Not long after graduating college, in the late 2010s, I had a data analysis job that scarred me for life. It involved reading through hundreds of documents—some of them handwritten—and meticulously, painstakingly entering data into spreadsheets. Who among us hasn’t been scarred by a similar experience? I remember thinking, as I spent mindless hours copy-pasting rows of text from one screen to another, that there had to be a better way. Mercifully, there is.

Let’s talk about AI document analysis.

Table of Contents

What is AI document analysis?

How does AI document analysis work?

AI document analysis examples: How are other people using it?

Frequently asked questions about AI document analysis.

How to get started with AI document analysis.

AI document analysis, also called intelligent document processing (IDP), is a modern automation technology that extracts, classifies, summarizes, and generates meaningful insights about data stored in documents of all kinds. 

Think of any process that requires pulling lots of data from semi and unstructured documents: insurance claims processing or policy renewal, credit card or loan application processing, invoicing or billing, hospital patient intake, contracting, order processing, the list goes on. An IDP tool can sort your documents into categories, recognizing the difference between an invoice, claim, and purchase order. Then, it can pull out all the data you need and display it in a more structured format that can be easily parsed by either a human or a machine and used in automated workflows going forward.

How much faster could you work if you didn’t have to worry about manual data processing anymore? What more exciting projects would you have time to focus on?

And what features should a good IDP tool have in 2024? Here are the basic steps most modern AI document analysis tools follow and the technologies involved along the way:

First, the AI needs to read through the document. If the document is handwritten, an image, or a scanned PDF of a physical document, the AI will use optical character recognition (OCR) to put it in a machine-readable format. OCR is a technology trained on lots of different fonts and typeface styles that can recognize characters and convert them into digital text. 

Once the data is readable, the document AI tool interprets it using—you guessed it—natural language processing (NLP), a machine learning technology that lets computers comprehend human language. Think of the way a chatbot understands human queries and responds appropriately (most of the time)—that’s a form of NLP.

Now that the AI knows what the document says, it can classify what kind of document it is. It can sort invoices into one virtual pile and purchase orders into another. Some IDP tools will come with pre-configured document types, and others will let you customize your document categories by creating your own rules for classification. 

More advanced AI models will go beyond rules and let you train them on documents that represent the categories you’d like to use. They’ll automatically learn the differences between categories and classify accordingly without much effort at all on your end. ​​

Training an AI model can be challenging, though, if you’re doing it the traditional way. A low-code platform like Appian can be a big help. The Appian Platform has a pre-configured AI Skills feature that lets you avoid the complex development work to train, provision, and integrate AI. It’s all low-code, so all you need to do is a bit of pointing and clicking.

Training an AI model to classify documents with Appian AI Skills

This is where your data entry nightmare began and where AI document analysis steps in to relieve you, at long last. Instead of you having to copy-paste data from each document into a more structured, usable format—maybe a company database or application—the AI does it for you. The data extraction process is similar to the classification process. The IDP tool can either operate by rules telling it what to look for, or, you can train a more advanced AI model with sample documents containing data similar to what you want to extract.

Here’s where the humans come in. (You didn’t think we were off the hook entirely, did you?) After the AI is done, you can set up a “reconciliation” task that will automatically loop in a human to verify all or just some of the data the AI has classified and extracted. Sometimes, only documents that look unusual in some way are sent for reconciliation, maybe because they’re missing information or were filled out incorrectly.

This way, you keep a human in the loop to handle exceptional cases and make sure the AI is working as expected. AI isn’t perfect, and human oversight is always going to be part of the process—the AI future is one of mixed autonomy. AI models can learn from the way humans handle these reconciliations, though, using machine learning to improve their algorithms so they can handle similar cases independently in the future.

With more basic intelligent document processing tools, data extraction is where the AI’s work ends. But IDP tools with generative AI capabilities can go further. A generative AI powered by a large language model will be able to summarize your documents for you, giving you an overview of key information and trends. And if you’re working on a platform with an AI chat component, you’ll be able to ask questions about your documents and get instant valuable insights.

