Artificial intelligence (AI) has been used within information-driven businesses for many years now. For example, think about forms processing applications where AI technology automatically extracts key data points while capturing and scanning a paper file or ingesting an electronic document. These applications are “trained” with a sample set of forms and then learns from these. This enables the AI technology to process potentially thousands of files with minimal human interaction.
Despite being around for many years, artificial intelligence is currently enjoying a resurgence. This renaissance is being driven beyond simple forms processing and the categorization of documents – it’s also being leveraged by digital asset management (DAM) solutions that manage videos, audio files, images and other types of rich content.
Taking Advantage of AI Today
Rich content like videos and images have been difficult to work with, in the past, and typically humans have had to “understand” the digital asset being stored in order to properly classify and manage it. This has often been a manual, inconsistent and time-consuming process.
However, advances in AI (fueled by an annual investment of around $30b from organizations such as Amazon, Google, and Baidu) are now enabling organizations to automate the process of classifying and extracting key attributes and data points from images, audio files, and videos in increasingly sophisticated and intelligent ways. Advances in Natural Language Processing (NLP) and Natural Language Understanding (NLU) enable enterprises to not only automate how content and data are identified and categorized, but it does so in a manner that is more consistent and accurate than manual approaches.
Artificial intelligence is also used by many businesses today to automate transaction-based processes such as invoice processing and claims management. These are use cases with strict controls relating to the use of templates, workflows, metadata management, access permissions and other clearly-defined rules – and these are the type of highly-repetitive processes that AI can automate and streamline.
Leveraging AI in the Very Near Future
There’s no doubt that many businesses are seeing measurable benefits by using AI to automate many information-centric processes that have been traditionally been managed in manual, inconsistent and error-prone ways. However, we’re now beginning to see AI expanding to more dynamic parts of the business – areas that do not follow regular patterns.
Early use cases leveraging artificial intelligence required large amounts of data for the AI technology to be adequately “trained,” which was inherently difficult when dealing with dynamic and ad-hoc processes. This is because most early AI deployments used a technique known as machine learning, which essentially looks at large data sets to establish patterns, and then builds rules and algorithms to “make use” of those patterns. This is very similar to the way in an adult learns a new language – we build our vocabulary and then work to understand and apply the rules. Anyone who has tried to learn a new language as an adult knows how hard this is.
The alternative approach is to learn like a child does – by observation and experimentation. By trying out new ways of forming and using language – over and over again. The “learner” often gets things wrong, but they learn more rapidly.
In AI terms, this technique is known as deep learning, and it offers significant benefits in environments that are more dynamic and where large training data sets do not exist.
Deep learning is the future for AI within non-transactional business scenarios, and it’s being used in many video, image, and voice processing applications today. The results being generated by these tools are now proven to be more accurate than humans in accurately classifying rich content, and this form of AI is making its way into more and more dynamic use cases within businesses.
It’s clear to see that AI will continue to play a big role in automating and simplifying all aspects of business operations. For organizations that manage substantial volumes of digital assets, artificial intelligence and deep learning technology will provide value beyond simple understanding and classification and will evolve to deliver more predictive and analytic capabilities. The question surrounding AI is no longer if an organization will be using it, but rather when, where and how.