Indian CIOs to boost edge adoption for GenAI by 2027: IDC

Nearly 80% of Chief Information Officers (CIOs) in the Asia–Pacific region are expected to adopt edge services from cloud providers by 2027 to meet the performance and compliance needs of generative AI (GenAI) workloads, according to new IDC research commissioned by Akamai Technologies.

The research paper, "The Edge Evolution: Powering Success from Core to Edge," highlights how the rising adoption of AI is prompting APAC enterprises to reevaluate their digital infrastructure. It finds that centralised cloud models alone are no longer adequate to support the speed, scale, and regulatory requirements of AI inferencing.

According to the IDC Worldwide Edge Spending Guide, public-cloud services at the edge are forecast to grow at a compound annual growth rate of 17% through 2028, with spending expected to reach USD 29 billion.

Adoption and Pressure Points

The report reveals that 31% of surveyed enterprises in APAC have already moved GenAI applications into production, while 64% are still in the testing phase. This growing momentum is putting strain on conventional cloud architectures and driving the shift to more distributed models.

Key infrastructure challenges include multi-cloud complexity (49%), changing compliance requirements, which are expected to impact 50% of the A1000 by 2025, unpredictable cloud costs (24%), and latency-related performance issues. These gaps are pushing organisations to integrate edge computing into their infrastructure plans.

Adoption rates vary across the region. In China, 37% of enterprises have GenAI in production, with another 61% in testing. Most of them—96%—rely on public-cloud infrastructure-as-a-service (IaaS). Japan is slower in deployment but is investing in AI, IoT, and disconnected environments. Meanwhile, countries in ASEAN are taking an edge-first approach to support decentralised operations.

India’s Edge Build-Out

In India, the focus is on expanding edge infrastructure beyond metro cities to manage costs and improve performance. Around 92% of enterprises in the country believe GenAI has already disrupted or will disrupt their operations in the next 18 months. Over half (56%) identify AI workloads as the primary driver for changes to their infrastructure.

While 16% of Indian enterprises have GenAI in production, 82% are still in pilot stages. Among adopters, 91% plan to use public-cloud IaaS for AI training and inferencing. Cost concerns and a shortage of skilled professionals are driving the demand for affordable and AI-ready infrastructure.

Similarly, Edge IT spending is expected to rise among Indian enterprises in 2025. The need to deliver GenAI services closer to customers and remote sites is resulting in new edge data centres in tier-2 and tier-3 cities. These support use cases in IoT, surveillance, content delivery, and real-time insights across sectors such as financial services, healthcare, retail, telecom, and digital-native businesses.

India’s geographic spread and uneven network quality, however, remain a concern. Locating compute closer to data sources is seen as a way to reduce connectivity costs and address performance issues caused by unreliable links.

“As GenAI transitions from experimentation to deployment, organisations must rethink where and how their infrastructure operates,” said Daphne Chung, Research Director at IDC Asia–Pacific.

Parimal Pandya, Senior Vice-President and Managing Director, Asia–Pacific at Akamai Technologies, said the findings show that businesses are shifting to edge-first models to handle the demands of modern AI workloads.

What Should the Enterprises Do?

The report recommends that enterprises align infrastructure plans with business goals. This includes assessing current estates, identifying workload needs, evaluating cloud providers, and updating plans as the business evolves.

A centralised management experience across platforms is advised to improve governance, security, and compliance, while avoiding vendor lock-in.

IDC suggests that workload placement is a critical first step. Enterprises should keep training activities on centralised or dedicated infrastructure and shift latency-sensitive inferencing and data pre-processing to the edge to meet data sovereignty and performance needs.

The report advises enterprises to adopt edge orchestration and open standards to manage distributed compute environments. It also stresses the importance of integrating security and data governance into edge deployments.

Key security practices include zero-trust architecture, encryption, real-time monitoring, and a secure-by-design approach. Data-management strategies should cover data lifecycle, sovereignty, and governance, with AI and machine learning used to automate analytics and maintain integrity.

Automation is recommended to simplify deployment and manage hybrid and multi-cloud operations. Cost management should be treated as an ongoing process. IDC advocates for FinOps practices to track usage, uncover savings, and optimise resource allocation. This should be supported by analytics tools and GenAI-powered insights.

For India, IDC recommends partnerships that enable cost-effective compute and storage, alongside strategies to lower egress charges. Enterprises are urged to modernise infrastructure from core to edge with a focus on portability, operational simplicity, security, and latency.