Execution beats strategy in every field, including generative AI (GenAI). In this discussion, George Fraser, CEO of Fivetran, Bob Muglia, former CEO of Snowflake, and Steve Jones, EVP of Capgemini, explore the key challenges and solutions for developing mature, production-ready generative AI models. The focus isn’t just on clever algorithms or massive data volumes — it’s on effective data curation and robust data management practices.
 
The future of GenAI remains open, but clear trends are emerging. As Jones notes, “The technology is going to keep evolving. Every week there's a new model. Every day there's a new variation and a new piece. But one thing I can predict is if a company can rely on AI to really make decisions and forecasting and simulation ahead of the market, they're going to win against somebody who can’t.” 
 
Muglia also highlights the growing importance of knowledge graphs: “A knowledge graph is simply a digital representation of human created knowledge that can be understood by a machine. We're still at the point where that is quite nascent in technology, but I think it's going to become much more important over the next couple of years.”
 
Takeaways from the conversation:

  The future of storing structure and semantic meaning in knowledge graphs
  How organizations can master data curation and other data management practices
  Understanding the limitations of relying on a single AI model for all analytics needs