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14 articles
CIO Gill Haus of JPMorgan Chase emphasizes that successful AI implementation requires more than hiring engineers to write code. Instead, he prioritizes data quality, organizational readiness, and ethical governance as core fundamentals.
Learn how to implement data governance controls in PostgreSQL to enforce AI agent behavior at the data layer, including role-based access control, row-level security, and audit logging.
This article explains OpenAI's Computer History feature, which records user behavior data to enhance AI responses and discusses its implications for AI training and user privacy.
Karina Portugal has evolved from an advertising executive to a leading voice in financial trust and verification, addressing critical questions about data reliability in an AI-driven world.
AI agents require a strong data foundation to function effectively. Xebia's global CTO, Niels Zeilemaker, emphasizes that without proper data infrastructure, even advanced AI systems can fail.
This article explains the technical architecture of AI Search features and how a UK regulatory ruling is changing how publishers control their content in Google's AI systems.
A Google engineer has been charged by the DOJ for allegedly using confidential search data to place $2.7 million in bets on Polymarket, netting $1.2 million. This marks the second federal criminal case involving the prediction platform.
This article explains how different countries approach data governance, showing how the EU, U.S., and China have very different views on data ownership and use. Understanding these differences helps explain how global AI systems are developed.
Scotiabank has launched Scotia Intelligence, an integrated AI framework designed to unify data and AI operations while ensuring compliance and empowering client-facing teams.
Data quality is often neglected until issues arise, but as organizations scale, the cost of fixing inaccurate data multiplies. Proactive data governance is essential for sustainable growth.
As AI systems become more autonomous, the importance of data governance is gaining prominence. Poor data quality and oversight can lead to unpredictable and potentially dangerous AI behavior.
Learn how to create a cross-functional AI implementation framework that goes beyond just appointing a Chief AI Officer, following the 'magician' leadership approach that successful organizations use to maximize generative AI potential.
