Automate Your Data Processes With Intelligent Data Extraction

What Is Intelligent Data Extraction?

Think of this as a smarter, faster way to handle data. It uses natural language processing, artificial intelligence (AI) and machine learning technologies to automatically pull and process information from documents like invoices, credit notes and receipts.

While IDE might sound similar to Optical Character Recognition (OCR), the two are fundamentally different. Unlike intelligent data extraction, OCR lacks the ability to understand the context and structure of the information. It simply digitises what it "sees." This limitation often leads OCR to repeatedly extract data incorrectly without recognising the error. AI-driven data extraction, on the other hand, intelligently interprets and validates the information you provide.

Here’s a quick comparison to show you how intelligent data extraction performs against manual data entry and OCR.

Features	Manual Data Extraction	Optical Character Recognition	Intelligent Data Extraction
Accuracy > 95	✖	✖	✅
Processing time < 1	✖	✖	✅
Minimal human intervention	✖	✅	✅
Multiformat document processing ability	✅	✖	✅
Data interpretation	✅	✖	✅
Self-learning ability	✅	✖	✅

Common Misconceptions About Intelligent Data Extraction

Assuming OCR and IDE are the same isn’t just one misconception about automated data entry. There are several more myths that you need to stop believing:

It completely replaces human involvement

No, it does not.

While IDE uses AI to process and extract data, it’s still a tool. It very much relies on you to set the right parameters, guide the process, and validate results. Plus, we have empathy, emotions, cultural sensitivity, and intuition that AI cannot mimic.72% of firms say their executive leadership takes responsibility for AI usage. But here’s the thing: accountability isn’t a one-person job. Most businesses report having 3 or more people or teams involved. Even with AI, your team’s expertise is crucial to guide, oversee, and make the most of it.

IDE does not apply to complex documents

This is not true.

AI data extraction is incredibly versatile. Whatever format (spreadsheets, handwritten text, PDF, images or unstructured text documents) your data comes in, IDE can extract it with ease. According to research, an employee relies on 37 different tools for daily tasks, with 70% of them dedicated to handling unstructured data. With intelligent data extraction, you reduce the need for so many tools and extract data with accuracy as high as 99%. It is difficult to integrate into my business operations This used to be the case, but technology has advanced. With an annual growth rate of 20% in cloud-based systems, you don’t need complex setups or deep technical expertise. You can connect IDE directly to your accounting software, which allows the two systems to communicate and exchange data automatically. Many platforms also offer a drag-and-drop feature, or you can directly capture and upload the document for data extraction.

I need an IT department for the optimum use of smart data extraction

No, you don’t.

One of the benefits of this data extraction technique is that you can train and use the model yourself without having to resort to the IT team. While IT is involved initially, AI-based data extraction is designed to be user-friendly, so you don’t need a full IT team to get the most out of it.

My business is too small for automated data processing

This is definitely not true.

Sure, big companies dealing with tons of paperwork will definitely see major benefits from automation, but size doesn’t matter. Whether you are a start-up or large business, working in hospitality, retail or even in education, think how much time you would save by completely removing manual data entry. The decision to automate your document processes has less to do with the size of your company and more to do with where you are on your digital journey and the growth you’re aiming for. In fact, a global survey revealed that 91% of US respondents are confident that AI can prepare finance teams for disruption, while confidence in Europe ranges from 72% in the UK to 57% in Germany. It's clear that businesses worldwide see the value in automation, whether they are small or large enterprises.

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What Does The Future of Intelligent Data Extraction Look Like?

Although the prevailing misconceptions might make you hesitant to adopt IDE, it is evolving to make it easier for teams to capitalise on its value.

Currently, only 15% are using AI at an advanced level, highlighting the vast potential for its growth across the industry. Hence, future iterations of intelligent data extraction will focus on creating intuitive, user-friendly interfaces. This means your finance team will spend less time on training and have fewer barriers to adoption.

In fact, a report by the Bank of England predicts that AI-driven benefits will grow by 21% over the next three years.

So, how can you prepare for this shift?

Start by identifying pain points in your current workflows and communicating them openly with your finance team. Fostering transparent discussions about IDE adoption will encourage buy-in and its training. Finally, invest in a scalable automation solution that adapts as technology improves.

The key takeaway

If there’s one takeaway from this blog, it’s this: intelligent data extraction is the future for businesses across industries.

With IDE, you can automate workflows, enhance data accuracy, save costs, and gain insights, all while keeping your finance team at the heart of decision-making.