Artificial Intelligence may well be the most potentially transformative technology since the Cloud, but it's clearly become the reigning champion for Tech hype and media buzz. IBM's Watson – a "cognitive" computer capable of answering natural language questions - was developed to compete on Jeopardy, a popular quiz show. In 2011, Watson competed against world champions Brad Rutter and Ken Jennings before a TV audience of millions…and beat them. At the end, Jennings remarked: "I for one welcome our new computer overlords". In fact, the Watson that won Jeopardy was an outcome of decades of research in "Symbolic AI".

The starting point in Part 1 of this series was the fragmented "knowledge landscape" of most big companies. Information is everywhere but it mostly lives in autonomous silos, in different formats and suffers from "semantic incoherency". This is a huge problem for extending the use of AI in business, beyond the many "narrow" (i.e. To meet this challenge, I argued in Part 2 that we need to "connect up" different forms of enterprise knowledge, with the help of semantic technologies - such as ontologies and knowledge graphs - from the "Symbolic AI" tradition where meaning and reasoning take center stage. This concluding post will focus on business outcomes – the benefits that leading-edge companies around the world are already beginning to achieve, leveraging semantic graph technologies to integrate enterprise knowledge and transform knowledge work.