In this guide to data extraction using AI, we’ll explore the core concepts, evaluate the top tools, weigh the benefits and challenges of AI-powered data extraction technology, and show you how to get started with the newest tools. In 2025, AI-powered OCR is automating manual data entry from PDFs, simplifying the complexities of automated data extraction workflows, and becoming more accessible to businesses than ever before. Let's see how!
Software Engineer
While "data extraction" typically refers to pulling key information from PDFs and image documents, it is also used in contexts related to workflow automation and document processing.
Document data extraction is the process of converting information from physical or digital documents into an application-friendly format, such as JSON or CSV, making it easier to integrate with other systems. In most cases, it involves parsing and extracting data from PDFs and document images, which are typically unstructured and not directly compatible with APIs or business applications.
Extracting data from documents is just one part of the process - what happens next is just as important. Errors in extracted data can lead to costly mistakes, so validation and error handling are essential. Additionally, the extracted data needs to be formatted correctly and sent to the right system, whether it’s an ERP, database, or accounting software.
Automating the full data extraction workflow means incorporating validation steps to catch errors, applying business rules to format data, and integrating seamlessly with other applications.
Automated data extraction is often an early step in larger document processing workflows. In accounts payable, for example, extraction tools pull financial data from invoices, validate and format it, and export it to an accounting system, Excel sheet, or database, where processes like approvals and payouts begin.
That’s why data extraction technology is often integrated into automated workflows, playing a key role in automation and RPA processes with tools like Power Automate, Zapier, Blue Prism, and UiPath.
For years, businesses have relied on OCR technology to extract text from PDFs and document images. However, its reliance on fixed-layout documents often demands complex workarounds and costly consultants for handling more variable formats.
Today, AI-powered data extraction tools have overcome those limitations. By combining OCR, deep learning and large language models, modern solutions can extract data from almost any document, whether highly variable invoices or contracts requiring contextual understanding. Not only has the technology improved, but it’s also more affordable, making it accessible to SMBs. With the OCR market projected to hit $32.90 billion by 2030, AI-powered data extraction should be a priority for any business still relying on manual data extraction from PDFs to business applications.
AI data extraction tools generally fall into two significantly different categories:
Document extraction-only tools provide AI models that users can integrate into their own applications. While these AI models work out of the box, users must handle its setup and integration themselves. Large language models (LLMs) are the most widely adopted in this category, but it also includes pre-trained template AI models that offer high accuracy for documents they've been pre-trained on, such as tax forms.
Large language models (LLMs) are highly effective at extracting data from unstructured text, thanks to their ability to understand context and meaning with impressive accuracy. These tools excel at processing complex documents, like contracts and reports.
However, they aren't specifically designed for data extraction, so you'll need to build your own error handling solutions and integrations. Effective error handling is crucial, as LLMs can "hallucinate" or generate incorrect information. They cannot be retrained and rely on prompt engineering to adapt to specific documents. Popular tools in this category include OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude.
Template-based AI models are pre-trained on specific document types, making them highly effective for extracting data from fixed layouts like tax forms or standardised layouts. Some models allow limited customisation for added flexibility. Custom post-processing, typically with RegEx, is often needed to validate and clean the data. While much less flexible than LLMs, they offer high accuracy for simple, predictable documents. Examples of template-based models include Amazon Textract, Google Document AI, Azure AI Document Intelligence.
These tools offer everything needed to automate data extraction workflows without writing any code. They provide pre-trained AI models, and also highly customisable AI models that self-improve over time, making them ideal for the most complex document data extraction needs. They include interfaces for validation and error handling, and formatters for applying business logic.
They typically cater to businesses that prefer a self-service approach and rapid deployment, and provide flexible API/webhook integrations, as well as native integrations with popular tools such as Power Automate, Zapier, UiPath, and Excel. Popular tools in this category include Cradl AI, Hyperscience, Rossum.
Any business investing in manual data entry can benefit from AI-powered data extraction, but industries handling large volumes of documents, such as finance, insurance, manufacturing, education, healthcare, and logistics, stand to gain the most. These sectors depend on efficient document workflows, making accurate and flexible data extraction a critical driver of cost savings and increased efficiency.
Document processes often requires manually entering data, a tedious and time-consuming task that can consume up to 40% of an office worker's day. AI-powered data extraction significantly reduces this burden by automatically converting data from unstructured documents like PDFs and images into a business application-friendly format.
According to Gartner, organisations believe poor data quality to be responsible for an average of $15 million per year in losses. Human errors in data entry lead to financial losses, compliance risks, and inefficiencies. AI-powered OCR and machine learning models extract and validate data with greater precision, minimising the need for corrections.
