Singapore’s National AI Council is a signal: AI is moving from experiment to operating system

Photo by Christian Chen on Unsplash

TL;DR, Singapore is not just encouraging companies to “try AI” anymore; it is turning AI into a national execution agenda.

While this creates a real opening for SMEs, this is also a familiar trap that many SMEs fell into during the previous digital transformation arc of Singapore: treating new gahmen grants and initiatives as free money for buying tools; just like past productivity schemes that produced templated WordPress websites, abandoned CPanel portals, and digital transformation theatre.

The real winners this time will not be the companies that buy the most AI products, but the ones that use AI to properly address unoptimised workflows, clean up unstructured data, strengthen cloud and compliance foundations, and ultimately create measurable operational leverage.

This is not the moment for SMEs to start asking, “what can we start claiming now?” it’s time for asking, “what part of our business can we make sharper, faster, safer, and more scalable?”: with the Singapore government’s support.

AI is now a national execution agenda

Singapore’s latest AI push is not just another innovation headline.

The setup of a new National AI Council signals that AI is becoming a coordinated national execution agenda rather than a loose collection of pilots. The Council’s role is to provide strategic direction for Singapore’s AI agenda and oversee national AI missions across key sectors such as advanced manufacturing, connectivity, finance, and healthcare.

That matters because Singapore’s constraint has always been clear: limited labour, limited land, high operating cost, and an economy that has to stay globally relevant by being sharper than larger markets.

AI is being positioned as one way to attack those constraints directly. For SMEs, this movement is especially important.

The common mistake is to read “National AI Council” and assume this is mainly for banks, telcos, hospitals, ports, research labs, and large enterprises. That would likely be the wrong interpretation.

The policy direction is also about helping smaller companies adopt AI in practical ways: through support for AI-enabled solutions, enterprise capability-building, worker training, and innovation incentives.

This is the opening.

Grants are not transformation

SMEs should not treat these initiatives as free money for buying tools.

Singapore has seen this all play out before anyway.

For years, “productivity” and “digital transformation” grants created an entire cottage industry of grant-friendly vendors selling templated websites, basic e-commerce stores, generic portals, and software bundles that looked good in a claim submission but often had little relationship to the company’s real operating model.

Those who have been in the game for some time would remember the good ol’ times of PIC grants.

IRAS investigated or audited about 71,000 PIC cash payout claims from 2011 to 2015, with about 1500 claims requiring clawback and 11 million clawed back for 2011 to 2014. There were also court cases involving false PIC claims, including 14 individuals charged over alleged false claims amounting to more than $300k, and a separate case where a PIC promoter was alleged to have assisted 49 claimants with fraudulent claims exceeding $1 million.

That history matters.

The easiest way to waste Singapore’s AI push is to repeat the same pattern with newer vocabulary.

1. Shoddily grant-funded websites become shoddily grant-funded chatbots.
2. Abandoned portals become abandoned AI dashboards.
3. Digital transformation theatre becomes AI transformation theatre.

Can claim, can buy, can launch — but if nobody uses it and nothing improves, then what is the point?

The lesson: a subsidised tool is still a bad investment if it does not change the economics of the business.

To be fair, Singapore’s SME digitalisation programmes have also created measurable benefits. Evaluations of programmes such as SMEs Go Digital, PSG, and Start Digital have shown positive effects on firm-level outcomes, including improvements in value-add per worker and revenue.

The companies that truly benefited were not the ones that merely bought software, they were the ones that used software to improve real workflows.

AI must change business economics

AI adoption has to be held to the same standard as any serious business investment.

1. Does it reduce cycle time?
2. Does it improve conversion?
3. Does it reduce rework?
4. Does it improve customer response?
5. Does it give management better visibility?
6. Does it reduce dependency on manual admin?
7. Does it help the business earn more, serve better, or operate with less drag?

That is the bar, instead of:

1. Is the tool claimable?
2. Is the vendor pre-approved?
3. Does the demo look clever.

A small business does not need a moonshot AI strategy. It needs fewer manual handoffs, faster quote generation, cleaner customer follow-up, better inventory visibility, automated reporting, tighter finance workflows, and less dependence on tribal knowledge sitting in one person’s head.

