Distributed AI drives demand for AI PCs and workstations in APAC enterprises

Enterprise AI adoption across Asia Pacific is moving into a more implementation-driven phase, with organisations increasingly balancing on-device intelligence and high-performance computing for advanced workloads. Dell Technologies said this shift is shaping a more distributed AI environment, where workloads are split across endpoints, edge systems, and data centres.

According to two IDC InfoBriefs commissioned by Dell Technologies and Intel, 48% of organisations with more than 500 employees in APAC have already deployed AI PCs, while 95% expect workstations to play a critical or important role in AI initiatives within the next two years. The findings point to a growing emphasis on matching compute resources to specific workload requirements rather than relying on a single infrastructure model.

AI PCs move into core enterprise deployment

AI PCs are emerging as a primary layer for enterprise AI execution, particularly for everyday workflows. These systems enable AI processing directly on the device, reducing reliance on cloud connectivity while improving responsiveness and control over data.

The IDC research shows that 89% of organisations in APAC now consider AI capabilities a key factor in future PC purchasing decisions. Adoption is already more pronounced in some markets, with 54% of organisations in Singapore deploying AI PCs. Southeast Asia overall is ahead of the regional average by 6%, supported by fewer legacy constraints and stronger infrastructure in key markets.

Operational gains are beginning to surface. Organisations with more than half of their PC fleet equipped with AI capabilities report saving an average of 2.17 hours per employee per day, translating to a 30% increase in productivity compared to traditional PCs. Use cases range from real-time collaboration and automated report generation to natural language search and content creation.

The shift is also influencing spending behaviour. Across APAC, 65% of organisations indicate a willingness to pay at least a 10% premium for AI PCs, reflecting their role as part of core enterprise infrastructure rather than incremental upgrades. At the same time, 84% expect these systems to improve productivity, while 78% cite security benefits and 77% point to cost advantages from running AI workloads locally.

Workstations remain central to advanced AI workloads

While AI PCs extend AI capabilities across the workforce, workstations continue to anchor more demanding and specialised workloads. These systems are used for model development, simulation, rendering, and data preparation, where sustained performance and reliability are required.

The research indicates that 50% of organisations would choose workstations as their preferred device for AI development, while 97% agree they enable innovation by supporting advanced technologies such as AI and machine learning models. In Southeast Asia, 92% of organisations report higher productivity among workstation users, and 52% expect workstation deployment to grow over the next five years.

Workstation usage is concentrated in areas such as data preparation at 66%, model fine-tuning at 62%, and foundational model training at 55%. These figures reflect the continued reliance on high-performance systems for the full AI lifecycle, from development to deployment and inference.

This also shifts procurement considerations beyond upfront cost. Organisations are increasingly evaluating workstations based on lifecycle longevity, scalability, and performance consistency, particularly as AI workloads move closer to production environments.

Compute continuum shapes enterprise AI deployment

Dell Technologies frames the combined role of AI PCs and workstations as an “AI compute continuum”, where different device classes serve distinct but complementary roles across the enterprise.

IDC notes that advances in model optimisation are enabling more capable AI workloads to run on endpoints, while demand for high-performance systems remains strong for complex development tasks. This dual trajectory reinforces a distributed approach, where organisations deploy AI across multiple layers rather than centralising it in a single environment.

The model places greater emphasis on workload placement. Everyday productivity and user-facing applications are increasingly handled on AI PCs, while compute-intensive tasks continue to rely on workstation-class systems. Together, these platforms form the operational base for scaling AI across enterprise environments.