Content intelligence 101
The importance of data in these applications is not how much companies have, but how effectively they can contextualize it for gaining insight and taking appropriate action on it. Companies can take data from any source to find answers that enable cost and time reductions, optimized offerings, and smart decision-making. Recognizing an invoice, receipt, or other document is something that humans learn to recognize and take specific actions, and now AI with ML can also learn. However, so many AI-enabled technologies reproduce the confirmation bias of data, building better data capture mousetraps and sidestepping the human process dimensions of the content.
Use AI to make better decisions
AI technologies like natural language processing (NLP) and ML are used to ‘read’ information and apply experience and skill to make decisions on it. NLP, for example, reads content as humans read it, making it well-suited for applications that include information extraction, question answering, machine translation, and report generation in several industries including automotive, high tech, consumer goods, and more. AI "learns" by being trained by humans to read large amounts of complex information similar to trained workers. It can distinguish various forms containing exceptions and be applied to different situations.
NLP can make entity recognition in documents far more effective. An "entity" is a relationship of different types of information used for decision-making. For example, a business (entity) takes action (entity), prompting further action (entity) from the decision maker (also an entity). Making connections between data fields from different sources and applying intelligence to it enables content to solve problems at the heart of business processes and engagements. NLP is particularly well suited to entity identification and interpretation because it is trained to read language as humans do. But it goes beyond simple Named Entity Recognition (NER) and must intelligently dive deeper into segmentation, and even clause detection before entity extraction in its proper context can take place.
In banking, for example, the transition away from LIBOR has posed a global problem. Banks rely on armies of trained legal reviewers or consultants to sift through thousands of documents that mention the word "LIBOR" and include terms specific to the LIBOR standard. If automation is used for this, it is limited to simply searching and finding fields that contain LIBOR, but not necessarily the impacted entities and nature of impact. NLP can help banks get a better handle on this challenge by making the effects of LIBOR terms on other business entities visible and more easily addressed on a large scale.
In a compliance scenario, if companies want to understand where their exposure lies, they have to bring in intelligence to understand what’s happening with the fields in their documents, what entities are affected, and how. This is where digital "content skills" are key. Rather than solely extracting data with OCR, content skills apply trained AI to apply human-like problem-solving skills to complex types of content—invoices, first notice of loss, a loan application, or any document in a process. Moreover, content skills are both process and human aware, acting as virtual assistants to get content work done on an enterprise scale. Today, in our low code/no code world, these digital skills can be containerized and available in marketplaces for knowledge workers to use as needed in their intelligent automation platforms and existing applications. Combining AI-based content skills with process helps create a smarter way for enterprises to process documents and accelerate the business outcomes.
The next phase in our digital revolution will be moving from strictly data-driven to AI-enabled processes. AI-enabled computing tackles complex content and the skilled human shortages at the same time. Embracing AI technologies, such as NLP, help us process and interact with unstructured information more effectively than data alone, giving professionals the chance to contribute in meaningful ways that are complementary to the process. And the more efficient organizations become, the bigger opportunity they have to stand out from the competition. AI can radically change enterprise computing, but only if it sheds its confirmation bias that its use is "all about data."