A small business needs to deliver instead of caring about the bureaucracy. That means the first question is not:

“Which AI tool can we claim under a grant?”

It’s:

“Which recurring workflow is costing us margin every month, and what data, cloud, and control foundations must be fixed before AI can improve it?”

This in our opinion is the more honest question.

It is also the more uncomfortable one, because it forces the business to look at how work actually happens, not how the org chart says work should happen.

AI is not a tooling problem

This is where many AI adoption efforts break down.

AI does not run well on messy data, fragile cloud infrastructure, or weak controls.

• If customer data is scattered across spreadsheets, shared drives, SaaS tools, and old CRMs, AI will amplify the mess.
• If the cloud environment has no clear architecture, cost governance, access controls, or deployment discipline, AI projects become expensive experiments.
• If compliance posture is weak, especially around data privacy, security, auditability, and vendor risk, AI adoption creates exposure instead of leverage.

In other words: anyhowly plugging AI into a messy operating environment does not result in transformation. That is how companies end up with more tools, more exceptions, more shadow IT, and more confusion.

AI adoption has to be treated as an operating model problem, not a tooling exercise.

The way forward: start with one workflow

Start with one workflow that directly affects cash, capacity, or customer experience. Then build the data, cloud, and governance foundations around it.

A practical 90-day SME plan could look like this:

1. Identify three high-friction workflows that consume time or create revenue leakage.
2. Map the data behind those workflows: where it lives, who owns it, whether it is clean enough, and whether it is safe to use.
3. Assess whether the current cloud and SaaS environment can support automation securely and cost-effectively.
4. Define basic controls for access, audit trails, data retention, vendor usage, and human review.
5. Implement one AI-enabled workflow with a measurable business outcome: faster response time, lower admin overhead, fewer errors, higher conversion, or better reporting.

No need to boil the ocean (or whatever the latest greatest corporate buzzword for that is), pick one workflow, fix the foundations around it, measure business result. Repeat.

That is not glamorous, but it is how real adoption happens.

Policy creates opportunity, execution creates value

The National AI Council gives the movement top-level coordination. The national programmes and incentives give enterprises a clearer adoption pathway. The broader ecosystem gives SMEs more room to experiment, learn, and implement.

But policy does not create business value by itself, execution does.

For SMEs, the next 12–24 months should be treated as a window to build capability before AI becomes table stakes.

The companies that move early can compound small operational gains into real advantage: faster teams, better service, lower admin drag, stronger compliance, and more scalable revenue.

Singapore is not just funding AI experimentation anymore, it is now funding AI execution. SMEs should respond the same way or risk losing to the competition be it other local players, or global firms finding their market in Singapore.

How Aijutsu can help

And this is where we believe Aijutsu can help.

With a solid foundation in data, cloud, and compliance, we help businesses take on transformation projects that run deeper than “what product should we buy?”

Because it is obviously not that simple. AI is not a buy-and-forget solution; a chatbot subscription does not fix fragmented data, unclear process ownership, weak access controls, or workflows that were never properly designed.

Aijutsu helps organisations move from vague AI ambition to practical execution: mapping critical data, reviewing cloud architecture, defining compliance controls, identifying workflow bottlenecks, and implementing internal tools that improve real business operations.

As a company, Aitjusu focuses on operator-level expertise. Everyone in Aijutsu has handled production issues at 1AM, dug through databases finding that one incoherent value that QA didn’t think of and which broke the system, helped to architect software that has stood the test of time, and played various roles from writing code to shaping product vision.

The goal is not to add another shiny system. The goal is to help teams use AI safely, securely, and measurably, with a clear link to revenue, margin, capacity, risk reduction, or customer experience.

And that’s what we’re for.

Aijutsu is a Singapore-based fractional technology leadership practice for founders, SME business owners, and lean technology teams that need clarity, change, and delivery confidence across AI, cloud, compliance, infrastructure, operations, and software delivery. Check us out at https://aijutsu.dev