Artificial Intelligence (AI) is having fundamental impacts on so many different industries as well as our day to day lives. We can now use Siri and Alexa for multiple uses, we encounter customer service chatbots in our regular interactions with businesses, and there are even robots trading on the stock market. Data collection is just another area that’s being affected.
There are already systems and technology in place that run automated data capture processes to increase our efficiency in the workplace and uses the strength of AI as well as RPA (robotic process automation) to benefit companies and organizations. Data collection, data processing and data storage all three plays important role in getting the required task completed. Now, we can scan a document and without any additional work the machine will ingest the information, file it properly, and prepare and display it, leaving us to focus only on the exceptional cases. This allows employees to focus on more complex tasks while AI can complete the more manual and repetitive tasks.
We can take this even further, however. Data collection software that’s actually intelligent doesn’t need to follow a script, template, the right keywords, or even the right definitions and taxonomies. Instead, this intelligent software can pull out just the relevant information and process it in a way that makes sense, on a large scale and extremely quickly. This can be done no matter the size, format, or even language and symbols used.
The Changing Field of Intelligent Data Capture
Intelligent data capture is great software because it works by teaching the AI-driven core the method to perform a task – in this case, a data entry task. The AI software will go beyond regular software by picking up on additional context, information, and be able to interpret and understand and grow as time passes, such as learning different document types.
It also goes beyond that, by validating the data it’s collecting against the existing data, which adds an extra protective layer that can’t be easily duplicated. Intelligent data capture has been changed by AI in three main areas, which are classification, extraction, and validation.
This is also known as document sorting, and in these cases the AI-driven software will learn to understand and identify different types of documents once it’s seen a few examples and variations. AI becomes like a human in that it can read and analyze sample documents and understand the aspects that are subtly the same and different.
It doesn’t need to see every type of contract to recognize what a contract looks like, for example. This is great because to set up you don’t need to add as many rules and exceptions, which means you can have a lot more trust and confidence in the final classified product without having put in a lot of manual effort.
AI has completely revolutionized the field of data extraction for both unstructured and semi-structured documents, including forms written by hand. For example, invoice number identification can be completed easily whereas it normally requires using complex templates and keyword tags for specific fields and labels.
A human can look at the document and locate the invoice number easily, not matter what format the form takes, because they know what to look for. However, now an AI software can also do it without additional programming. It can go quicker and with fewer mistakes. The AI tool trains itself to be able to understand and capture the context so the extraction is extremely accurate.
AI engines can also extract complex data from tables that are broken or different. They can understand formatting and recognize patterns so that they can quickly locate the key data elements without a human to walk the machine through it. The human intervention is only required for exceptions.
Artificial intelligence can even go beyond that approach, by searching and validating the data extracted against information in another system. For example, it can identify a line item located in an invoice and then validate it against the purchase information that’s in another system. AI can search in many different ways (known as multi-way search) so it can use quantity, price, description, amount, and other pieces of information to match the item. The software can also make a reasonable deduction that something is the same item even if it’s not a precise match (i.e. if an abbreviation is used in one instance but not the other).
In the past, a data entry clerk needed to be hired for the precise purpose of data validation for exact matches. This is now minimized, if not completely eliminated, due to AI, so that employees can spend their efforts on strategic tasks.
The market for AI is booming and it’s thus far surpassed expectations for automating processes that are both complex and rule-based. The overall projection for AI market is that it will be worth 2.9 billion in 2021 (for scale, it was only worth 250 million in 2016). Where RPA fails is when there is too much variance, which is a real possibility in document data collection.
That’s why AI and machine learning can work with RPA to create dynamic variance networks. That’s a complicated way of saying the software will look at the overall data and how it relates to the rest of the document. Then, it can calculate vectors between the words on the document and target fields to fill the gaps left by RPA errors during extraction.
In addition, the AI software can monitor what the user is doing and correct it in the right way, negating the need of a human to intervene. The system is therefore getting smarter on its own without updates from human employees.
What Does it All Mean?
There are obvious benefits which we’ve seen here, but more than that, intelligent data capture software cancels out any guesswork for programming and setup. AI won’t replace humans, but will instead automate as much as possible to allow employees to move on from mundane tasks and only focus on the high-value tasks that require a human mind.
Author Bio: Editor Aimee Laurence works with Write My Essay. She focuses her research and publications on artificial intelligence, machine learning, and data collection. She is passionate about how industries are adapting to AI and how humans and AI can work together to improve the market.