Singapore Pushes AI Investments Toward Measurable Business Value

# Singapore Pushes AI Investments Toward Measurable Business Value

Tan's core point is simple: using AI is not enough. The work itself must change. That means moving past standalone pilots and point tools, and instead redesigning workflows so that people and AI systems operate together in repeatable, reliable ways that demonstrably improve business metrics.

He pointed to Sea as an example of a company applying AI across different business lines. At Shopee, AI is being used to improve product listings, aid product discovery and support seller decision-making. Sea's broader use across Shopee, Monee and Garena illustrates how a company can embed AI across products and services rather than treating it as an experimental overlay.

To address that gap, Singapore is using government-led experiments and programmes. SEA-LION and the National AI Missions exist to trial and test AI applications in sectors such as finance, healthcare and advanced manufacturing. The government's goal is to identify and scale applications that provide clear, measurable value in operational environments.

• Start with the outcome. Define the business metric you want to improve and design AI workstreams to move that metric.
• Rework workflows. Map how tasks will change when AI participates, and assign responsibilities across people and systems to avoid fragmented adoption.
• Plan for integration and reliability. Early pilots should include plans for connecting to core systems and for maintaining consistent operation under load.
• Measure before you scale. Use real-world pilots that capture economic impact so you can justify broader rollouts.

Why Singapore is focused on measurable value

Leaders should audit existing AI pilots for integration risk and measurable outcomes, redesign workflows with clear human–AI roles, and select pilot projects that link directly to revenue, cost or risk KPIs. Where appropriate, engage national testbeds such as SEA-LION and National AI Missions to validate outcomes in sector-specific contexts.

Short takeaway: adoption alone is insufficient. Singapore's message is that the next phase of AI investment must focus on changing how work gets done and on proving measurable economic value before scaling projects across organisations.