Artificial intelligence has been put to amazing use providing great abilities for recognition, pattern and anomaly detection, predictive analytics, autonomous systems, hyperpersonalization, and goal-driven systems. However, AI systems can't do anything at all without access to data to train the machine learning models. And much of that data is locked in documents in paper or electronic form or in human-controlled processes. Often, a necessary first step to making any AI project happen is simply getting those documents and processes out of paper and human-based forms and into digital forms that a machine can understand. The notion of converting these analog assets into digital forms is known as digitization in the context of documents and information, and digitalization, in the context of processes and human-based activities.

In an effort to get from point A to point B as quickly as possible, many companies jump into automation without considering the bigger picture. They adopt one tool to solve a problem, and then another one to handle a different set of challenges. The end result is that organisations use multiple tools and technologies – many of which don't co-operate and collaborate with each other – to handle different parts of a larger process. What else causes friction in the business journey? Processes that exist but haven't yet been automated.

Parascript has introduced a data location, extraction and verification software solution that deploys template-less, neural network-based document extraction. Parascript says it has'productised' it's machine learning platform to support custom-developed recognition projects with much quicker turnaround than traditional rules-based approaches. The result is significantly faster production with more reliable and refined results. "Machine learning offers a whole new set of opportunities for organisations across many industries to more precisely streamline their operations and deliver rapid, accurate data to their clients," said Greg Council, Vice President of Marketing and Product Management. Traditional recognition and capture solutions often successfully use business rules to process information.