Budget 2026 Puts AI Into Execution Mode. Operators Need To Fund Foundations Before Features

Singapore is accelerating AI deployment, but for regulated, asset-heavy operators, the binding constraint remains legacy systems and operational controls

Image: AzerNews

Singapore’s Budget 2026 elevates AI from emerging tech to a national priority. A new National AI Council, chaired by PM Lawrence Wong, will coordinate strategy, regulation, and resources.

National AI Missions will target four sectors: advanced manufacturing, connectivity including logistics, finance, and healthcare. Enablers include a new one-north AI park for testing and scaling, expanded TeSA program to build AI literacy in non-tech roles such as accountancy and legal, and a Champions of AI program to support enterprise transformation.

For operators in banking, logistics, manufacturing, and similar sectors, this is not a signal to rush AI purchases. Long procurement cycles, legacy cores, and regulatory scrutiny mean early spending choices determine whether deployments scale safely or create operational risk.

The temptation is to fund visible, demo-friendly tools, leveraging government incentives, missions, and talent programs. These feel aligned and low-risk but often fail under production load, audit pressure, or incident response when deeper foundations are absent.

So the question is simple: are you funding AI tools, or the foundations that let them survive production?

The Operator Gate: Three Tests Before Scaling

Before scaling any AI system, operators should test these three basics:

• Reliability: Can networks, integrations, and core systems handle real-world usage without degradation or failure? If not, AI will falter at peak traffic or during incidents.

• Evidence: Can you rapidly trace data inputs, model changes, and decision rationale? Without this, audits, compliance reviews, and post-incident probes become major risks.

• Portability: Can you switch vendors or roll back without rewriting core workflows? Vendor lock-in turns every update into a costly, high-risk event.

Foundations, Not Models, Drive ROI

Most AI projects fail not because models underperform, but because surrounding systems are unprepared. In regulated sectors, success hinges on legacy integration, clean data flows, robust identity/access controls, monitoring/incident response, and disciplined change management.

As Adeline Liew, Country Business Leader, Singapore, Alcatel-Lucent Enterprise, said in her notes to media, the real gap is between ambitious AI applications and the aging infrastructure required to run them. For many enterprises, the bottleneck is not the model itself but the reliability and speed of internal networks. If underlying systems are slow or disconnected, AI investments struggle to deliver returns.

Budget 2026 incentives are sequencing tools, not transformation funding. The enhanced Enterprise Innovation Scheme (EIS) offers 400% tax deductions on qualifying AI spend, capped at S$50,000 per year of assessment for YA2027 and YA2028. It helps offset early experiments and foundation work, but it will not remove enterprise constraints on delivery.

“To realize the full economic potential of these national investments, infrastructure must be viewed as a strategic business asset rather than a back-office expense,” said Liew.

In this view, AI readiness becomes a core business capability: investing in modernized foundations enables organizations to move beyond testing and achieve sustained gains in productivity and service delivery.

Use these incentives for low-regret priorities: foundational cleanup such as data lineage and monitoring upgrades, and tightly controlled pilots, not broad deployments.

Skills Boost Adoption; Operating Models Ensure Survival

Budget 2026 expands AI training across the workforce, including non-tech roles, raising baseline comfort and usage. Yet training alone does not redefine decision rights, escalation paths, or risk ownership. In regulated environments, redesigning workflows, governance, and accountability determines whether systems survive audits, outages, and other future problems.

PM Wong framed the Budget as a shift from isolated pilots to scaled deployment at national speed. For operators, speed and scale only work if operating models evolve too. As KPMG partner Edmund Heng notes, “Clear accountability, a risk-based approach, and early governance will be critical for sustainable AI implementation at scale. Good governance empowers AI adoption with confidence, while unclear governance hinders it.”

What to Fund Now, Pilot, or Delay: By Sector

National AI Missions provide sequencing signals. Each sector carries different evidence burdens and incident tolerances.

Finance and healthcare

• Fund now: Data lineage, audit trails, model validation, and rollback controls meeting regulatory standards.

• Pilot with guardrails: Narrow decision-support use cases under human oversight with clear incident playbooks.

• Delay: High-impact automation in core banking or clinical workflows until auditability, explainability, and safe rollback are proven. In practice, this means decisions that change customer or patient outcomes without an auditable human sign-off path. In healthcare, delay automation that touches triage, diagnostic support, or patient routing unless model behavior can be demonstrated and safely rolled back.

Advanced manufacturing and connectivity/logistics

• Fund now: Legacy system integrations, network upgrades, and monitoring for stable data flows under load.

• Pilot with guardrails: Limited automation on specific lines, routes, or segments with manual fallback.

• Delay: End-to-end autonomy across systems of record unless integrations have proven SLOs and tested fallback paths.

In all cases, prioritize low-regret foundations, run scoped pilots with rollback, and scale only after reliability, governance, and accountability prove themselves in live conditions.

Budget 2026 makes AI a national imperative but leaves execution risks inside enterprises. Competitive advantage will come not from adopting fastest, but from sequencing investments more deliberately than peers: fund foundations first, treat missions as leading indicators, and move with disciplined speed.

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