SUTD is opening CoLab so people build with AI, not only know it

Confidence with AI is not something people pick up by reading about it. It is built when they redesign a real workflow, decide where the tool belongs and where it does not, and then take a prototype far enough that someone can run it. That is the argument behind SUTD CoLab.
The Singapore University of Technology and Design has opened CoLab at the Lifelong Learning Institute, with strategic partners AvePoint and MaivenPoint and support from the Skills and Workforce Development Agency. The space is framed as somewhere "all can innovate with AI". Under a Learn, Build, Deploy model, participants learn how AI applies to real challenges, build solutions that combine the tools with their own domain expertise, including physical AI, and get last-mile help from AvePoint and MaivenPoint to put prototypes into enterprise systems.
The year-one aims are concrete: train 2,000 working professionals and help more than 100 enterprises. The initiative sits alongside Singapore's national push to help 10,000 enterprises adopt AI and 100,000 workers become AI proficient. Underneath the model is a human-AI-human pedagogy that sets out which tasks stay with people and which people and AI tackle together. The flagship is a three-day Design·AI prototyping workshop through SUTD Academy, where participants spend the first day exploring use cases in their own fields and then two days tackling problems from their own work.
Professor Phoon Kok Kwang, SUTD's president, put the message in one line: "build with AI, not only know AI." Senior Minister of State Tan Kiat How, from the Ministry of Digital Development and Information, officiated the launch. Dr Tianyi Jiang, co-founder and chief executive of AvePoint, added the commercial stake: buying software is simple, but translating it into performance requires people who understand the workflow and know how to transform it.
That is the same reason our AI Design Sprints end with a tested prototype and a decision rather than a certificate. Knowing AI is cheap. Building with it, on a problem someone already owns, is the part that shows up in the work.
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