employees often need to review documents, find the relevant data, validate it, and enter it into business systems.

This manual effort becomes increasingly difficult to manage as document volumes grow. A process that takes a few minutes per document can quickly consume hours of employee time when repeated hundreds or thousands of times every month.

Manual data entry also creates a higher risk of errors. Incorrect values, missing information, and duplicate entries can impact downstream processes, reporting, and compliance activities.

At the same time, valuable information remains trapped in documents and unavailable for automation, analytics, and decision-making. This contributes to the growing volume of dark data that many organizations struggle to use effectively.

Hey, Doxi, how does data extraction from unstructured documents work?

AI data extraction vs. traditional OCR

OCR and AI data extraction are often used together, but they serve different purposes.

OCR provides the text. AI understands what the text means and determines how it should be used within a business process.

The technologies behind AI data extraction

Several technologies work together to automate data extraction.

OCR

OCR (optical character recognition) converts text from scanned documents, PDFs, and images into machine-readable text.

Machine learning

Machine learning identifies patterns across documents and improves extraction accuracy over time.

Natural language processing

Natural language processing (NLP) helps systems understand the meaning and context of document content.

Large language models

Large language models (LLMs) can interpret complex documents and extract information from unstructured content.

Validation engines

Validation engines check extracted information against business rules, related documents, or external systems to help ensure data quality.

Common AI data extraction use cases

Finance

Finance teams process large volumes of invoices, receipts, and payment documents. AI data extraction helps capture key information automatically, reducing manual effort and accelerating approval processes.

Combined with Doxis Invoice, the data can be validated and transferred directly into invoice workflows.

Procurement

Purchase orders, supplier confirmations, and delivery documents often contain information that needs to be transferred into business systems. AI can extract this data automatically and help improve visibility across procurement processes.

Human resources

HR teams manage contracts, onboarding documents, personnel records, and employee forms throughout the employee lifecycle. AI data extraction helps make employee information available faster and reduces administrative work.

Legal

Contracts and agreements contain important dates, obligations, and business terms that often need to be tracked manually. AI helps identify and structure this information, making documents easier to manage and search.

Customer service and sales

Customer requests, emails, forms, and orders frequently contain information that must be entered into business systems before action can be taken. AI helps capture this information automatically, enabling faster response times and more efficient processing.

Business benefits of AI data extraction

Organizations that automate data extraction can benefit from:

• Faster processing by eliminating manual document review and data entry tasks.

• Reduced operational costs by automating repetitive work and allowing employees to focus on higher-value activities.

• Better data quality through consistent extraction, validation, and reduced manual errors.

• Greater scalability by processing growing document volumes without increasing administrative effort at the same rate.

• Improved visibility by making information searchable, reportable, and available for downstream business processes.

• Less dark data by turning information trapped in documents into structured data that can be used across the organization.

Implementation considerations

Successful AI data extraction depends on more than the technology itself. Organizations should consider:

• Document quality: Poor scans, incomplete documents, and inconsistent formats can affect extraction accuracy.

• System integration: Extracted information should flow directly into ERP, CRM, HR, and other business systems where it can be used.

• Validation processes: Automated and human validation mechanisms help ensure data quality and compliance.

• Security and compliance requirements: Sensitive business and customer information must be protected throughout the extraction process.

• Workflow automation: The greatest value comes when extracted information automatically triggers downstream processes instead of requiring additional manual handling.

How Doxis AI.dp automates data extraction

Many organizations still rely on employees to review documents, extract information, and transfer it into business systems manually. As document volumes grow, this creates bottlenecks, increases the risk of errors, and leaves valuable information trapped inside documents.

Doxis AI.dp from Doxis is an Intelligent Document Processing platform that automates the extraction, validation, and processing of document data. By combining OCR, machine learning, natural language processing, and workflow automation, it transforms unstructured documents into structured information that business systems can use.

With Doxis AI.dp, your organization can:

• Capture documents from email, uploads, scanners, APIs, and business applications

• Classify documents automatically and identify the information relevant to each process

• Extract and structure data from invoices, contracts, forms, orders, and other business documents

• Validate extracted information using business rules and automated checks

• Transfer data directly into workflows and business applications

• Reduce manual effort while improving data quality and process visibility

Organizations using Doxis have achieved significant productivity gains through document automation, workflow orchestration, and AI-driven processing.

According to the Forrester Total Economic Impact™ study, customers achieved a 336% ROI and less than six months' payback from their Doxis investment.

Ready to see AI-powered data extraction in practice? Request a Doxis demo and discover how intelligent document processing can help eliminate manual data entry across your organization.

FAQs on Data Extraction