# Southeast Asia’s AI Boom Is Real, But Don’t Mistake Momentum for Maturity

- Link: https://www.thailand-business-news.com/generative-ai/314565-southeast-asias-ai-boom-is-real-but-dont-mistake-momentum-for-maturity
- Published: 2026-06-17T07:04:00+07:00
- Author: J. Allan

## Abstract

 * A McKinsey, Singapore Economic Development Board, and Tech in Asia report finds
   that 46% of surveyed Southeast Asian companies have moved beyond piloting AI 
   to scaling it, surpassing a cited global average of 35%. However, this figure
   masks significant unevenness across markets and industries, with Singapore and
   technology sectors driving results while healthcare and public services remain
   in early stages.
 * The financial returns tell a more cautious story: nearly 80% of companies report
   marginal or no bottom-line impact from AI investments. Widespread challenges 
   including data quality issues, talent shortages, and immature governance frameworks
   suggest the region is advancing on adoption metrics while the foundational infrastructure
   needed to convert that adoption into measurable value remains underdeveloped.

A new report from McKinsey, the Singapore Economic Development Board, and Tech in
Asia has landed with a headline that sounds almost too good for a region often accused
of playing catch-up in tech: nearly half of the surveyed companies in Southeast 
Asia have moved beyond piloting AI initiatives to scaling them, placing the region
ahead of the global average. 

The [study](https://www.edb.gov.sg/en/business-insights/insights/new-mckinsey-edb-tech-in-asia-report-ai-adoption-in-southeast-asia-is-outpacing-the-global-average.html),
based on a survey of over 300 senior executives across six ASEAN markets and ten
industries spanning healthcare, travel, logistics, and legal services, surveyed 
respondents from companies with AI use, of varying annual revenue, from six key 
markets: Singapore, Malaysia, Indonesia, the Philippines, Thailand, and Vietnam.
On paper, it reads like a victory lap for a region that has spent decades being 
told to wait its turn in the technology race.

ADVERTISEMENT

It would be easy to file this under feel-good regional boosterism and move on. But
the more interesting story isn’t the headline number, it’s what the report admits
sits underneath it, and how that number looks once placed against the wider data
on AI adoption globally. Strip away the framing, and what you have is a region racing
ahead on adoption while still struggling with the basics that determine whether 
that adoption actually pays off.

### The Unevenness Hiding Inside the Headline 

Start with the unevenness hiding inside that 46 per cent figure. The “Southeast 
Asia outpaces the world” framing flattens a region where the gap between leaders
and laggards is enormous. Singapore and Indonesia are standing out as leaders in
AI adoption, with 56% and 51% of respondents, respectively, reporting progress toward
scaled adoption, while at the other end of the spectrum, entire categories of the
economy are barely off the starting line. Industry-wise, technology, media, and 
telecommunications, and advanced industries dominate AI usage, with roughly six 
in ten (62%) companies in these sectors reporting scaling or having fully scaled
their deployments. In contrast, the public sector, healthcare, and service-oriented
industries remain in the early stages of usage, with nearly seven in ten companies(
69%) in these sectors still piloting or experimenting. In other words, the “region”
isn’t moving as one. 

A handful of digitally native sectors in a couple of advanced economies are pulling
the regional average up, while public services, healthcare systems, and large swathes
of the service economy, the parts of the economy that touch ordinary people’s lives
most directly, are still essentially in the lab. That’s a very different picture
from “Southeast Asia is ahead of the world.”

### Related**Posts**

###  󠀁[Beyond Food Security: Tapping into Brunei’s Agri-Food Opportunities](https://www.thailand-business-news.com/opinion/337082-beyond-food-security-tapping-into-bruneis-agri-food-opportunities)󠁿

###  󠀁[Asia’s Economic Cycles Turn Inward as Regional Finance Gains Influence](https://www.thailand-business-news.com/opinion/333558-asias-economic-cycles-turn-inward-as-regional-finance-gains-influence)󠁿

###  󠀁[How ASEAN+3 Can Build Resilience to Energy Shocks](https://www.thailand-business-news.com/opinion/336402-how-asean3-can-build-resilience-to-energy-shocks)󠁿

###  󠀁[ASEAN+3’s Path to Faster, More Inclusive Cross-Border Payments](https://www.thailand-business-news.com/opinion/334722-asean3s-path-to-faster-more-inclusive-cross-border-payments)󠁿

Now place the regional figure against the global numbers, and the comparison gets
murkier still. Different surveys measuring “AI adoption” arrive at wildly different
answers depending on what exactly they’re counting. McKinsey’s enterprise survey,
covering 1,993 companies across 105 countries, finds 88% using AI in at least one
business function. The OECD’s official government-level firm measurement puts the
number at 20.2%. Microsoft’s population tracking, which measures how many working-
age adults actually opened a generative AI tool, lands at 16.3%. None of these are
wrong; they’re just measuring different things, from “has anyone in the company 
ever touched an AI tool” to “is AI embedded in core national economic activity.”
The EDB report’s claim that Southeast Asia’s 46 per cent “scaling” rate beats a 
global average of 35 per cent sits somewhere in the middle of that spectrum, but
it’s worth remembering that “outpacing the global average” on one fairly narrow 
definition of adoption can coexist comfortably with the region lagging badly on 
others. Bragging rights on a single metric, in other words, don’t amount to leadership.

