In most working environments, there are two things people tend to dislike: First, excessive meetings that prevent employees from making progress on their to-do lists. And second, paperwork. Whether it’s processing financial statements, reading inventory forms, or completing onboarding paperwork for employees, organizations have to deal with a slew of document types to keep operations running. While meetings can’t always be curtailed, processing documents can be offloaded with AI document extraction tools, freeing up significant time for employees.
AI document classification and extraction, also referred to as intelligent document processing (IDP), allows organizations to process both structured and unstructured documents (and even handwritten documents), then turn that information into usable data. Think of the time savings—employees don’t have to manually enter data into databases to make the information usable.
The key to making this approach work is to find strong AI document processing tools. But what sets the best of these tools apart from the rest? Read on to find a few must-have features and capabilities in document extraction AI tools.
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1. The ability to intake digital, printed, or handwritten documents.
While much of the world’s business is transacted online and over email, snail mail documents still pile up in many offices. Before artificial intelligence can process documents, it needs to be able to recognize these documents. It does so via optical character recognition (OCR), which converts a document or image into readable text. With optical character recognition, a document extraction solution can take a scanned, handwritten document and turn it into something a computer can read and pull critical information from. OCR forms the basis of all AI-driven document processing solutions.
2. Custom document classification models.
Traditional intelligent document processing (IDP) solutions had a major flaw: they were prebuilt. If you classified incoming documents, errors were common due to a lack of precision in the models. This led to human workers having to manually review results after the fact and reclassify some documents.
Modern document AI tools will let you leverage machine learning to build your own AI models for tasks like document classification. Simply upload a batch of business documents, then your platform can train an AI model to recognize these documents in the future. While the AI model can recognize a wide range of document types, this custom-model approach ensures that it’s able to pinpoint the exact document types and indicators for incoming documents so you can then extract the right information and funnel it to the right source. Plus, the right platform will let you easily version your models and create new models if anything changes such as the layout of an order form. See how Appian does this with our AI Skills.
[When you build your own AI model, you get the added benefit of increased data privacy. Learn more by reading Implementing Private AI: A Practical Guide.]
3. Connection to wider business processes.
AI document processing is task-based automation. While automating a task like this can generate real value for your organization, consider how you can use AI to design, optimize, and automate wider processes. In other words, while you could process bank statements or tax documents when onboarding new employees using intelligent document processing, you could also automate wider onboarding processes for things like sending new hire emails, requesting follow-ups, submitting requests for background checks, or enrolling employees in training. This saves even more time and frees up workers for more complex tasks.
The only way to do this is to choose an AI process platform that includes these document AI tools among a constellation of other automation and development tools that enable end-to-end process automation. This could include generative AI to quickly build user interfaces, robotic process automation (RPA) to solve routine tasks, and workflow orchestration to embed document processing into a wider process.
4. Strong data foundations.
Take document extraction to the next level by building a strong data foundation. Again, consider using a platform that includes a data fabric architecture and offers easy data connections. Data fabric ensures the seamless flow of data across various environments, software, and data systems by allowing you to work with data in a virtual layer (no need to move data or worry about database schemas changing and breaking anything). This means any information pulled from documents can be easily passed to other systems.
Plus, data fabric gives you a complete view across your data ecosystem, making it easy for employees to gain insights across datasets, not just those pulled from documents. When your process platform also includes easy, one-click API connectors to popular software systems, then it makes it much easier to move data directly from documents to other systems like your CRM or billing software.
Simplifying document processing.
Today, organizations no longer have to manually process a mountain of paperwork when there are excellent AI document extraction tools on the market. Leveraging these tools can dramatically alleviate the administrative burden on your teams and expedite processes across the organization. To really maximize your document processing, remember to search for document tools that offer strong OCR capabilities, the flexibility to develop customized models, and seamless integration into wider processes via an AI process platform, including one that offers a strong data fabric architecture. By adopting AI document extraction tools that encapsulate these features, organizations can not only streamline their operations but also free up workers, paving the way for innovation and agility.
This post mentioned how any document processing efforts need to be part of a wider platform that offers multiple automation tools for end-to-end process automation. Find out what other tools you’ll need to accomplish this by downloading the report, Gartner® Emerging Tech Radar: Hyperautomation.
