6 Use Cases of Generative AI Applications for Document Extraction

Every device, transaction, and interaction in our digital world generates an endless stream of data. By 2025, the amount of global data is expected to reach a mind-boggling 180 zettabytes.  So, how do we extract and make sense of this growing data? 

That’s exactly where generative AI proves its value.  This blog explains generative AI applications for document extraction and how this technology helps cut through the noise and zero in on exactly what you need.  

What’s the Buzz About Gen AI? 

Generative AI (Gen AI) is a form of artificial intelligence that creates new content like text, images, music, and videos.  Instead of relying on predefined rules, Gen AI learns patterns from large datasets and uses them to produce unique outputs.  This ability to generate fresh content makes it more versatile than traditional AI. 

It uses advanced technologies like neural networks, machine learning, and Large Language Models, with popular models like ChatGPT, BARD, and DALL-E leading the charge. 

Gen AI excels at summarizing information, answering questions, and generating tailored content.  It also improves fraud detection and data analysis by recognizing patterns. Gen AI is transforming how we interact with machines, making everyday tasks faster and more efficient.  

Its ability to bring human-like creativity to various fields is what keeps everyone buzzing about its potential.   

How Gen AI is a Step Up from Traditional AI 

Let’s take a look at how generative AI differs from traditional AI, and why this new approach is gaining so much traction in document processing and extraction. 

Feature	Traditional AI	Generative AI
Data Processing	Primarily structured data (e.g., databases, spreadsheets)	Can handle both structured and unstructured data (e.g., text, images, audio)
Learning Approach	Supervised or unsupervised learning	Unsupervised or semi-supervised learning, often using techniques like reinforcement learning.
Output Format	Structured outputs (e.g., numbers, categories)	Can generate human-readable text, images, or code
Document/Data Extraction	Limited to predefined fields and formats	Can extract information from complex documents, including nuanced context and relationships
Efficiency	Efficient for well-defined tasks but can struggle with complex or ambiguous data	Can be more efficient for complex tasks, especially when dealing with large volumes of unstructured data
Accuracy	Depends on the quality and quantity of training data	Can achieve higher accuracy, especially when trained on large datasets

Gen AI + Intelligent Document Processing = The Dream Team 

Gen AI is shaking things up in document processing through advanced capabilities in classification, data extraction, interpretation, and analysis.  It can interpret information from various documents, including structured, semi-structured, and unstructured data.  

Gen AI can also pull data from documents that are poorly printed or obscured, all while filtering out duplicates.  When integrated into Intelligent Document Processing (IDP) solutions it can understand, interpret, and generate content that closely mimics human intelligence. 

How Does Generative AI Work in Data Extraction? 

It starts by cleaning up the text using Natural Language Processing (NLP) techniques like tokenization and part-of-speech tagging.  Then, it uses transformers, the brains behind popular models like GPT and BERT, to apply an “attention” mechanism to understand the relationships between words and sentences. 

For example, in invoice processing, the transformers can pinpoint payment dates while simultaneously tracking amounts and vendor names. 

Trained on huge amounts of text and code, generative AI models can identify patterns and correlations that traditional methods struggle with.  They get better at generating high-quality content by adjusting their settings. 

To use generative AI, you give it a prompt or starting point.  Then, you can have a conversation with it to explore different options and refine the results. The output is in natural language, making it easy for everyone to understand and find the answers they’re looking for. 

Here’s an example:  

Input: User Query  

Output: Natural Language Response 

6 Real-world Generative AI Applications for Document Extraction

Generative AI is causing quite a stir in different industries, helping teams tackle their data extraction challenges.  Let’s look at some of the use cases of Gen AI in extracting data across various industries.  

Finance

In the high-stakes world of finance, accuracy is key, but unstructured data can complicate the process of data extraction.  Luckily, AI saves the day by automating the processing of invoices, receipts, and financial reports — making cents of it all! 

Generative AI extracts vital information from invoices, loan applications, and W-2 forms.  It automatically pulls out crucial details like payment amounts and due dates. The result is streamlined workflows and less processing time.  

This means fewer manual errors and more time for financial analysts to focus on strategy.   

Legal

In the legal sector, generative AI streamlines data extraction from contracts and legal documents.  It quickly finds relevant information, making document management a breeze.  Lawyers can shift their focus from searching for details to winning cases, turning “legalese” into “legal-ease”.  

Retail

Retailers often face a flood of unstructured data from customer reviews and purchase histories.  Generative AI helps make sense of it all.  It reveals trends in customer preferences and identifies top-performing products.  

With these insights, retailers can manage inventory better and create targeted marketing strategies that keep customers coming back. 

Insurance

Generative AI automates the extraction of claim amounts and policy details, turning what used to be a sluggish process into a slick operation.  

With AI handling the heavy lifting, insurers can improve service times and keep clients smiling.  A study found that automated claims processing can reduce turnaround times by a whopping 70%.  And let’s be honest: nobody enjoys waiting for claims. 

Education

Generative AI can analyze student surveys and assessments to pull insights about performance and engagement.  With these insights in hand, educators can fine-tune their teaching strategies and ensure that every student gets the attention they need.  

Healthcare

Generative AI uses NLP to analyze unstructured data in Electronic Health Records (EHRs).  It identifies key information that helps doctors make accurate diagnoses and treatment decisions. 

In clinical documentation, AI takes the pain out of creating medical reports.  It reviews physician notes, patient histories, and diagnostic reports, turning them into structured documents like EHRs and discharge summaries.  These AI systems extract important details and highlight critical concepts, ensuring healthcare providers are “on the same page” and improving patient care coordination.  

How Does Astera Use Gen AI to Extract the Best for Your Business?

As we’ve seen, the integration of Generative AI in data extraction is reshaping how businesses manage their data.  If you’re ready to extract every ounce of value from your data, Astera can help you do that.  Our intelligent solutions make data extraction easy—no more juggling complex formats or manual entry! 

With AI-powered template generation, just specify the fields you need, and Astera Intelligence will do the heavy lifting.  Plus, our robust mapping handles even the most tangled datasets, giving you the confidence that your data is in good hands.

With Astera, you can now extract any document, from any source, in any format, anytime. Ready to extract the full potential of your unstructured data?  Sign up for a free trial today!