By 2026, generative AI will be embedded across core enterprise workflows, shaping customer interactions, operational efficiency, and risk decisions. While adoption across Asia/Pacific continues to accelerate, scaling GenAI safely, responsibly, and at enterprise scale remains a significant challenge. IDC research highlights that by 2027, organisations that fail to prioritise high-quality, AI-ready data could see up to a 20% productivity loss, even as many move toward composite AI approaches to improve reliability and outcomes. For enterprises in regulated environments, this brings a critical shift—from focusing on model performance to addressing data and context risk. Without a connected understanding of enterprise data, relationships, and policies, AI systems struggle with accuracy, explainability, and trust. 

This executive roundtable brings together CIOs and senior leaders to explore why GenAI breaks down at scale, how organisations can move from model-centric to context-aware approaches, and the role of explainability and observability as key enablers in building reliable, governed, and production-ready AI systems powered by connected data and graph intelligence.