A data-driven look at enterprise AI adoption, showing how organizations move from experimentation to real productivity gains and new capabilities.
The article "Beyond RAGs: Building Actually Truthful AI Harnesses" discusses the limitations of Retrieval-Augmented Generation (RAG) systems, emphasizing that retrieval alone does not guarantee evidence for AI claims. It explores methods for constructing AI systems that can substantiate their statements, moving beyond simple retrieval to more robust proof mechanisms. The piece highlights the importance of developing AI that can verify its own outputs rather than merely retrieve information.
By Ari Joury, PhD
The paper introduces a synthetic data generator that creates fully consistent, fictional enterprises—complete with workforce, customers, sales, support, and communication records—without relying on any real dataset. It validates realism through a five‑axis scorecard, an adversarial detector, and soundness checks, achieving a mean realism score of 99.1 across 23 generated companies. A second generator produces relational databases from business questions, ensuring qualifying rows and exact labels, and is available as a hosted service and containerized simulators.
By Benjamin Gruenbaum, Doron Porat, Assaf Natanzon, Roy Zavida, Chen Dinachi, Or Itzahary, Omer Niv
A practical enterprise AI architecture with data agents, AI-powered QA, and AI governance. The post Many Companies Use AI.
By Jiayan Yin
Microsoft Research Asia – Singapore, launched a year ago, has built a solid foundation and strengthened collaboration with government, academia, and industry. The lab is exploring how frontier AI research can generate real‑world value. It continues to advance research, partnership, and talent for practical impact.
By Microsoft Research Team