Data’s value to your organization lies in its quality. Data quality becomes even more important considering how rapidly data volume is increasing. According to conservative estimates, businesses generate 2 hundred thousand terabytes of data every day. How does that affect quality? Well, large volumes of data are only valuable if they’re of good quality, i.e., usable for your organization’s analytics and BI processes.

If you’re working in the data space today, you must have felt the wave of artificial intelligence (AI) innovation reshaping how we manage and access information. One of the areas affected is data catalogs, which are no longer simple tools for organizing metadata. They’ve evolved dramatically into powerful, intelligent systems capable of understanding data on a much deeper level.

Information extraction (IE) finds its roots in the early development of natural language processing (NLP) and artificial intelligence (AI), when the focus was still on rule-based systems that relied on hand-crafted linguistic instructions to extract specific information from text. Over time, organizations shifted to techniques like deep learning and recurrent neural networks (RNN) to improve the accuracy of information extraction systems.

Business leaders find themselves involved in a range of high-priority tasks, most of which require making critical decisions. Let’s say you’re the sales head of a global organization. You’re ready to make an important decision about next quarter’s sales strategy, but you must first look at the right data set. You know it exists somewhere in your organization’s databases, yet it’s not within the arm’s reach.

The financial document processing domain has undergone a 360-degree shift in the past decade. It was at the brink of the 1980s when software providers began releasing document management systems aimed at helping companies save time, money, and effort in regard to financial document processing. What started as simple document management systems using Optical Character Recognition to digitize printed financial documents has evolved into advanced, AI-powered solutions.

What if your document processing system could do more than categorize documents and extract data, no matter the format? That’s exactly what you can do with intelligent document processing (IDP) software. IDP tools adapt to varying structures and formats and understand content to summarize lengthy documents, identify anomalies, and flag errors. The best part? IDP software continuously improves in accuracy the more you use it.

The GenAI revolution is well and truly here. To take inspiration from our favorite comfort show, Gilmore Girls, “It’s GenAI’s world, and we’re just living in it.” In fact, McKinsey reports the number of organizations regularly using GenAI has doubled in ten months between their 2023 and 2024 surveys.

Your docs are a lot like your family—not in the corporate jargony “we are a family” way, but more in the “can’t live with them, can’t live without them” way. Yes, these docs are crucial in more ways than one, but teams that regularly work with them know that the time they spend searching for, cleansing, and prepping their docs can be better utilized elsewhere.

Let’s be honest – AI can seem like a bit of a mystery, and with this mystery comes myths and misconceptions. Is it actually that good? Can it handle varying document structures? Can it integrate with my existing systems? Because of this mystery, many companies have yet to take the leap and incorporate AI into their data processes. Today, we’re going to play MythBusters, separate fact from fiction, and show how you can use AI document processing to maximize efficiency and save costs.

The computer revolution in law took flight in the 1970s with the release of the iconic red “UBIQ” terminal. This innovation completely changed how legal document management was performed. It empowered lawyers to easily browse case law online rather than looking through towering racks of yellowed paper. As the years passed, a wave of new document management solutions emerged.

Healthcare data is set to soar, with projections showing that it will grow from 2,300 exabytes in 2020 to an impressive 10,800 exabytes by 2025. To put that in perspective, that’s like having enough data to fill over 2.5 billion DVDs! What’s more is that a large portion of this data is unstructured—scanned documents, handwritten notes, and PDFs that don’t easily integrate into traditional systems. This is where Intelligent Document Processing (IDP) comes in.

What’s one thing all your favorite legal shows have in common? Whether it’s Suits or The Lincoln Lawyer, they rarely show the amount of paperwork lawyers have to handle on a daily basis. Understandably so, paperwork isn’t the most glamorous part of the job but that doesn’t mean it’s not crucial. In fact, lawyers deal with tens, if not hundreds, of documents on a daily basis during most parts of their job, such as discovery, research, or drafting.

In the world of LLMs, size doesn’t matter. It’s how you generate output that counts. Generative AI (GenAI) adoption rate in organizations jumped from 33% to 65% this year, which means if your organization isn’t leveraging AI, it’s time to get on board or get left behind. One powerful way enterprises are leveraging GenAI is by training and deploying private Large Language Models (LLMs).

Retrieval-augmented generation (RAG) is gaining traction, and for good reason. As businesses and AI experts search for more intelligent ways to process information, RAG combines the best of both worlds, i.e., the vast knowledge of retrieval systems and the creative power of generation models. But what exactly is RAG, and why is everyone talking about it?

What do you think of when you think of generative AI? Generating photos, animations, and videos? Coding and solving math problems? Writing content and brainstorming with a chatbot? These have all driven plenty of excitement around AI, but there’s so much more to it than that! From an enterprise perspective, Generative AI’s impact on intelligent document processing technology is remarkable.

Every business handles numerous document types—contracts, purchase orders, reports, invoices—you name it. And the thing about documents? They never look the same. One day, you’ve got a well-organized PDF with neatly labeled sections, making it easy to find what you need. The next, you’re stuck with a document that’s all over the place—random tables, text scattered everywhere, or even a scanned image that doesn’t fit the mold.

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.

Retrieval Augmented Generation (RAG) is an intelligent assistant that helps you find exactly what you’re looking for in a pile of medical records. Like an X-ray shows you hidden details inside the body, RAG helps you quickly extract precise information from complex data. RAG provides instant, accurate answers—often visualized in charts or summaries that require analysts to produce manually. RAG combines two AI capabilities—retrieval systems and generative models.

Let’s say you’re leading a company that receives thousands of documents daily. These documents come in various formats like Excel, PDFs, CSVs, and more. And they differ in terms of layout. Before you can analyze the data, your team spends hours sorting, cleaning, and preparing these documents. Most of their time is spent preparing the documents for integration into business systems. Then, a colleague shares how intelligent document processing helped him save time and boost productivity.

Pharmaceutical industry leaders are adopting new artificial intelligence (AI) technologies and increasing process efficiency. The Infosys report on AI adoption shows that pharmaceuticals are among the most mature industries in Al adoption. In the same report, 40 percent of the respondents claimed their organizations had deployed Al and that it was working as expected. AI-powered features help them manage massive volumes of pharmaceutical data with great accuracy and speed.

AI has entered many aspects of data integration, including data mapping. AI data mapping involves smart identification and mapping of data from one place to another. Sometimes, creating data pipelines manually can be important. The process might require complex transformations between the source and target schemas while setting up custom mappings.

Picture this: You are part of a BI team at a global garment manufacturer with dozens of factories, warehouses, and stores worldwide. Your team is tasked with extracting insights from company data. You begin the ETL (Extract, Transform, Load) process but find yourself struggling with the manual effort of understanding table structures and revisiting and modifying pipelines due to ongoing changes in data sources or business requirements.

The year is 1424. You’re shipping goods across the world, and the ship in question gives you a bill of lading. It’s a piece of paper containing details about what your goods are, where you’re shipping them from, and where they’re headed. Fast forward to 2024. You’re shipping your goods across the world, and the shipping company gives you a bill of lading. It’s still (most likely) a piece of paper.