According to the research paper titled "The Edge Evolution: Powering Success from Core to Edge," Asia-Pacific (APAC) enterprises are realizing that centralized cloud architecture alone is unable to meet the increased demands of scale, speed, and compliance. It is crucial that businesses rethink and enhance infrastructure strategies to include edge services to stay competitive and compliant, and be ready for real-world AI deployment.
According to the IDC Worldwide Edge Spending Guide — Forecast, 2025, public cloud services at the edge will grow at a compound annual growth rate (CAGR) of 17% through 2028, with the total spending projected to reach US$29 billion by 2028. In addition, in the latest research paper, IDC predicts that by 2027, 80% of CIOs will turn to edge services from cloud providers to meet the performance and compliance demands of AI inferencing. This shift marks what is emerging in the paper as "The Edge Evolution."
The research paper further outlines how public cloud–connected systems combine the agility and scale of public cloud with the proximity and performance of edge computing, delivering the flexibility businesses need to thrive in an AI-powered future.
The AI infrastructure reality check
As generative AI moves from experimentation to execution, enterprises across APAC are confronting the limits of legacy infrastructure. Today, 31% of organizations surveyed in the region have already deployed GenAI applications into production. Meanwhile, 64% of organizations are in the testing or pilot phase, trialing GenAI across both customer-facing and internal use cases. However, this rapid momentum is exposing serious gaps in existing cloud architectures:
- Complexity of multicloud: 49% of enterprises struggle to manage multicloud environments due to inconsistent tools, fragmented data management, and challenges in maintaining up-to-date systems across platforms.
- Compliance trap: 50% of the top 1,000 organizations in Asia-Pacific will struggle with divergent regulatory changes and rapidly evolving compliance standards, and this will challenge their ability to adapt to market conditions and drive AI innovation.
- Bill shock: 24% of organizations identify unpredictable rising cloud costs as a key challenge in their GenAI strategies.
- Performance bottlenecks: Traditional hub-and-spoke cloud models introduce latency that undercuts the performance of real-time AI applications, making them unsuitable for production-scale GenAI workloads.
"AI is only as powerful as the infrastructure it runs on," said Parimal Pandya, Senior Vice President, Sales, and Managing Director, Asia-Pacific at Akamai Technologies. "This IDC research paper reveals how Asia-Pacific businesses are adopting more distributed, edge-first infrastructure to meet the performance, security, and cost needs of modern AI workloads. Akamai's global edge platform is built for this transformation — bringing the power of computing closer to users, where it matters most."
Daphne Chung, Research Director at IDC Asia-Pacific, added, "GenAI is shifting from experimentation to enterprise-wide deployment. As a result, organizations are rethinking how and where their infrastructure operates. Edge strategies are no longer theoretical — they're being actively implemented to meet real-world demands for intelligence, compliance, and scale."
Key findings for APAC:
- China scales GenAI with edge and public cloud dominance: 37% of enterprises have GenAI in production and 61% are testing, while 96% rely on public cloud IaaS. Edge IT investment is accelerating to support remote operations, disconnected environments, and industry-specific use cases.
- Japan accelerates AI infrastructure despite digital maturity gap: While only 38% of Japanese enterprises have GenAI in production, 84% believe GenAI has already disrupted or will disrupt their businesses in the next 18 months, and 98% plan to run AI workloads on public cloud IaaS for training and inferencing workloads. Edge use cases like AI, IoT, and operational support for cloud disconnection are driving infrastructure upgrades.
- India expands edge infrastructure to meet GenAI demand and manage costs: With 82% of enterprises conducting initial testing of GenAI and 16% leveraging GenAI in production, India is building out edge capabilities in tier 2 and 3 cities. 91% of GenAI adopters rely on public cloud IaaS, but cost concerns and skills gaps are pushing demand for affordable, AI-ready infrastructure.
- ASEAN embraces GenAI with edge-first strategies beyond capital hubs: 91% of ASEAN enterprises expect GenAI disruption within 18 months, with 16% having introduced GenAI applications into the production environment and 84% in the initial testing phase. 96% are adopting public cloud IaaS for AI workloads, while edge investment is rising to support remote operations and data control.
Building a cloud-connected future
To stay ahead, enterprises must modernize infrastructure across cloud and edge, aligning deployments with specific workload needs. Securing data through Zero Trust frameworks and continuous compliance is essential, as is ensuring interoperability to avoid vendor lock-in. By tapping into ecosystem partners, businesses can accelerate AI deployment and scale faster, smarter, and with greater flexibility.
Download the full IDC InfoBrief, commissioned by Akamai, "The Edge Evolution: Powering Success from Core to Edge", August 2025, IDC Doc #AP242522IB, to explore strategic insights and recommendations for building cloud-connected, AI-ready infrastructure across APAC.
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