
Elevating AI to a national infrastructure priority speeds industry adoption, boosts productivity and positions Singapore as a regional AI hub.
Singapore’s decision to place the prime minister at the helm of a dedicated AI council signals a strategic shift from fragmented experiments to coordinated national policy. By centralising regulatory review and sandbox creation, the council can streamline approvals, reduce compliance uncertainty, and align research funding with commercial objectives. This governance model mirrors the approaches of leading AI economies, where high‑level political sponsorship accelerates cross‑agency collaboration and signals long‑term commitment to the technology.
Fiscal levers are a core pillar of the new strategy. The 400% tax deduction on qualifying AI expenditures, capped at S$50,000 per assessment year, effectively turns every dollar of AI spend into a five‑fold return for businesses, encouraging both large corporates and SMEs to invest in data pipelines, model development and workforce upskilling. Coupled with an expanded Productivity Solutions Grant that now covers a broader suite of AI‑enabled tools, firms gain tangible financial support to move beyond proof‑of‑concepts toward full workflow redesign. These incentives also address talent bottlenecks by making it more affordable to hire data scientists and retrain existing staff.
Regionally, Singapore’s AI push aims to cement its status as a digital‑innovation hub in Southeast Asia. The four mission sectors—manufacturing, connectivity, finance and healthcare—target high‑value industries where AI can deliver measurable productivity gains and new revenue streams. Success will depend on overcoming data‑readiness challenges and ensuring smaller enterprises can access the same resources as larger players. If the council can deliver clear regulatory pathways and scale the "Champions of AI" programme, Singapore could set a benchmark for AI‑driven economic infrastructure, attracting foreign investment and talent while fostering homegrown AI champions.
February 15, 2026 @ 1:18 pm · By Michael Wong
Singapore will set up a new National AI Council chaired by Prime Minister Lawrence Wong to steer policy, align government agencies and push the city‑state’s next phase of artificial intelligence adoption, Wong said in his Budget 2026 statement.
Wong said the government would “review regulations and create sandboxes” so firms can test AI innovations “safely and responsibly”, while better coordinating research and development, regulation and investment promotion so agencies “act in concert”.
The prime minister, who is also finance minister, said Singapore wants AI to move beyond pilots and isolated experiments, announcing a new set of “national AI Missions” aimed at AI‑led transformation in four sectors: advanced manufacturing, connectivity, finance, and healthcare.
Wong cautioned that meaningful AI transformation is difficult even for large multinationals, requiring companies to organise data, rebuild systems, redesign processes and jobs, and retrain workers.
He pointed to DBS and Grab as examples of Singapore companies moving decisively, and announced a new “Champions of AI” programme to support firms seeking end‑to‑end business transformation using AI.
To broaden adoption, Wong said Singapore will expand the Enterprise Innovation Scheme, which provides businesses with 400 % tax deductions on qualifying expenditure, to include AI spending for the Years of Assessment 2027 and 2028, capped at S$50,000 per year of assessment.
He also said the Productivity Solutions Grant will be expanded to support a wider range of digital and AI‑enabled tools for companies of all sizes.
The move builds on Singapore’s wider push to strengthen national AI capabilities, including government plans announced in January to invest more than S$1 billion in public AI research through 2030.
By putting the prime minister at the helm and naming mission sectors, Singapore is signalling that AI is now treated as economic infrastructure, not just a tech policy file, with a focus on execution, faster diffusion into industry, and regulatory pathways that reduce uncertainty for deployment.
The main test will be whether smaller firms can move from point solutions to genuine workflow redesign, given data‑readiness constraints and talent bottlenecks.
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