In this special guest feature, Sergio Suarez, Jr., Founder & CTO, TackleAI, discusses guidelines for data processing and data automation professionals offering insight into the role of AI in dynamic data discovery. TackleAI is a dynamic data discovery and extraction platform powered by AI. Sergio is an AI expert and entrepreneur with experience leading engineering and technology teams at Red Crest Solutions, Mercury File, CETA and dibit.
The definition of Artificial Intelligence varies depending on who you ask, and although there are countless companies claiming to offer an AI-powered automated document processing platform, 95% of the world’s data still isn’t being processed with true intelligent process automation.
If you were to ask one of the few companies that have real AI-powered data automation process, their definition of it would be very similar.
A data automation process that is truly powered by AI can process dynamically unstructured or semi-structured data, as well as documents it has never previously seen. This includes extracting data from document types that are new to the AI.
True AI processing is continuously learning. It learns from its own “yes-or-no” questions and corrected errors. True AI that powers an AI data automation process keeps getting better and faster without humans having to be heavily involved.
Another critical element to a true AI data automation platform is that the AI is trained by the company that created it. The responsibility of training and improving the AI automation should not be on the people who use it. The AI itself should be able to evolve, adapt, and learn on its own.
Many deep learning tends to be more academic and less practical in everyday business use. Deep learning models require large volumes of data to train and are expensive to create, which makes it difficult for smaller businesses to use. However, models created for AI data automation are much broader and easier to adapt. Their scalability and flexibility allow businesses of all sizes to use it, since the business does not need to train the AI on any particular data or documents.
Intelligent document processing is by far the fastest and most cost-effective way to analyze, extract, and process thousands of documents in only minutes. It keeps manual data entry costs, resources, and errors to a minimum. It allows companies to achieve better data transparency and control. It is also poised to easily and inexpensively incorporate updates and future advancements in this rapidly changing area of technology.
Here’s some terminology to be aware of that will indicate if you are really using a true AI Data Automation Process:
Robotic Processing Automation (RPA)
Although often touted as AI, this isn’t a true AI data automation process. RPA is looking for coordinates and data it has already seen. As the user, you must train it. It’s comprised of tools built to help you train the AI. RPA requires re-doing your workflow and creates a monstrosity of procedures.
AI Components
When a company says “AI Components,” that really means human-processed data – with the assistance of AI. It’s what is referred to as “Meatware.” There are other terms to be aware of, such as “Intelligence RPA” or “Human in the Loop”; essentially, they mean the same thing. We have nothing against humans. It’s just important to distinguish between how slowly and inaccurately humans process data compared to the hyper-intelligent and fast-processing power of AI – trained to think critically like a human, but without human error.
Zoning
If processing requires zoning, that means it needs forms that never change. It will only be able to extract data from the same exact forms every time. Slight variants in a document would lead to much lower accuracy. That is not true AI processing.
Digitizing Documents or Making Digital Documents
This would be a bad term to search for when looking for intelligent document processing. Think of your scanner on your home printer. That can digitize documents. You will wind up with services that don’t process usable data.
It’s important for businesses to be able to determine if a company they are considering is offering true AI data automation processing. Here are some questions businesses can ask, and the answers they should look for:
Q: Can your AI process dynamically unstructured and semi-structured data and documents it has never seen or processed before?
A: Yes.
Q: Does the AI need to be trained?
A: No.
Q: What percentage of the data document processing will work on day one, and before any training?
A: All of it.
Q: Is your software developed in-house vs using other third-party company tools?
A: We create our own software and train our own AI.
Q: How many examples will you need?
A: At the most, a few thousand. But we can work with as little as 250 if that’s all you have.
Q: How will I be charged?
A: You will only be charged for what you are actually using.
Q: Will I have to change my workflow?
A: Our AI data automation process can incorporate your current workflow. We automate your existing RPA.
There are so many companies and industries buried in their big data, and in desperate need of an AI data automation process. Large real estate companies, healthcare, legal, logistics, financial, and government top the list. You would be hard pressed to find an industry that wouldn’t benefit from an AI automated document processing platform.
Most companies are aware they are behind the times, but don’t know where to begin. That’s another reason true AI in automated data processing will be key in helping them make the transition seamless and cost-effective.
There are many crossovers in AI-processed data. Things the AI learns in one industry can help it quickly learn within another. You can only find that kind of Artificial Intelligence in real AI-powered data automation processing.
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1
Leave a Reply