HEADQUARTERED in Singapore, ST Telemedia Global Data Centres recently released Philippines-specific findings revealing a market with strong AI ambition but significant constraints in scaling capability.
Its latest research study, titled “Mind the Gap: Bridging the AI Infrastructure Readiness Divide,” shows that 79 percent of Philippine organizations are now in the “Builder” stage of AI adoption, actively deploying early operational AI solutions. However, only 2 percent have progressed to the “Integrator” stage, while 19 percent remain in the “Explorer” phase, highlighting a sharp drop-off beyond initial deployment and signaling structural barriers to scaling AI adoption.
While momentum remains strong, 71 percent of respondents cite insufficient compute capacity, storage or network bandwidth as the top barrier to advancing their AI initiatives. Many organizations report that their networks can support basic AI workloads, but 71 percent also say latency, bandwidth constraints and network bottlenecks are already limiting performance and reducing their ability to run more data-intensive or mission-critical AI models.
Talent shortages are compounding these challenges. More than three-quarters, or 76 percent, of organizations report critical AI talent gaps, while 53 percent acknowledge they lack the in-house expertise required to manage complex AI infrastructure and operations. Beyond specialist skills, workforce readiness remains uneven, suggesting adoption challenges extend to organizational change and operational maturity.
Despite rising energy and cooling demands driven by AI workloads, sustainability considerations remain secondary for most organizations when evaluating infrastructure options. Although 27 percent of organizations say ESG goals will actively shape or be central to their future plans, 64 percent of organizations across Asia continue to prioritize performance or cost, even as power density, thermal efficiency and long-term total cost of ownership become increasingly critical factors in scaling AI responsibly.
The study also highlights a persistent disconnect between how organizations evaluate infrastructure partners and the capabilities they actually need to scale AI. Across Asia, organizations continue to prioritize baseline requirements such as security and reliability even as they identify scalability, operational expertise and cost efficiency as their most significant challenges.
The findings suggest Asia’s next phase of AI progress will be defined not only by ambition but also by execution capability. In Singapore, sustaining leadership will depend on infrastructure strategies focused on scalability, resilience and speed that enable organizations to convert early AI momentum into long-term competitive advantage.
The research also points to a growing future-readiness gap. Nearly half of respondents expect AI workloads to grow by more than 50 percent over the next three years. However, less than 5 percent are prepared to scale high-demand AI workloads. At the same time, 86 percent report investing 5 percent or less of their total IT budgets in AI, raising doubts about whether current investment levels and infrastructure strategies are aligned with future growth prospects.
