Understanding what a trained model actually is, what it has learned, and what it is capable of is essential to using AI effectively. Rather than starting with a list of AI use cases, this session starts with the model itself and builds an intuition for where these systems are genuinely useful within a data estate.<\/p>\n
Most organisations aren\u2019t running on a greenfield data stack. They\u2019re running on legacy on-prem systems, years of accumulated business logic and pipelines that quietly keep the organisation moving. The challenge is working out where AI can add value without assuming you can replace the systems already in place.<\/p>\n
Drawing on experience embedding AI into an established enterprise data platform, I\u2019ll explore how a practical understanding of trained models can help identify opportunities across a mature data estate, including automated documentation and lineage, knowledge governance, drift detection and surfacing business logic hidden in stored procedures and tribal knowledge.<\/p>\n
Key Takeaways:<\/strong><\/p>\n\n