Singapore’s latest AI measures signal a more structured push to embed artificial intelligence (AI) across the economy.
At Budget 2026, Prime Minister and Finance Minister Lawrence Wong announced the creation of a National AI Council, alongside sector-focused “AI missions” and expanded fiscal incentives to accelerate enterprise adoption and workforce transformation.
Kok Ping Soon, chief executive officer of the Singapore Business Federation, says the Budget provides “clearer pathways to access new growth markets, anchor global value chains and build future-ready capabilities.” The establishment of a Prime Minister-chaired National AI Council and national AI Missions signals “a coordinated push to position Singapore as one of the world’s first AI-ready nations,” he adds.
However, for technology executives, the next challenge is execution.
Oliver Jay, managing director for international at OpenAI, says the opportunity lies in closing what he calls the “capability overhang or the gap between what AI can do and how it is typically used”. He notes that Singapore ranks among the top three globally for per-capita ChatGPT adoption. Converting that usage into sustained productivity growth will require better tooling, training and governance.
As for Ben King, managing director of Google Singapore, the roadmap “cements Singapore’s position as a global force in this space”, pointing to the combination of national coordination and hands-on AI skilling. Through expanded R&D and initiatives such as Majulah AI, he says Google will continue partnering with Singapore to build solutions that address local challenges while driving new growth.
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From pilots to returns
Early data suggests AI is already delivering financial impact for some enterprises.
Local organisations have invested an average of $18.9 million in AI over the past year, generating an average return on investment of 16%, with expectations that this could rise to 29% within two years, according to a recent research by SAP.
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“This illustrates that AI is already moving beyond concept into business impact,” says Eileen Chua, managing director of SAP Singapore, adding that clearer policy objectives, coordinated industry missions and structured support will help companies move from early returns to sustained value.
Yet readiness remains uneven. “Our research also shows that 70% of leaders are still unsure if they are capturing AI’s full potential, largely due to gaps in skills and data integration,” says Chua. Clean data foundations, modern cloud architecture and redesigned workflows will ultimately determine whether AI drives resilience or simply incremental gains.
Mark Tham, country managing director for Singapore at Accenture, says the new initiatives reflect a broader regional shift. “We’ve moved beyond experimentation into the era of enterprise-scale AI transformation,” he says, adding that scaling requires a secure digital core, modernised data foundations and workforce readiness — without which AI remains stuck in pilots.
According to Carlos Quaderi, head of Asia at Zoom, businesses are entering a more demanding phase. “It’s no longer enough to just ‘use’ AI. Organisations now expect their AI investments to produce measurable productivity,” he says, describing agentic AI systems as a turning point in embedding intelligence directly into workflows.
Meanwhile, Amit Khandelwal, UiPath’s regional vice president and managing director for Southeast Asia, believes sector-focused AI missions could help firms move “past isolated experiments” by integrating AI into core operational processes.
Lee Bo Han, partner for R&D and Incentives Advisory at KPMG in Singapore, says expanding the Enterprise Innovation Scheme to include AI expenditure lowers financial barriers, but cautions that widespread transformation “will require more than financial incentives — it necessitates a fundamental shift in mindset among business leaders.”
Data foundations become decisive
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Several tech leaders argue that data architecture — not algorithms — will determine who captures value.
Love Srivastava, Confluent’s regional head for Singapore and Greater China, cautions that AI performance depends on modernised data flows. “AI is only as powerful as the data behind it. Many businesses today face what we call the ‘AI context gap’, where models lack access to real-time, trusted data needed to generate accurate, relevant outcomes,” he says.
Remus Lim, senior vice president for Asia Pacific and Japan at Cloudera, adds that Singapore’s ambitions will move “only as fast as the data foundations underneath them.” Without secure and well-governed data, particularly in highly regulated sectors, even well-funded AI programmes risk stalling.
Security and governance pressures
As AI deployment accelerates, cybersecurity and observability executives warn that risk exposure will expand in parallel.
Andrew Kay, APJ director of systems engineering at Illumio, says AI readiness must extend beyond skills and incentives. “If Singapore wants to unlock AI’s full economic benefit, security must evolve alongside innovation, not behind it,” he says, noting that proliferating AI agents and automation will increase system complexity.
Data security will be another concern. “Two in five organisations in Singapore cite data loss via public or enterprise generative AI tools as a top concern,” says George Lee, Proofpoint’s senior vice president for Asia Pacific and Japan. Responsible governance and data integrity will therefore be foundational to sustainable adoption of AI.
James Greenwood, Tanium’s AVP of solution engineering, adds that the expansion of endpoints (from laptops to cloud systems and connected industrial devices) increases the importance of maintaining visibility and patching at scale, particularly across critical infrastructure.
Beni Sia, general manager and senior vice president for Asia Pacific and Japan at Veeam, frames the issue more broadly. “The question is shifting from ‘Where can we use AI?’ to ‘What must be true for AI to be safe and dependable at scale?’” he says, arguing that data trust and operational resilience will determine which firms sustain advantage as AI becomes embedded in daily decision-making.
Cost management is emerging as another strategic fault line. Rob Newell, senior vice president and general manager for APJ at New Relic, says organisations must ensure AI investments deliver commercial value while controlling operational expenditure. Focusing large language models (LLMs) on high-impact use cases and selecting fit-for-purpose solutions can help balance innovation with affordability, advises Newell.
Alexey Navolokin, general manager for APAC at AMD, says sustaining competitiveness will require diversified compute architecture rather than reliance on a single technology stack. “Optimising AI goes beyond GPUs,” he says, arguing that heterogeneous and open ecosystems will be critical to building resilient infrastructure.
Trust and workforce readiness
Industry leaders also suggest Singapore’s competitive edge may hinge on trust as much as speed.
Raen Lim, Qualtrics APJ managing director, believes broader access to AI tools and compute will help ease adoption bottlenecks but confidence will determine scale. “If Singapore wants to be a ‘trusted AI hub’, the next step is to treat trust like national infrastructure which requires measurable governance,” she says.
As for Jess O’Reilly, Workday’s general manager for Asean, guardrails around agentic AI and human accountability will be critical as firms move from pilot to production. This ensures systems operate with a “human-in-the-loop” approach.
Andy Sim, vice president and managing director for Singapore at Dell Technologies, says Singapore’s $37 billion commitment to research, innovation and enterprise underscores its determination to remain competitive among larger economies. However, he adds that technology investments will only deliver returns if workforce skills keep pace, and this requires sustained public-private collaboration.
Taken together, industry reactions suggest broad alignment with the government’s direction but also a sober understanding that ambition alone will not determine outcomes.
As enterprises move from pilots to production, the differentiators will be disciplined execution, trusted data foundations, cyber resilience and workforce transformation — the less visible infrastructure that ultimately determines whether AI becomes a durable competitive advantage or an expensive experiment.
