Powering Future-Ready Computing with Workstations: Built for AI, Built for You

Executive summary

AI is rapidly reshaping compute needs across Asia/Pacific, positioning workstations as mission-critical infrastructure required to meet specialized demands across industries. Workstations deliver significantly higher productivity, reliability, and security than traditional PCs, especially for AI-driven and compute-intensive work. Organizations are accelerating workstation adoption to support on-premise AI development while protecting sensitive data, reducing latency, and ensuring consistent performance across engineering, design, scientific, creative, and financial workloads.

Although cost perceptions persist, experienced users consistently report strong long-term ROI through durability, energy efficiency, and reduced disruption, making total cost of ownership for workstations very compelling. The biggest obstacle is no longer technology, it is outdated IT policy. Modernizing these policies is now essential for leaders seeking to scale AI efficiently, securely, and with measurable business impact. Workstations have emerged as strategic investments enabling organizations to unlock and fuel AI-powered innovation.

The drive for progress: why modernization is a business imperative

Across Asia/Pacific, organizations are prioritizing technology modernization to stay competitive, with artificial intelligence driving much of this transformation. Yet progress is challenged by significant technical hurdles. Security breaches remain the region’s most persistent threat, while inadequate system performance limits the ability to run modern, compute-intensive workloads. At the same time, data mismanagement, software defects, and widening talent shortages continue to impede operational efficiency. Addressing these challenges is essential as organizations work to improve IT support, upskill teams, and enhance digital experiences while safeguarding data integrity and compliance. By focusing on these key priorities and understanding the day-to-day technical computing challenges, businesses can unlock greater innovation and resilience.

Top 5 technology priorities for the next 12 months

1. Technology modernization
2. Improve IT support
3. Improve technology skills across the business
4. Improve customer-focused digital experiences
5. Improve data governance and IT security

Top 5 greatest technical computing challenges

1. Security breaches and infections
2. Inadequate system performance/lack of workstation-grade hardware
3. Data mismanagement, loss, or corruption
4. Software bugs and defects
5. Skills/talent gap

The productivity powerhouse: how workstations unlock potential

• Workstations deliver superior performance and reliability, making them an ideal choice for compute-intensive tasks and AI/ML workloads.
• Workstations are ISV certified offering dependable local compute power, reducing latency, errors, downtime while also minimizing reliance on the cloud.
• These are real productivity gains ensuring long-term cost efficiency and value from workstations.

Top 5 reasons to choose a workstation over a laptop or PC

1. High performance for demanding workloads
2. Greater reliability, reduced risk of downtime or data loss
3. Better support for AI/ML or data science workloads
4. Long-term cost efficiency
5. Certified for mission-critical/ISV applications

Which of the following workstation benefits impacts your organization the most?

1. Improved end-user experience and productivity
2. Reduced cloud costs and latency due to greater local computing power
3. Reduced downtime due to reduced software issues

Fueling the future: workstations driving AI momentum

AI has rapidly become the top technical computing use case for workstations across Asia/Pacific. The survey reveals that organizations are employing workstations for every major stage of the AI lifecycle — from data preparation (62%) and foundational model training (60%), to model fine-tuning (59%), deployment (44%), and inference (29%). This widespread adoption points to the essential role workstations now play in enabling complex, high-value AI work.

On-premise power: performance, reliability, and security

With cloud costs spiraling across the region, in addition to data sovereignty and privacy concerns, the desire to do more AI development on-premise is growing.

IDC’s view is that a hybrid strategy will ultimately be essential, leveraging the technical strengths of both on-premise capability and the flexibility and elasticity of cloud, public or private.

Workstations are trusted for on-premise technical computing, with “data security and privacy” and “AI development speed and agility” leading as the primary factors for local deployment.

Primary factors influencing your choice of AI workload location

1. Data sovereignty/regulatory compliance
2. AI development speed and agility
3. Scalability and resource flexibility

Top advantages when using workstations to run AI locally

1. Performance and speed
2. Data security and privacy
3. AI governance and compliance

Breaking cost myths and supporting C-suite priorities

Although cost perceptions for workstations exist, IDC research shows workstation users report strong long-term ROI for workstations through durability, energy efficiency, and reduced disruption.

IDC finds these cost perceptions mainly due to traditional procurement approaches driven by initial budget assessment versus long-term value. When factors like fewer delays, lower maintenance, and control over compute costs are considered, the total cost of ownership (TCO) becomes very compelling. All markets in the region plan to expand workstation fleets, showing rising confidence in their value.

