New McKinsey-EDB-Tech In Asia report: AI adoption in Southeast Asia is outpacing the global average

Southeast Asia is experiencing remarkable levels of AI infrastructure investments, significantly outpacing global trends.

A new report by McKinsey, EDB, and Tech in Asia, titled "AI in Southeast Asia: An Era of Opportunity” revealed that nearly half (46 per cent) of the surveyed companies in the region have moved beyond piloting AI initiatives to scaling them, placing Southeast Asia ahead of the global average of 35 per cent.

These findings were part of the report that surveyed 330 senior executives across six ASEAN markets to assess the current state of AI adoption and investment in the region. The report also identifies critical gaps that continue to hinder AI adoption and innovation across the region. 

Read on to discover how Singapore’s AI ecosystem is uniquely positioned to help companies overcome these barriers and capitalise on the region’s AI boom:

Barrier #1: Recruiting the right talent and skills

THE CHALLENGE Survey data revealed that one in five executives identified talent as the single biggest challenge in scaling and delivering measurable impact. Finding skilled AI professionals remains the top obstacle across Southeast Asia, constraining companies' ability to design, deploy, and scale AI initiatives effectively.

SINGAPORE’S AI ADVANTAGE Singapore hosts over 60 AI Centres of Excellence from leading global technology companies, creating a dense network of specialists in machine learning, data science, AI governance, and enterprise AI architecture. This concentration means MNCs can recruit from a talent pool already experienced in enterprise-scale AI deployment, rather than training from scratch.

AI Singapore (AISG), a nationwide programme hosted by the National University of Singapore works with universities, research institutions, and industry to grow Singapore’s AI capabilities. AISG’s AI Apprenticeship Programme has trained over 410 apprentices across 16 cohorts since 2018 – of this, 90 per cent of graduates go on to pursue AI related roles. Aside from AISG, local universities also offer AI courses targeted at producing graduates with the requisite skills to fill talent gaps.

Besides the domestic talent pool, Singapore's comprehensive employment passes and tech.pass schemes can facilitate companies’ efforts to bring global AI talent to the region. This enables MNCs to establish truly global AI teams in Singapore, combining local market knowledge with international best practices.
 

Barrier #2: Navigating the complexities of AI integration

THE CHALLENGE Roughly one in ten executives cited data quality and availability as a key barrier to adoption. Integrating AI with legacy systems and existing workflows prove technically difficult, often requiring extensive custom development and creating maintenance burden.

SINGAPORE’S AI ADVANTAGE Major cloud providers have invested substantially in Singapore's infrastructure, creating a robust foundation for hybrid and multi-cloud architectures. This means companies can integrate AI capabilities without worrying about underlying infrastructure limitations.

Aside from this, robust public-private partnerships have resulted in novel programmes like the Enterprise Compute Initiative, designed to help companies navigate AI integration by addressing barriers like high cost, lack of computing power, and shortage of technical expertise.

This is done through providing access to:

• Subsidised Cloud Credits: It offers financial support (cloud credits) for compute-heavy AI workloads, significantly lowering the upfront cost and risk of building and deploying AI models.
• AI Tools and Platforms: Companies gain access to advanced AI software and tools from major global Cloud Service Providers (CSPs) like Google Cloud, Microsoft, and Amazon Web Services (AWS), which may include pre-built Generative AI modules or specialised platforms.
• Consultancy Services: The government co-funds consultancy services (up to 70 per cent of eligible costs) from system integrators and partners nominated by the CSPs. These experts can help companies develop clear AI roadmaps, manage change management processes, and even develop a Minimum Viable Product (MVP).

Barrier #3: Uncertainty around returns on AI Investment

THE CHALLENGE 60 per cent of respondents reported seeing less than five per cent Earnings Before Interest and Taxes (EBIT) impact from AI adoption, while 18 per cent of respondents, see no financial impact at all. Companies struggle to quantify AI's business impact, leading to stalled investments and difficulty securing executive buy-in for scaled deployment.

SINGAPORE’S AI ADVANTAGE With 56 per cent of Singapore companies already scaling AI—higher than the regional average (38 per cent)—the city-state offers ample opportunities for peer learning. This means MNCs can engage in industry forums, roundtables, and informal networks where peers share practical insights on measuring and capturing AI value.

These learnings that are grounded in real implementations and in the face of current regulatory, talent, and market conditions.

To this end, home-grown large enterprises like DBS Bank and Singapore Airlines have proven that AI adoption can result in measurable outcomes:

• DBS BANK has moved from experimentation to enterprise-wide agentic AI deployment. Their generative AI chatbots handle complex customer conversations whilst improving efficiency—resulting in demonstrable outcomes that justify continued investment.
• SINGAPORE AIRLINES similarly shows measurable results across customer experience transformation and operational efficiency after their decade-long journey in foundation building.

Singapore's concentration of global consulting firms, system integrators, and technology partners means access to advisors who've guided dozens of AI implementations.

These partners bring frameworks for establishing clear value creation metrics from project inception—defining KPIs tied to business outcomes. They help companies avoid the common trap of measuring AI success by models deployed rather than EBIT impact achieved.

Barrier #4: Trust is crucial in scaling AI

THE CHALLENGE The McKinsey report also found that 41 per cent of companies have experienced negative consequences from AI inaccuracy proving the need for trust, accountability and responsible governance in enabling AI adoption at scale.

SINGAPORE’S AI ADVANTAGE While global AI-specific standards and governance are still in its early stages, Singapore took early steps to outline ethical principles for adoption.

Some of these initiatives include:

• AI Verify for traditional AI – A testing and assurance framework that helps organisations assess the transparency and robustness of AI systems based on internationally recognised responsible AI principles through a combination of technical tests and process checks.
• Project Moonshot for generative AI – A gen AI testing toolkit launched by Singapore’s Infocomm Media Development Authority (IMDA) and supported by the AI Verify Foundation to help developers safely build and evaluate large language models (LLMs). It is one of the world’s first open-source frameworks that brings together benchmarking, red-teaming and baseline safety testing so that organisations can assess the quality, security and deployment risks of their generative AI systems before they go live.
• Model AI Governance Framework – Provides practical guidance for companies on ethical AI deployment, risk management, and accountability.

Contrary to the opinion that governance standards slow down adoption, these frameworks and toolkits provide clear guardrails, so companies have the confidence to deploy AI responsibly at scale.

Follow us on LinkedIn to get more updates or stay in the loop with our newsletter.