In this interview from Phi Moments @ Google Cloud Next 2026, Mehul Trivedi, group vice president of technology at John Wiley & Sons, joins Debopriyo Nag, practice lead for data and analytics at Quantiphi, to talk with theCUBE's Rebecca Knight about how a 219-year-old publishing company is dismantling a decade of fragmented data infrastructure to build a unified, AI-ready lakehouse on Google Cloud. Trivedi details how Wiley's legacy ecosystem — built across disparate business units over 10 years — created a data debt that blocked meaningful AI and analytics work. He outlines the three-part case for choosing Google Cloud and BigQuery: cost consolidation, a fully integrated AI and ML stack and open-source flexibility through formats such as Parquet and Iceberg. Nag explains how Quantiphi deployed its in-house AI agent platform, Codeaira, to automate query translation, validation and migration across 300 terabytes of data and 30,000 tables — compressing what typically takes two years into six to nine months. The conversation also explores how the migration is reshaping Wiley's workforce. Rather than displacing engineers, Trivedi notes, Gemini-assisted tooling frees teams from routine pipeline and reporting work so they can focus on building semantic models, uncovering new data relationships and developing future-ready products. Nag reinforces a lesson drawn from years of enterprise migrations: lifting and shifting data to the cloud without first contextualizing it will not unlock AI value. From piloting a scalable lakehouse architecture to positioning Wiley for the next decade of AI competition, both guests make clear that data readiness — not model selection — is the decisive factor in enterprise AI success.