Seth Earley

Creating an ontology is an essential investment to prepare your enterprise to realize the benefits of AI and machine learning. Gone are the days when businesses should simply allow a number of small AI projects to blossom independently: for these projects to be competitive they need to draw on data from across the company, data stored in many different forms in many different systems and in different structures. An ontology defines these connections in a way that AI can take advantage of.

Business will be best positioned to build and apply practical business-focused ontologies if they 1. Identity pain points and friction in the current process 2. Define the desired future state 2. Understand what data is required to support that future state 3 Develop the organizing structures to harmonize and apply are most needed-before beginning to set the organizing principles for the ontology itself.

Cognitive AI runs on knowledge – expertise and content structured and organized for retrieval using virtual assistants, chat bots, and conversational commerce applications. But how do organizations build this capability cost effectively and sustainably? The answer is a “knowledge factory,” an approach that solves information problems today while providing a foundation for the future methodology for analyzing business problems, breaking problems into specific information requirements, developing and structuring knowledge components, and building out models for continuous learning and knowledge curation using automated and human in the loop approaches along with process metrics to quantify business impact. Organizations that do not build these capabilities will be caught flat footed as the industry progresses and competitors build sophisticated capabilities that enable higher levels of customer service at lower costs.