Enterprise AI Architecture Series: How to Build a Knowledge Intelligence Architecture (Part 1)

Since the launch of ChatGPT over two years ago, we have observed that our clients are increasingly drawn to the promise of AI. They also recognize that the large language models (LLMs), trained on public data sets, may not effectively solve their domain-specific problems. Consequently, it would be essential to integrate domain knowledge into these AI systems to furnish them with a structured understanding of the organization. Recently, my colleague Lulit Tesfaye described three key strategies to enable such knowledge intelligence (KI) in the organization via expert knowledge capture, business context embedding and knowledge extraction using semantic layer assets and Retrieval Augmented Generation (RAG). Incorporating such a knowledge intelligence layer into enterprise architecture is not just a theoretical concept anymore but a critical necessity in the age of AI.