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AI push faces a two-speed reality

Nurdianah Md Nur
Nurdianah Md Nur • 3 min read
AI push faces a two-speed reality
Singapore has put AI at the heart of its growth strategy, but a gap is emerging between firms testing the technology and those able to turn it into returns. Photo: Pexels
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Budget 2026 placed AI at the centre of Singapore’s growth agenda. Prime Minister and Finance Minister Lawrence Wong described AI as a “decisive factor for success” and said Singapore’s edge lies in deploying it “effectively, responsibly, and at speed.” While the policy direction is clear, whether that ambition translates evenly into enterprise execution is less certain.

Data on enterprise AI adoption suggest momentum. The Infocomm Media Development Authority (IMDA)’s Singapore Digital Economy 2025 Report shows AI adoption among large enterprises rose to 62.5% in 2024 from 44% the previous year. Among small- and medium-sized enterprises (SMEs), adoption more than tripled from 4.2% to 14.5%. Firms that have adopted AI are using it across multiple business functions, with SMEs applying AI in an average of three functions and larger enterprises in five. The most common use cases were in IT, customer service, finance and accounting.

However, adoption breadth does not automatically translate into economic impact. Companies that generate measurable returns tend to reconfigure processes rather than layer AI onto legacy systems, as highlighted in the AI in Southeast Asia report published in February 2026 by McKinsey, Singapore’s Economic Development Board (EDB) and technology news publication Tech in Asia. These higher-performing organisations also allocate larger digital budgets to AI and demonstrate stronger executive ownership.

Without structural change, experimentation has a limited impact on margins. That dynamic could produce a two-speed AI economy, where productivity gains accrue first to a narrow group of firms. Those firms are likely to be larger corporations with the capital, data infrastructure and engineering depth required to redesign workflows across departments. When integrated into core operations, AI can move beyond pilot and begin to influence cost-to-income ratios, product development cycles and operating leverage. Gains may be gradual, but they compound when embedded across functions.

For SMEs, the path is less straightforward. Many operate with lean technology teams and legacy systems that complicate integration. AI adoption in this segment often centres on off-the-shelf generative AI tools and packaged applications such as accounting automation, customer chatbots or marketing analytics software. These can improve efficiency at the margin but rarely reshape operating models on their own.

Recognising the execution gap, the government has introduced several layers of support. Last year, the Ministry of Digital Development and Information and IMDA launched the GenAI Playbook and GenAI Navigator to provide SMEs with guidance on pre-approved, grant-backed AI solutions tailored to specific business and sector needs.

See also: Singapore embraces the AI revolution

Digital Industry Singapore, a joint office set up by the EDB, Enterprise Singapore and IMDA, also partnered with Amazon Web Services, Google Cloud and Microsoft to help Singapore-based companies translate AI pilots into coordinated technology, process and workforce transformation. The separate programmes are designed to align innovation with operational strategy and governance, while ensuring deployment is scalable and secure.

Budget 2026 elevated that effort with the announcement of national AI missions focused on priority sectors, including advanced manufacturing, connectivity, finance, and healthcare. The move places AI deployment squarely within Singapore’s broader economic strategy.

The success of these measures will determine how AI shapes Singapore’s next phase of growth. If adoption remains concentrated among larger firms with deeper resources, productivity gains may accrue unevenly. That would strengthen select corporate leaders while leaving a long tail of smaller enterprises trailing behind. Over time, such divergence could limit economy-wide productivity growth.

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