The hype cycle for GenAI is so yesterday (where insights are gained through natural language responses, not in-depth manual research from possible irrelevant sources). Today the hype cycle is focused on Agentic AI (where actions combined with human guidance perform routine and structured tasks). But not so long ago the hype cycle was focused on obsoleting on-prem data infrastructures and moving workloads to the cloud.
Why have we moved on from this topic?
Why talk about routine data engineering, the foundation of data science/advanced analytics/agentic AI, when you can talk about exciting profit optimization or top line sales uplift through business use cases that deliver customer life time value insights to retailers, or improving industrial productivity though predictive maintenance or improving commercial effectiveness of Life Science enterprises by generating real-world evidence. Money talks!
David LeGrand
- Data Engineering
- Databricks