Document AI: Unlocking enterprise document intelligence and actionable insights for data-driven decisions

The global AI market has already entered a new phase in the wake of the COVID pandemic. A major requirement for enterprises. According to recent reports, the annual AI software revenue will grow to nearly $100 billion by 2025.

What is Document AI?

Analyzing documents to unearth contextual meaning is an inherent human quality. Capturing data from a horde of documents of varying formats, like slides or posters, in advertising, infographics, and email, was beyond AI capabilities until now. Document AI uses Optical Character Recognition (OCR) and computer vision capabilities to recognize words, decode and interpret images, and other forms of media with 99% accuracy.

This holistic interpretation of documents is termed multi-modal image extraction, which extracts the pertinent information and converts it into structured data. It resembles how the human brain would have interpreted the same information. But, unlike humans, AI does that in a blink of an eye without compromising on the details.

5 Document AI benefits for enterprises

Document AI has found its way into various business use cases. With the help of NLP, AI document processing can bring tangible benefits to businesses, a few of which are described below:

Time saved

Document AI saves productive time by structuring unstructured data, indexing it, and ensuring easy searchability of the data. AI for documents has wide applications in insurance, healthcare, and other industries where piles of documents are analyzed regularly.

Optimal utilization of employee skills

With less time spent in the needle in a haystack search for pertinent data, employees have more bandwidth to use their inherent skills and expertise to cater to value-added services for which they were initially hired. This translates into better employee engagement and reduced turnovers.

Improved customer experience

With structured data easily searchable, businesses can witness massive improvements in customer service. For example, an insurance company takes hours to set up a customized policy by collating and evaluating all information about a customer’s unique circumstances. With AI document analysis, customers’ credit history, demographics, policy options, and possible exclusionary risks are highlighted in a searchable database; and setting up a bespoke policy can be done in a single phone call.

Improved document security

Data breaches have become a daily occurrence, thereby bringing security under the spotlight. Document AI can scan for sensitive information and automatically redact it when required. The same systems can be on the lookout for unusual activity, warning you of a possible data breach before it happens.

Unexpected business insights

Humans are smart individually but generally don’t deal well with scale. Artificial Intelligence is the opposite: the more data it has, the more unexpected insights it can produce. For example, AI can make correct medical diagnoses that trained doctors, miss. Likewise, when Document AI is fed with millions of business documents, it can extract unexpected insights for the company.

How does Document AI work for businesses?

Document AI can very well work for your business, however, depending on the following factors:

The breadth of AI: Evaluating whether or not Document AI can work with structure, data, and prediction problems unique to your business.

AI requirements: Companies need to understand their unique AI requirements. Most businesses expect a useful, integrate-able, and easy-to-consume AI.

IT readiness: This is an important factor to consider – the IT readiness of a company. Questions like “How will you scale? How can I integrate my documents? How do I bring the documents in? How do I pull the results out? What kind of security do you support?” are typical ones to find answers when bringing a new product into the data center.

Once these factors are in place, Document AI goes through the following steps:

Digitization of data: Company documents are a collection of different elements of varying sizes and formats, like graphs, charts, tables, logos and the text. Digitizing documents should be the first step before bringing in Document AI capabilities to address the data extraction clause.

Classification of data: Documents are classified and separated into specific groups after uploading those important paper files on a digital platform. So, if somebody uploads many documents, which is a collection of checks, invoices, purchases or sales orders, Document AI automatically separates them.

Analysis of extracted data: Once the data is extracted, it is important to add some sense. For example, take a form with the name “Alan” on it: just by looking at it, the AI doesn’t know what “Alan” means till it is paired with the keyword (field) “Name:,” printed on the left of the word “Alan.” So, the Document AI platform will do a full field value pairing to give meaning to the values in the document.

Evaluate content intent: Understanding the intent of the text blocks is essential. Whether it’s a termination clause in a legal document or explaining some medical procedure, Document AI can help by making a proper sense of the document data and the context of the information.

Comparison and analytics: Using advanced techniques and analytics, document comparison can understand the sentiment and historical implications and predict future outcomes and potential risks.

Converting into consumable information: Unless business users can consume the extracted data when needed, the whole point of processing documents and extracting information will have no meaning. Hence, actionable insights unearthed from unstructured documents are made consumable for businesses to use as and when needed.