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The Future of Intelligent Document Processing (IDP)

Blog: ProcessMaker Blog

Think you have a tight handle on your organization’s data? Not so fast: experts project that the amount of unstructured data creeping into your information ecosystem doubles every 90 days

Look at it this way: say you have 500 sheets of unprocessed documents sitting on your desk. That’s your standard ream of copy paper. With everything else crowding your to-do list, dealing with it creeps further and further onto the back burner. 

Left unchecked, the stack would grow to 43 feet in just two years—about the length of a yellow school bus. 

And that’s just your desk. Multiplied by 1,000 employees at a small or mid-market enterprise, the stack grows so high that a Boeing 757 would need to dodge its towering height. 

That doesn’t begin to count the number of invoices, applications, contracts, and other documents stagnating on company shared drives, document management systems, and in forgotten desktop folders. It’s no surprise that apps and tools like Intelligent Document Processing (IDP) that make document processing faster, easier, and more cost-effective top the list of must-have tech. But as unstructured data continues to skyrocket, so do innovations in IDP.

How intelligent document processing rose to popularity

Despite its recent celebrity status in the tech sector, the technologies that underpin intelligent document processing are not entirely new technologies. 

Over a hundred years ago, one inventor harnessed Optical Character Recognition (OCR) to convert handwritten notes into the dashes and dots used by telegraph operators. 

Fast-forward a few generations, and iterations of the technology are everywhere: using your smartphone, Amazon can read the verification code off gift cards left by Santa, and cops can snatch license plates off videos of vehicles speeding away from a bank robbery. And right in your back office, OCR extracts numbers from invoices to populate accounting systems or drops serial numbers from warranty complaints into an inventory manager. 

So how did IDP become the talk of the tech town? 

Like the earliest versions of the iPhone, even the most culture-melting inventions need other technologies to catch up before they can make their splash. Prior to the rise of always-on internet and the societal expectation to be ‘always reachable,’ early stabs at iPhone-esque technology (like the Magic Link) fell on deaf ears. 

Similarly, IDP hitched its wagon to the automation suite. As organizations adopted new software and technologies, like business process automation and artificial intelligence, IDP gained new footholds in the expanded automation universe. 

Now, IDP thrives healthily across every industry. Organizations use niche software to process invoices and extract hallowed passages from yellowing books into historical archives. Larger behemoths seamlessly integrate IDP into broader software offerings like business process automation. So where is IDP headed from here? 

The future trajectory of IDP

The future of intelligent document processing is an exciting area of technology with endless possibilities. In the next few years, we can expect to see tremendous advancements in the way we interact with, analyze, and process large volumes of information contained in documents. Here are five trends guiding the future of IDP innovation. 

1. Documents integrate deeper into workflows

Platforms like ProcessMaker do more than sort and store documents—and their capabilities will only expand from here. IDP can parse needle-in-a-haystack details from incoming documents and insert them into a workflow. For example, a bank can rip personal identifiers like names and birthdates from a passport snap and insert the findings into OFAC or other sanctions lists that check for nefarious backgrounds. 

The ultimate goal for any organization exploring automation should be process orchestration. Like it says in the name, you can think of process orchestration as the beautiful experience of a coordinated orchestra. Instead of a bunch of tasks raucously hammering away at disjointed notes, every task plays the right notes and in the right order, coalescing into blissful harmony. 

From an IDP perspective, process orchestration will further blend data and documents into finely tuned-workflows. Instead of just filing an application away or sending it up the rungs for approval, information folds into processes as a live data point. Contract dates, and company locations—even the excitement expressed in a customer testimonial—can become actionable intel that triggers workflow tasks. 

2. Training models are the new gold standard

Scaling up your business and signing new customers all result in an explosion of incoming documents. With the increasing focus on digital transformation, there will be greater demand for tools that can process large volumes of documents efficiently and accurately. 

This is a good thing for organizations leveraging IDP—the more information available, the better the training set. The influx of documents combined with human input will continue to hone the accuracy of machine-learning models. Access to large swaths of samples will become the new gold standard, a valuable asset that IDP vendors can use to attract new clients. 

3. Machine learning models tackle new forms of media

Documents are all inherently paper-based, but as a society, we’re racking up unstructured data more than ever before. This type of data includes everything that doesn’t follow a rigid template: think the constraints of a checkerboard vs. an open-world video game.

TikToks, webinars, Twitch streams; all of these new forms of media contain a wealth of market insights invaluable to an organization’s growth potential. But videos and images follow few guidelines—there’s no template for what kind of information it contains, making it challenging for computers to codify. 

The back-end technology evaluating both written and spoken language is called natural language processing (NLP). NLP is a subfield of artificial intelligence, focusing on helping machines understand and process the quirks of human language. NLP can pick up on idioms, discern between adjectives and nouns, and pluck specific entities like a customer’s first name from a larger block of text. NLP ninjas are also honing its soft skills—the technology can skim a testimonial or Tweet and grade how positive or negative it leans. 

With the rise of NLP, we can expect to see more sophisticated document processing tools that can understand the meaning of the text they are processing, rather than just the structure of the document. This will enable new applications such as automatic summarization, translation, and question-answering.

4. Improved accuracy

Large language models (like the media darling ChatGPT) are expanding computers’ situational awareness. Computers can now understand context: they’ll pen you a love letter or give you the eli5 low-down on quantum physics. It’s NLP on steroids—worth more than playful news stories of disappointed customers using LLM scripts to negotiate refunds. 

LLMs will help IDP systems gain further context into the documents it ingests and can improve accuracy and enable new generative AI features like generating text or images. You might be able to ask it to write you a new sales contract based on clauses of your pre-existing agreements. Ask what contract terms were inadvertently omitted from a draft when a particular client agreement expires, or what customers share a particular SLA condition. In the future, you’ll be able to converse with your documents and unravel a fresh bounty of insights today’s search bars could never imagine. 

The future of intelligent document processing is full of exciting possibilities and holds great potential for organizations looking to streamline their processes, reduce costs, and improve their ability to make informed decisions. Whether it’s through machine learning, NLP, or improved user experience, we can be sure that the next few years will see significant advancements in this area, making it easier and more efficient than ever to process and analyze large volumes of information contained in documents.


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