Asia’s AI push faces execution limits as infrastructure and skills lag behind
Most Asian firms adopt AI, but infrastructure and talent gaps are limiting their ability to scale beyond pilot deployments.
A regional study by ST Telemedia Global Data Centres finds that while AI adoption is widespread across Asia, most organisations remain unable to scale beyond early-stage deployments.
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The report, based on a survey of more than 600 enterprise and digital-native leaders across nine Asian markets, shows that nearly 90% of organisations have started AI initiatives. However, only a small minority have built the infrastructure and operational capability needed to turn those efforts into consistent business outcomes.
Adoption outpaces operational readiness
The study identifies a structural gap between AI ambition and execution. Around 71% of organisations are still in the “Builder” stage, where pilot projects struggle to transition into production environments. Only 17% are considered “future ready”, with investments in scalable infrastructure, data governance, and specialised expertise.
This imbalance is reinforced by a cycle that limits progress. Many organisations launch AI workloads on infrastructure that cannot scale, which weakens return on investment and reduces the case for further funding. At the same time, a shortage of in-house expertise makes it difficult to manage increasingly complex systems.
Chris Street, Group Chief Revenue Officer of ST Telemedia Global Data Centres, said, “Across Asia, organisations are moving quickly from experimentation to implementation, but many are discovering that AI success now depends less on training models and more on foundations. Without scalable infrastructure and operational readiness in place, it becomes difficult to convert early AI ambition into consistent business value.”
Sustainability remains a secondary concern
The report also highlights a gap between infrastructure demands and sustainability priorities. AI workloads are driving higher energy and cooling requirements, yet most organisations continue to prioritise cost and performance.
While 27% of organisations expect ESG goals to shape future plans, 64% still rank performance or cost above sustainability when selecting infrastructure. This comes despite increasing pressure on power density, thermal efficiency, and long-term cost management as AI deployments expand.
Evaluation criteria misaligned with scaling needs
Organisations across Asia continue to assess infrastructure partners based on baseline requirements such as security and reliability. However, the study finds that their main operational challenges lie elsewhere, particularly in scalability, cost efficiency, and access to specialised expertise.
This mismatch slows progress, as companies prioritise familiar criteria rather than capabilities that directly support AI deployment at scale.
Singapore shows maturity but faces capacity constraints
Singapore stands out as one of the more advanced markets in the region. Around 40% of organisations have reached the “Integrator” stage, indicating stronger early execution compared to the regional average.
However, the transition to full leadership remains limited. Only 3% of organisations in Singapore have reached the “Leader” stage of AI infrastructure maturity.
As adoption matures, the constraints have shifted. Infrastructure capacity, access to specialised talent, and investment discipline are now the primary barriers to scaling AI workloads.
Mingcheng Lim, Country Head for Singapore at ST Telemedia Global Data Centres, said, “For Singapore, AI adoption is relatively mature; the defining challenge now is scaling deployments fast enough to support real-world demand. Whether the country can maintain its lead in the region will depend on whether infrastructure capacity, specialist expertise and investment approaches can evolve at the same pace as AI workloads.”
Awareness does not translate into action
Despite relatively high awareness of sustainability in Singapore, driven in part by regulatory expectations, it remains a low priority in infrastructure decision-making. Organisations continue to focus on immediate performance and cost considerations, even as long-term efficiency becomes more critical to scaling AI responsibly.
Across both Singapore and the wider region, the study points to a consistent pattern. Organisations recognise the need for scalable, high-density infrastructure and specialised expertise, but continue to prioritise baseline requirements that do not directly address their scaling challenges.