People use AI document analysis for all sorts of processes across basically every industry. Here are a few examples.

What might you turn your attention to if you didn’t have to process invoices manually? You can use AI document analysis to classify invoice documents and extract data, such as billing details, line items, and due dates. No one should be manually dealing with invoice data in 2024.

Automate employee onboarding by using AI document analysis to classify onboarding documents and extract data from things like resumes, tax forms, and identification paperwork. By processing these documents with AI, you save human workers from having to spend hours entering data into HR systems and make your onboarding process more consistent and accurate, ensuring compliance with regulatory requirements.

Legal documents aren’t exactly known for their punchy, readable language. Spare your teams the legalese by managing contracts automatically. You can use AI document analysis to extract and organize key information from legal documents, like clauses, obligations, expiration dates, and parties involved. Of course, you wouldn't leave crucial processes like this solely to AI. Mixed autonomy is the future, and human oversight is still vital.

For insurance companies—with their essential but sometimes hard-to-differentiate product offerings—customer experience is especially important. AI document analysis tools create a more streamlined insurance claims experience by automatically extracting and validating data from claim forms, receipts, and medical reports for faster claims approval, reduced risk of errors, and an expedited reimbursement or settlement process.

Raise your hand if you love getting audited. Yeah, I didn’t think so. It might not be your favorite thing, but AI document analysis can at least make audit preparation go faster. It can be used to automatically extract and analyze information from policies and procedure documents, financial records, and even emails. Automating the document processing part of compliance audits makes the whole ordeal faster and more accurate, and it helps you be better prepared, reducing the chances you’ll run into non-compliances. 

[ How can reducing manual paper processes help your organization? Get the eBook, 6 Advantages of Eliminating Manual Document Processing, to find out. ]

The short answer is no. Most AI document analysis tools don’t require any AI or coding expertise. Because IDP is a low-code or no-code technology, all the AI, machine learning, and high-code infrastructure is pre-configured and happening behind the scenes. But professional developers do use IDP all the time to speed up their work, and they can usually add in hand-coded customizations to tailor the tool to very niche use cases.

Not if you choose a vendor that offers private AI. But not all vendors do. Lots of AI providers take all the data you give them and use it to train and improve their models . . . which means all your data is being used to inform the same AI models your competitors are potentially using. Products from many of the largest AI providers work this way, so tread carefully. Read the fine print. A truly private AI approach means your data is used to train your AI models only. And that has the dual benefits of keeping your data secure and making the insights the models provide even more tailored to your organization. 

You don’t have to, but combining IDP with other tools like robotic process automation (RPA) will definitely make your life easier. Lots of IDP tools are sold as part of a platform with other integrated automation capabilities. 

If your goal is speed, using IDP as part of a larger process automation platform is probably your best bet. When AI document analysis capabilities are built into a low-code process automation platform, users can work in a visual, drag-and-drop environment. They can point and click to add AI document processing, RPA, business rules, and other design objects to process models that orchestrate their workflows. It’s the fastest way to incorporate IDP into your organizational processes rather than trying to use it in isolation for individual tasks.

Clicking and dragging to add an AI skill to an Appian process

[ Get the eBook for more than 200 ideas for how to use RPA and IDP, with use cases for multiple functions and industries. ]

If you can answer yes to two or more of these questions, you should consider checking out the guide below to help you get started with AI document analysis. 

1. Does one or more of your organizational processes involve dealing with a high volume of unstructured documents?
2. Do you or your team spend part of your time each week manually classifying or pulling data from documents? 
3. Is one of your goals to increase the speed of your organizational processes? 
4. Are you interested in using AI at your organization but concerned about data privacy?

If two or more of these questions resonate, check out the practical guide to implementing private AI, which discusses how AI document analysis fits into a wider AI adoption strategy.