Previously, the technology behind document data extraction was often too complex and costly for small and medium-sized businesses. As a result, SMBs had to rely on manual methods or outsource tasks. Today, with the advent of no-code, cloud-based solutions featuring user-friendly interfaces, SMBs can automate these tasks in-house
AI-powered extraction tools often integrate with business systems like ERP, CRM, and cloud databases through APIs and webhooks, streamlining document processing and data management. Modern AI data extraction solutions automatically adapt to different document formats, allowing businesses to scale efficiently and process larger volumes with fewer resources.
AI-powered data extraction allows businesses to process documents in real-time, enabling immediate access to critical information. This leads to more data-driven decision-making and enhances agility by allowing businesses to respond faster to opportunities or challenges.
While AI data extraction represents a major advancement, there are still significant challenges that need to be tackled to avoid issues like inaccurate results, workflow disruptions, or failed automation.
AI data extraction isn’t perfect, and a few mistakes are bound to happen. Spending too much time fixing those mistakes is kind of defeating the purpose of automation. That’s where a "human-in-the-loop" data validation step comes in: uncertain predictions get flagged for review, so you can handle errors gracefully without slowing down your automation.
If an AI model repeatedly makes the same mistake when extracting data from documents, it can seriously hinder your workflow. To prevent this, it's crucial to establish a feedback loop where past mistakes and their corrections are used to retrain the model. This allows the AI to learn from its errors and improve, ensuring fewer repeat issues and more reliable results over time.
Long documents like PDFs can be a challenge for AI extraction, with some services timing out. Using algorithms to split documents into smaller sections can help. However, if you're only extracting a few data points from a 70-page document, it may not be cost-effective, as many tools charge per page. In such cases, it might not be worth the expense.
AI data extraction is highly flexible and can handle a wide range of document layouts. However, certain structures, like tables, require reading in a specific order, which can be challenging. The solution is to use an AI model that supports defining tables as specific fields, allowing it to recognize and process them correctly.
Low-quality documents, like blurry scans or noisy images will compromise the accuracy of the AI model. To improve this, preprocessing steps like de-skewing, noise reduction, cropping and zooming can significantly enhance the document's legibility before it's processed by the model. These techniques ensure better extraction results, even from imperfect documents.
These document types provide the fastest return on investment with AI data extraction, as many services provide AI models that are pre-trained specifically on them, allowing for highly accurate data extraction right out of the box.
Invoices are central to managing cash flow and vendor relationships. By automating the extraction of totals, due dates, and vendor details, businesses can streamline processes like accounts payable, cost tracking, and reconciliation. Automation ensures invoice data is processed quickly and accurately, improving financial management and reducing the impact of errors or delays.
Purchase orders (POs) are key to managing procurement and inventory, helping businesses formalize orders with vendors. By automating data extraction—such as item descriptions, quantities, prices, and shipping details—businesses can streamline workflows like order tracking, invoice reconciliation, and inventory management. This reduces errors and boosts procurement efficiency.
Receipts play an important role in tracking business expenses and managing reimbursements. By automating the extraction of data such as total amounts, tax values, vendor details, and dates, businesses can significantly streamline workflows like expense reporting, tax filing, and employee reimbursements. For organisations without dedicated expense tools, automating receipt data extraction from emails offers a seamless alternative to manual processing.
Bills of lading (BOLs) are essential shipping documents that serve as proof of shipment and outline the terms of transportation. By automating the extraction of key data such as shipment details, item descriptions, quantities, and shipping addresses, businesses can simplify logistics management, improve inventory tracking, and manage shipping costs effectively.
Bank statements are crucial for managing cash flow, monitoring financial health, and reconciling accounts. By automating the extraction of key data such as transaction amounts, dates, payees, and account balances, businesses can streamline workflows like reconciliation, financial reporting, and cash flow management. For businesses looking to scale, automating data extraction ensures greater accuracy and timely oversight as transaction volumes grow.
It's really easy to create automated data extraction workflows by using the latest tools on the market. While the specifics vary, these tools typically follow the same core steps and require no coding. We'll use screenshots from Cradl AI, though the process is similar across solutions.
Start by defining the key information you want to extract. For example, with invoices, you typically extract details like invoice numbers, dates, total amounts, and vendor information.
Import your documents in formats like PDFs or images. Most tools support automated imports (e.g, from emails) and manual batch processing, allowing you to extract data from multiple files at once for greater efficiency.
After the AI has extracted the required data, validation might be needed to correct any errors. Many tools offer confidence scores and human-in-the-loop validation to ensure accuracy before exporting.
Send the extracted data to your ERP, CRM, database, Excel, or automation tools like Zapier or Power Automate. Seamless integration ensures that data flows into your existing workflows without manual intervention.
What once required expensive, enterprise-grade software is now accessible to businesses of all sizes. No-code, cloud-based AI solutions have eliminated the technical barriers, enabling companies to become more data-driven and free employees from repetitive data entry. There has never been a better time than 2025 to adopt AI data extraction technology.
We’ll help get you started with your document automation journey.
Schedule a free demo with our team today!
Internal document processing on autopilot.
