From Builder to Leader: What It Will Take for Thailand to Scale AI

Thailand does not have an AI ambition problem. It has an AI execution challenge.

A market moving fast, but stuck in the middle
The surprising finding is not that Thailand is embracing AI. It is that most organisations remain stuck between experimentation and scale.

According to Thailand's Digital Economy Promotion Agency (DEPA), digital industry is projected to reach THB 2.9 trillion by the end of 2026, accounting for around 15% of national GDP. Cloud and data centre capacity is expanding rapidly to meet that demand. 

But capacity is only useful if enterprises are ready to use it. STT GDC’s research report, Mind the Gap: Bridging the AI Infrastructure Readiness Divide, surveyed  60 major organisations and digital natives in Thailand. The findings are sobering. While 78% have progressed past experimentation into the 'Builder' stage, none (0%) have reached 'Leader' status. Fewer than 10% are ready to scale AI workloads effectively.

The gap between building a foundation and running AI as part of how the business operates is wider than the headlines suggest.

From AI ambition to AI commitment
What is holding Thai enterprises back is not a lack of interest. It is a lack of conviction in how AI will translate into measurable business value.

Our research shows that 57% of Thai business leaders cite budget constraints and difficulty in measuring return on investment (ROI) as their biggest hurdles. Today, 76% of surveyed organisations allocate less than 5% of their total IT budget to AI. This is the familiar corporate paradox: leaders are reluctant to commit capital to a technology whose returns have not yet been fully proven on the balance sheet.

But this hesitation is increasingly costly. In sectors where AI is being applied with discipline, the results are already tangible. 

Governance is not a brake.  It is an accelerator.
The organisations most likely to scale AI successfully are not those with the fewest guardrails. They are those with the clearest ones.

The most resilient organisations are moving away from restrictive digital 'gates' that slow teams down, and towards fluid 'guardrails' that allow teams to innovate at pace within a secure and well-governed perimeter. Alongside this, they are breaking down functional silos and creating centralised data environments that AI can actually work with.

But technology and process are only part of the answer. The true anchor of AI governance is human judgement.

AI can accelerate decisions, but accountability remains a human responsibility. Organisations that scale AI successfully are not removing people from the process. They are building governance frameworks where technology accelerates momentum and human expertise remains the ultimate validator. Embedding this discipline is what prevents 'Shadow AI', compliance breaches and reputational risk from emerging as AI scales across the business.

The execution gap is not just organisational. It is physical.
There is one dimension of readiness that is consistently underestimated in Thailand's AI conversation: the infrastructure itself. 

Our data reveals a critical disconnect. While 50% of Thai enterprises have already invested in high-performance AI hardware such as GPUs, only 15% are actively deploying or exploring liquid cooling. Next-generation AI workloads generate heat and power densities that traditional air cooling cannot support. Without the right thermal and power foundations, organisations end up throttling the very hardware they paid a premium to acquire yet never see the performance they were promised.

Software strategy and use case selection are important, but they are downstream of a more fundamental question: is the underlying infrastructure ready to run AI at production scale?

What leadership looks like from here
Crossing the chasm from 'Builder' to 'Leader' will not come from another round of proof-of-concepts. It requires a shift in how AI is planned, funded and delivered. Three priorities stand out:
•    Design for scale from the start. Build flexible infrastructure that can accommodate rising compute density and evolving AI workloads, rather than retrofitting environments after the fact. 
•    Adopting distributed and hybrid architectures that combine on-premise, colocation and cloud environments to enable greater flexibility, sovereignty and performance for AI workloads.
•    Partner where it matters. Acknowledge internal capability gaps honestly, and work with specialised infrastructure providers who bring the liquid cooling, high-density design and operational depth that AI at scale demands.

The real test ahead
Thailand has already demonstrated the ambition to participate in the AI economy. The next challenge is more difficult: turning ambition into execution.

The organisations that succeed will be those that look beyond individual AI projects and focus instead on creating the conditions that allow AI to scale — trusted governance, skilled people, resilient infrastructure and a clear path from experimentation to production.

AI leadership in Thailand will not be measured by how many pilots an organisation launches. It will be measured by how effectively it turns AI into measurable, repeatable business outcomes.
 

Download the full Asia AI Infrastructure Readiness Assessment Report and assess your organisation's AI infrastructure readiness to discover where you stand on the AI maturity journey.

This article is contributed by Budsarin Pradityont, Country Head, ST Telemedia Global Data Centres (Thailand). It was originally published in Bangkok Biz News on 10 August 2026.