AI is predicted to drive growth in Asia-Pacific, but reports from Mastercard, AWS and LinkedIn show a two-tier AI economy is emerging.
Asia-Pacific (Apac) governments are rolling out infrastructure investments, targeted financial support and industrial policies aimed at boosting productivity, positioning the region to benefit from the global artificial intelligence (AI) boom. However, despite increased adoption, the gap between policy ambitions and corporate capabilities is widening, with most companies stuck in the early stages of AI use.
Mastercard Economic Research Institute predicts that AI adoption and targeted fiscal support will provide meaningful tailwinds for Apac growth in 2026. South Korea, Japan, India, and Hong Kong are leading the region in the adoption of AI tools for businesses and consumers. Governments in the region are backing this momentum with industrial policies targeting AI hubs, data centres, smart cities and semiconductor development.
Singapore clearly illustrates both opportunities and constraints. Amazon Web Services (AWS) Unlocking Singapore’s AI potential in 2025 According to the report, 48% of businesses in the city-state are now using AI, up from 40% a year ago.
However, 65% of these AI deployment businesses remain primarily focused on basic applications such as publicly available chatbots and scheduling assistants, rather than innovative uses such as custom AI systems or research and development.
“2023 will bring AI experimentation, 2024 will bring production, and the next few years will bring us truly industrial-scale AI implementations,” said Priscilla Chong, Country Manager, AWS Singapore. The Edge Singapore.
As explained in the AWS report, the gap between adoption and impact has created a two-tier AI economy, particularly in Singapore.
Despite high adoption rates, large enterprises often struggle to scale AI beyond isolated use cases. About 62% of large enterprises in Singapore are using AI, but only 30% have a comprehensive AI strategy that outlines how the technology supports their core business objectives.
In contrast, startups are more likely to incorporate AI into their operating model, with nearly half putting AI at the heart of their business proposition.
“For companies looking to expand beyond basic deployments, three key areas require attention: first, building a unified ‘AI factory’ supported by a sound data strategy, second, developing a governance framework that balances innovation with appropriate guardrails, and third, developing the specialized technical capabilities needed for scalable AI systems that can operate autonomously across diverse workflows,” advises Chong.
The biggest brake on progress is talent. The same AWS study revealed that 43% of businesses in Singapore cited a lack of digital skills as the main barrier to expanding the use of AI.
“What's unique about the AI Skills Challenge is that it's not static. Skills evolve with technology. The discussion around skills is not new, but what has changed dramatically is how AI fluency varies from role to role,” says Chong.
She continued, “Think about what marketers will need in two years' time: not just agile engineering, but the ability to evaluate AI-generated content against brand value, integrate multimodal output across channels, and ethically leverage customer behavioral predictions. It's about bridging domain knowledge and role-specific AI skillsets.”
The skills gap is particularly pronounced among small and medium-sized enterprises (SMEs). According to LinkedIn research, AI literacy skills per employee for companies with 11 to 50 employees increased by 67% year-over-year, lagging behind 99% of companies with 1,000 or more employees. Work Style Reform Special Report: How will small and medium-sized enterprises survive in 2026?.
Small businesses also lag behind in training, with fewer than half of their employees receiving employer-provided AI training. Despite limited support, employees are self-taught and independently use AI for everyday tasks like writing emails and summarizing notes (45%), as well as for complex strategy and data analysis (26%).
With skilled talent scarce and unevenly distributed, the AWS report argues that Singapore is moving towards a “specialization model” where companies increasingly leverage external AI expertise rather than building everything in-house.
Chong explains: “Singapore's ecosystem is developing a natural specialization that creates a strong innovation cycle. Startups build new AI products and business models, enterprises prove these solutions are scalable, and the public sector acts as a 'trust multiplier' to drive broader adoption. Each segment reinforces the other.”
Singapore-based Hypotenuse AI is an example of this approach. The startup helps retailers reduce time to market by using AI to generate SEO-optimized product descriptions, enrich product data, and edit product images in days instead of months. By building our solutions on AWS cloud infrastructure, we serve major e-commerce brands like Quiksilver and Billabong in more than 30 languages across the US, Europe, and Asia.
“Retail really has huge untapped potential given the wealth of data it already collects, from consumer purchasing patterns and browsing behavior to inventory movements and seasonal trends. This rich data base makes retail ideally positioned for advanced AI implementation,” says Chong.
At the enterprise level, Keppel has built KAI, an in-house AI platform that powers specialized agents for research, investment analysis, and real-time news monitoring. Built on Amazon Bedrock, the platform integrates multiple generative AI models (including Anthropic Claude and Amazon Nova) and enables teams to choose the best model for each use case while building in governance and security controls from the beginning.
Public sector leadership remains central to this ecosystem. According to AWS research, 71% of businesses say the public sector is more likely to adopt and expand the use of AI, and 75% of startups say government adoption is critical to their ability to scale.
Commenting on this finding, Chong said, “We believe this is a synergy of trust. When the public sector demonstrates successful AI implementations, businesses gain confidence to adopt similar technologies. This creates a powerful ripple effect across the industry.”
