AI-Powered Document Processing and Data Extraction: A Government Deep Dive

Over the past few years, we’ve witnessed an extraordinary push towards digital transformation across various sectors. Governments, in particular, have embraced AI as the driving force behind the transformation of legacy processes, and nowhere is this clearer than in the application of AI-powered document processing and data extraction. These use cases reflect a shared vision to reduce administrative overhead, boost efficiency, and deliver better experiences to both employees and the public. Today, let’s take a closer look at how multiple government agencies are harnessing this technology to modernize their operations.

Summary Table for Document Processing and Data Extraction Use Cases

The Role of AI in Document Processing Across Government Agencies

Document processing is one of those classic pain points. It's repetitive, labor-intensive, and ripe for human error—basically, a perfect candidate for automation. Across agencies like the Department of Labor, GSA, DHS, DOE, and others, AI is transforming how documents are processed, reducing manual workloads, and allowing government workers to focus on higher-value activities.

Here are some of the standout document processing initiatives that have emerged across different federal entities, each with its own story of innovation and impact.

Department of Labor: Form Recognizer for Benefits

The Department of Labor (DOL) has taken a giant leap forward with its Form Recognizer for Benefits project. The solution leverages custom machine learning models to extract data from benefits forms, which can often be complex and full of nuances. What would typically require hours of painstaking manual data entry can now be done in seconds, thanks to AI. This use case is in full Operation and Maintenance mode, meaning it’s already proving its value in real-world settings.

The tech stack includes classification machine learning models and computer vision to scan and interpret form content. This type of targeted, trained model ensures the AI knows exactly what to look for in these very specific documents—a smart use of custom ML training to get precision results.

GSA: Intelligent Document Capture and Extraction

The General Services Administration (GSA) is also leading the charge with its Document Workflow and Intelligent Data Capture initiative. The goal is ambitious—to create a scalable document workflow that captures, classifies, and transfers critical data from PDFs and other document formats. Unlike the more targeted applications, GSA’s approach is broader, working to enhance many workflows simultaneously.

GSA’s initiative is already in the Operation and Maintenance phase, which tells us it’s been through the testing trenches and emerged successful. This project uses NLP (Natural Language Processing) to accurately pull structured data from documents and feed it into appropriate workflows, automating a previously manual and error-prone process.

NSF: The Resubmit Checker

Over at the National Science Foundation (NSF), they’ve implemented a unique solution called the Resubmit Checker. As you can guess, this tool is designed to detect if a proposal has been previously submitted. It’s about ensuring transparency and continuity, particularly for proposals that need revisions before getting the green light.

The Resubmit Checker uses similarity scoring models to assess content and score it based on its alignment with previous submissions. Launched in 2024, it’s in full Implementation mode, helping NSF evaluators streamline the assessment process and improve consistency.

Department of State: Product Service Code Automation

One of my favorite examples of AI in action is the Product Service Code Automation at the Department of State. This initiative uses machine learning to categorize procurement-related codes from product descriptions, which might sound straightforward but actually helps align procurement with market intent.

The automation tool is in the Development and Acquisition stage, utilizing NLP and machine learning models for token extraction and categorization. This kind of behind-the-scenes automation drives clarity and speeds up decision-making in procurement—an area known for its layers of bureaucracy.

DHS: RelativityOne for Document Review

DHS has embraced a platform called RelativityOne to tackle large-scale document reviews, particularly for litigation and FOIA (Freedom of Information Act) processes. Anyone who’s dealt with FOIA knows how overwhelming the paperwork can get—enter machine learning to save the day.

With continuous active learning (CAL) and machine learning clustering, the RelativityOne platform helps reviewers quickly identify patterns and extract relevant information. This tool is fully Operational, marking another win for AI’s ability to make sense of sprawling, unstructured datasets.

Department of Energy: Automated Sorting of Diffraction Data

On the science front, DOE has implemented an AI-based solution to sort high-repetition rate diffraction data. If you’re unfamiliar, this is about analyzing the behavior of materials under X-rays, and it’s important for scientific discoveries in fields like material science.

Implemented in 2021, the sorting system uses machine learning algorithms for correlation analysis, separating natural sample fluctuations from instrument errors. This precision helps researchers get to insights faster, removing manual filtering from the process.

NARA: Metadata Creation and Data Extraction

The National Archives and Records Administration (NARA) has a slightly different mission—preserving and making historical records accessible. To do this, they are testing AI models to generate metadata and extract structured data from archival records like documents, video, and images.

These tools are still in the Testing Phase, but they have huge potential for making archives more accessible. NLP and ML-driven models will enable historians, researchers, and the general public to discover, search, and utilize historical records in a much richer way.

A Shared Vision of Efficiency and Impact

Across the federal government, there is a clear vision: reduce manual processes, enhance accuracy, and bring more agility to public service through AI. By leveraging NLP, machine learning, computer vision, and targeted model training, these agencies are setting a high bar for digital transformation in the public sector.

The mix of Operational, Testing, and Developmental phases across the various use cases tells us that the journey to a fully AI-powered future is ongoing. Some tools are already transforming workflows, while others are refining their capabilities to become even more impactful. One thing is certain: the efficiency gains from these initiatives are setting the stage for a new era in how government works—more efficient, more responsive, and ultimately, more human-centered.

Bringing It All Together

The landscape of document processing and data extraction is evolving rapidly, and it's not just about automation; it’s about enabling people to do their best work by letting machines handle the mundane. Government agencies are taking the steps needed to be at the forefront of this change, setting an example of how AI can improve legacy processes and deliver better value to citizens.

There’s a lot more to come, and I’m excited to see how AI can continue to improve these foundational, behind-the-scenes tasks that keep everything running smoothly. After all, transformation often starts with the details—and nothing says details like automating millions of document fields.