How Mastercard is Building AI-Ready Data Products

At Mastercard, governance succeeds because engineering and enablement don't work in sequence — they co-own it as a dual mission. Vivek and Fabien share how their partnership balances technical precision with user adoption, embedding context into every data product while curating marketplace experiences that make AI use cases like text-to-SQL and conversational search actually work at scale.

Key takeaways:

• Co-ownership between engineering and enablement prevents governance from becoming either too technical or too aspirational

• Engineering builds automation and ensures scale, while enablement drives adoption and curates user experiences

• Shared responsibility means butting heads when needed but succeeding or failing together as one team

• Context embedded by design requires both technical metadata automation and human-centered product thinking

• AI-ready data products emerge from the tension between what's technically possible and what users actually need