When we reconsider the top technical priorities of the C-suite, IDC observes a direct correlation to the inherent benefits of workstations:

1. Technology modernization — workstations are uniquely suited to cater for the current demand for AI development.
2. Improve IT support — by advancing AI capabilities, especially in the AIOps area, IT support can be both improved and extended to workstations concurrently.
3. Improve technical skills across the business — the more rapid development of AI tools will allow AI to pervasively grow across the organization, thereby providing the necessary skills.
4. Improve customer-focused digital experiences — workstations help drive innovation across the organization.
5. Improve data governance and cybersecurity — this is both intrinsic to workstations as well as being an output from the use of workstations.

Paving the way for innovation

The survey confirms that organizational and workplace barriers — most notably restrictive IT policies and insufficient internal support — represent key obstacles to broader workstation adoption. Many organizations cite rigid standards and a lack of adequate IT resources as significant challenges, while management support for modernization efforts sometimes remains limited.

Additionally, for companies not currently utilizing workstations, the most common reasons include a lack of workstation offerings from their primary device vendor, uncertainty about the added value of workstations versus standard PCs, and concerns over perceived high costs. Closing these gaps — in policy, knowledge, and support — is vital for realizing the full benefits of workstation-centric strategies and resulting ROI.

IDC believes it is time to reconsider these policies that restrict workstation adoption as organizations rapidly work to deliver value from their AI projects. In today’s AI-driven ecosystem, workstations have emerged as strategic investments that fuel innovation.

Are there any workplace or organizational barriers to adopting workstations?

1. Company IT policies or standards
2. Insufficient IT support/maintenance
3. Lack of management support

Industry: manufacturing, engineering, architecture

Introduction: Manufacturing, Engineering, Architecture emerges as the leading sector in the survey that strongly relies on workstation-grade computing. With demanding workloads that require ISV-certified performance and exceptional reliability, these systems provide the foundational capabilities necessary for the industry’s most critical and complex operations.

Current adoption: Organizations in this sector consistently choose workstations due to their high performance for resourceintensive applications and superior reliability, both of which are essential for uninterrupted workflows.

Primary reasons for choosing a workstation in the manufacturing, engineering, architecture sector

1. High performance for demanding workloads
2. Greater reliability, reduced risk of downtime or data loss
3. Greater scalability and customization options

Challenges

The survey notes that manufacturing, engineering, and architecture organizations face technical computing challenges such as security, addressing skills gaps, and lack of a suitable compute platform — factors especially relevant in environments where protection of intellectual property and productivity are incredibly important.

Technical computing challenges relevant to industry

1. Security breaches and infections
2. Skills/talent gap
3. Inadequate system performance/lack fo workstation-grade hardware

Workplace/organizational barriers for workstations adoption

1. Company IT policies or standards
2. Insufficient IT support/maintenance
3. Difficulty integrating wiht existing systems

Why workstations

More than half of Manufacturing, Engineering, Architecture firms undertake AI development using on-premise or colocation facilities, reflecting industry concern over security and loss of intellectual property. Survey data shows these organizations are especially active in model fine-tuning (64%), data preparation (61%), and foundational model training (58%), — tasks where workstation performance is essential.

Main challenges in running AI workloads on the cloud

1. Data security concerns
2. Concerns over latency and real-time processing
3. Data privacy or compliance restrictions

Primary reasons for planning to use workstations for AI workloads

1. Faster processing and performance
2. Flexibility for compute power sizing, experimentation, and prototyping
3. Security and compliance requirements

Future outlook

For compute-intensive/AI-related tasks, 93% of manufacturing, engineering, and architecture organizations state that the productivity of workstation users is higher than those of non-workstation users.

Fleet expected to grow in the next five years. As production complexity and design file sizes continue to expand, manufacturing companies expect to further invest in powerful workstations to maintain operational agility and meet next-generation engineering demands and drive innovation.

Industry: web and technology

Introduction: Web and Technology emerges as one of the leading sectors in the survey that strongly relies on workstation-grade computing. Professionals in this field depend on advanced capabilities and exceptional performance to drive higher productivity across a broad range of use cases including digital content creation, software engineering, as well as AI development.

Current adoption: Web and Technology companies rely on workstations for their ability to handle resource-heavy workloads, including AI, with exceptional reliability — critical for uninterrupted development and innovation cycles.

Primary reasons for choosing a workstation in the web and technology sector

1. High performance for demanding workloads
2. Better support for AI/ML or data science workloads
3. Enhanced security and compliance features

Challenges

The survey notes that web and technology organizations face technical computing challenges such as data mismanagement or loss, security breaches, and inadequate system performance — factors especially relevant in environments where software drives the business.

