The core idea
This keynote argued that enterprise AI's real leverage lies in data and organizational discipline, not the models themselves — the path to faster time-to-value.
"When creating AI in the real world, the data used to train the model is far more important than the model itself." — Andrej Karpathy
Building the foundation
Using the Data Science Hierarchy of Needs and the Data Engineering Lifecycle, it showed why a robust data ecosystem is the prerequisite for AI that transforms customer experience.
Governing responsibly
The session closed on responsible AI — accountability, transparency, and bias across data, models, and pipelines — referencing NITI Aayog and TEC frameworks, plus a three-step bias-assessment approach.
