With their vast potential and capabilities, AI solutions have become a go-to for businesses looking to automate repetitive processes. However, the portrayal of AI in media has also given rise to a slew of misconceptions and misinformation that threaten to tarnish its reputation. In this article, we'll debunk five of the biggest misconceptions about AI-based data extraction technology and how it can be deployed.
There's a common misconception that AI-powered data extraction solutions are only useful for simple documents. However, these tools use a sophisticated understanding of language and visual structure to understand documents of any complexity.
For example, when the technology extracts data from a bank statement, it understands that a sequence of numbers in a row on the page refers to the transaction amount: and is not just an arbitrary string of numbers. AI software can also identify the document as a bank statement by recognising information such as the transaction reference, date, type, and name.
As a result, AI can easily handle documents with complex tables and unstructured data alongside other visual complexities such as handwriting or poor-quality scans.
Intelligent data extraction technology was actually created to replace the limitations of Optical Character Recognition (OCR) software, which was purpose-built for simple and unstructured data. OCR technology scans documents and visually matches the text with templates of characters, making it useful for documents with static information, such as cheques or government forms.
Some vendors claim to offer an 'intelligent' AI solution that is a slightly more sophisticated version of OCR. Before using our services, some of our clients experimented with these kinds of solutions: "OCR software with some rules bolted on," as one of our clients eloquently described them.
However, in terms of innovation, AI-based data extraction is light years ahead of OCR. Because of their machine learning and AI capabilities, AI-based data extraction tools can not only contextualise data but ensure that errors in interpreting data are never repeated. So, AI-powered data extraction isn't just a passive form of data capture: it is a dynamic and continuously learning technology.
Some might assume that integrating AI-based data extraction technology with existing company systems is challenging due to its advanced nature. Originally, this was true: when first invented, AI-powered data extraction solutions had to be installed on-premise, requiring significant resources and expertise. However, the rise of online cloud-based software has revolutionised AI data extraction and made it accessible for all businesses.
Now, intelligent data extraction software is available for integration in multiple ways. For example, data extraction tools can be implemented via a REST API or simply by directly uploading documents into an interface. Another option for integration is using a no-code solution, such as Workato, which can effectively construct an automated workflow.
There is no right or wrong way to integrate AI-based data extraction technology; only what is most convenient for your company's current workflow.
Let's say you've chosen a method of integration. You might assume that installing, training, and operating the technology would require assistance from your IT department. However, that's not the case.
One of the benefits of AI-based data extraction is that you can train and use the model yourself without having to resort to the IT team for even simple changes. Top-tier AI software always features a user-friendly interface, allowing minimal friction. With the user interface (UI), you can validate data and identify the origin of data points. The UI also provides a bridge for uploading the unstructured data and then download the extracted output.
Using an AI-powered data extraction solution should be empowering rather than burdensome. Even though you're outsourcing the technology, you can still retain in-house control over training and using the model.
While training an AI model to meet your company's requirements can be empowering, it's important to acknowledge that the extracted data may not be immediately 100% accurate. The model can learn quickly by training the model on a range of documents from which you need to extract data, such as invoices, bank statements, and financial statements.
Typically, it takes around 200 documents to train the model to achieve complete accuracy. However, once the model is fully trained, you can expect to enjoy benefits such as instantaneous extraction. If you have concerns about the training process, it’s important to initially mention this to the AI provider, who can give a realistic training period based on your company’s requirements.
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Overall, as the AI industry continues gaining traction, it's important to assess the advantages and practical implications of AI-based data extraction solutions objectively. To discuss AI data extraction technology for your company, book a demo or email hello@evolution.ai for more information.
Thinking about trying an AI-powered data extraction solution? Check out our other blog posts:
How to extract financial data from PDFs
How to Extract Data from Bank Statements - Five Things to Consider