### When Scaling Doesn’t Translate Into Returns 

Then there’s the money question, and this is where the report’s own numbers should
give pause to anyone tempted to treat “scaling AI” as synonymous with “AI is working.”
Sixty per cent of respondents reported seeing less than five per cent EBIT impact
from their AI investments, and eighteen per cent saw no financial impact at all.

Read that again: nearly four in five companies are getting marginal to zero bottom-
line returns from AI, even as the region as a whole claims to be scaling faster 
than the rest of the world. That’s not unique to Southeast Asia; it echoes a pattern
researchers are seeing globally. Key challenges include data quality, cited by 73%
of companies, alongside talent shortages, job displacement fears, and insufficient
governance, with 66% of leaders reporting their teams are not AI-ready. If two-thirds
of leaders globally admit their own teams aren’t ready for the AI systems they’re
deploying, a regional adoption race framed primarily around speed starts to look
less like a strength and more like a risk multiplier.

The talent gap the EDB report identifies as the single biggest barrier to scaling
fits squarely into this picture. The underlying McKinsey-EDB-Tech in Asia report
frames it starkly: talent shortages, unclear ROI, and integration complexity are
the biggest challenges preventing AI initiatives from scaling and delivering measurable
impact, despite strong executive intent and rising investment across the region.

Singapore’s answer, over 60 AI Centres of Excellence from firms including Alibaba
Cloud, IBM, NVIDIA, and Oracle, plus government-backed investment vehicles like 
SGInnovate, which has invested in over 100 business-to-business AI companies across
industries from marketing to healthcare, is a genuinely substantial infrastructure.

But it is also, by construction, a solution that concentrates benefit in one city-
state of roughly six million people. If Jakarta, Manila, or Ho Chi Minh City are
where the talent crunch actually bites hardest, “fly your AI team to Singapore” 
is a workaround for multinationals headquartered there, not a fix for the structural
skills gap across a region of nearly 700 million people.

### Trust, Governance, and the Limits of a City-State Model 

The trust dimension is perhaps the most honest part of the original report, and 
the one that deserves the most scrutiny against the wider data. Forty-one per cent
of companies said they had experienced negative consequences from AI inaccuracy,
a figure that should be sobering for any executive currently being told that AI 
adoption is a race they’re losing if they’re not “scaling” fast enough. And the 
appetite for AI use isn’t slowing down to match. Generative AI is projected to grow
at a 27.6% CAGR in Asia from 2026 to 2034, with 63% of Southeast Asian companies
already using it for text-based tasks and 71% of enterprises leveraging it across
business functions. 

Layer that onto a workforce where, by some measures, 78% of Asian workers are now
using AI at least weekly, surpassing the global average of 72%, and you get a picture
of extremely rapid, bottom-up adoption running well ahead of the governance, data-
quality, and ROI-measurement capabilities that the same reports say are still immature.
Singapore’s governance tools, AI Verify, Project Moonshot, and the Model AI Governance
Framework, are genuinely among the more thoughtful regulatory responses to generative
AI anywhere in the world, and the original report’s framing of governance as an 
enabler of confident deployment rather than a brake on it is a fair point worth 
taking seriously. But governance frameworks built primarily in and for one jurisdiction
don’t automatically travel across six countries with very different regulatory capacities,
data protection regimes, and digital infrastructure.

None of this is an argument against AI adoption, and it’s certainly not an argument
against Singapore’s role as a regional hub. The talent pipelines, cloud infrastructure,
and governance frameworks described in the EDB report are real assets, and companies
setting up in Asia would be foolish to ignore them. 

But the headline figure deserves more scepticism than it’s likely to get, especially
once set against the broader data: a region that leads on one definition of adoption,
while two-thirds of corporate leaders admit their teams aren’t AI-ready, three-quarters
cite data quality as a barrier, and nearly half of companies using AI report being
burned by its inaccuracy, isn’t necessarily “ahead.” It might just be further along
a path whose institutional foundations, talent, governance, and honest measurement
of value are still being poured in real time, often after the building has already
gone up around them.

The honest takeaway isn’t “Southeast Asia is winning the AI race.” It’s that Southeast
Asia, like much of the world, is moving fast on adoption, while the infrastructure
that determines whether that speed translates into value, skilled people, trustworthy
data, credible ROI metrics, and governance that works across borders rather than
within a single city-state, lags well behind. Singapore’s strength may not be that
it has solved these problems, but that it has been more deliberate than most about
building scaffolding while construction continues. Whether the rest of the region,
and the rest of the world, can close that gap before the cost of AI errors and wasted
investment starts to outweigh the benefits of speed is the question this report 
raises, but, for all its data, doesn’t quite answer.