Across Southeast Asia, generative AI pilots are stalling—not from a lack of model power, but from broken retrieval. Agentic RAG bridges this gap: autonomous agents that verify facts, enforce governance, and execute end-to-end workflows. For CIOs in 2026, this turns fragile experiments into auditable, scalable profit centres. With Gartner warning that 60% of AI projects will be abandoned due to poor data and weak controls, agentic RAG is no longer optional—it is the only practical path from pilot to production. In markets like Singapore, where data residency and compliance are non-negotiable, retrieval intelligence is now the bedrock of ROI. In this PodChats for FutureCIO, Ed Keisling, Chief AI Officer, Progress Software, discusses how CIOs and heads of AI across Southeast Asia, can turn AI pilots and POCs into profit-generating initiatives for enterprises in 2026. What is RAG?Given that most regional AI pilots never scale, what specific architectural weaknesses does agentic RAG fix that traditional RAG or fine-tuning cannot?In markets with fragmented data landscapes—legacy systems, multilingual content, and disparate cloud storage—how does agentic RAG ensure consistent, high-quality retrieval at enterprise scale?What out-of-the-box governance and audit trails does agentic RAG provide to satisfy both local data residency laws (e.g., Singapore’s PDPA) and board-level risk controls?For CIOs managing lean teams, how does agentic RAG reduce the operational burden of maintaining retrieval pipelines, monitoring hallucinations, and orchestrating multi-step agent workflows? How can agentic RAG help move beyond isolated use cases (e.g., customer support) toward fully autonomous, end-to-end processes spanning finance, supply chain, and compliance?As agents become more autonomous by 2027, what retrieval strategies will prevent cascading errors or unauthorised actions, and what should CIOs implement today to stay safe?(original 3) How should CIOs in Singapore and across Southeast Asia measure the ROI of retrieval intelligence compared to simply upgrading large language models?For regional enterprises without custom AI stacks, what vendor or open-source scaffolding for agentic RAG offers the fastest path from pilot to profit while preserving data sovereignty?What organisational, data, and leadership shifts must CIOs prioritise over the next 12–18 months to ensure agentic RAG transitions from a technical capability into a sustained source of competitive advantage?