Technical computing challenges relevant to web and technology

1. Data mismanagement, loss, or corruption
2. Security breaches and infections
3. Inadequate system performance/ lack of workstation-grade hardware

Workplace/organizational barriers for workstations adoption

1. Company IT policies or standards
2. Insufficient IT support/ maintenance
3. Budget constraints

Why workstations

More than half of web and technology firms undertake AI development using on-premise or colocation facilities, reflecting industry concern over security and loss of intellectual property. Survey data shows these organizations are especially active in data preparation (63%), foundational model training (61%), and model fine-tuning (55%) — tasks where workstation performance is essential.

Main challenges in running AI workloads on the cloud

1. Data privacy or compliance restrictions
2. Data security concerns
3. Concerns over latency and real-time processing

Primary reasons for planning to use workstations for AI workloads

1. Faster processing and performance
2. Flexibility for compute power sizing, experimentation, and prototyping
3. Security and compliance requirements

Future outlook

For compute-intensive/AI-related tasks, 93% of web and technology organizations state that the productivity of workstation users is higher than those of non-workstation users.

Fleet expected to grow in the next five years. As project complexity and file sizes continue to grow, web and technology organizations expect to further invest in powerful workstations to maintain development agility, meet innovation demands, and deliver next-gen digital experiences.

Industry: banking, financial, services, and insurance

Introduction: The Banking, Financial Services, and Insurance (BFSI) sector has emerged as a leading adopter of workstations. Professionals in this field depend on advanced capabilities and exceptional performance of workstations to drive higher productivity across a broad range of financial use cases.

Current adoption: BFSI organizations choose workstations for data security, reliability, long-term cost efficiency, and high performance for demanding workloads. Financial teams use these systems to enhance productivity across risk modeling, digital engineering, product development, and AI-powered analytics and innovation.

Primary reasons for choosing a workstation in BFSI

1. Greater reliability, reduced risk of downtime or data loss
2. Long-term cost efficiency (durability, lower maintenance)
3. High performance for demanding workloads

Challenges

The survey notes that BFSI organizations face technical computing challenges such as hardware failures and overheating, software bugs and defects, and security breaches and infections — factors especially relevant in data-intensive, risk-sensitive financial environments.

Technical computing challenges relevant to BFSI

1. Hardware failures and overheating
2. Software bugs and defects
3. Security breaches and infections

Workplace/organizational barriers for workstations adoption

1. Lack of awareness of workstation benefits
2. Insufficient IT support/ maintenance
3. Company IT policies or standards

Why workstations

More than half of BFSI firms undertake AI development using on-premise or colocation facilities, reflecting industry concern over security and loss of intellectual property. Survey data shows these organizations are especially active in data preparation (75%), model fine-tuning (67%), and AI deployment (56%) — tasks where workstation performance is essential.

Main challenges in running AI workloads on the cloud

1. Data privacy or compliance restrictions
2. Data security concerns
3. Concerns over latency and real-time processing

Primary reasons for planning to use workstations for AI workloads

1. Faster processing and performance
2. Flexibility for compute power sizing, experimentation, and prototyping
3. Security and compliance requirements

Future outlook

For compute-intensive/AI-related tasks, 97% of BFSI organizations state that the productivity of workstation users is higher than those of non-workstation users.

Fleet expected to grow in the next five years. With increasing workload complexity and escalating data demands, BFSI organizations plan to continue investing in advanced workstations to sustain analytical agility, strengthen data security, and drive financial innovation.

India

For organizations in India, high performance for demanding workloads — especially stronger support for AI/ML and data science use cases — remains the primary driver for choosing workstations. In a price-versus-value–sensitive market like India, long-term cost efficiency has also become a key factor influencing workstation adoption.

Primary reasons for choosing a workstation in India

1. High performance for demanding workloads
2. Better support for AI/ML or data science workloads
3. Long-term cost efficiency (durability, lower maintenance)

For compute-intensive/AI-related tasks

95% of India organizations state that the productivity of workstation users is higher than those of non-workstation users, confirming their confidence in the power of workstations

Challenges

The survey notes that organizations in India face technical computing challenges such as security breaches, hardware failures, and software bugs — issues that workstations coupled with ISV certification can address, while meeting current and future needs of the organization.

Technical computing challenges faced by organizations in India

1. Security breaches and infections
2. Hardware failures and overheating
3. Software bugs and defects

Workplace/organizational barriers for workstations adoption

1. Company IT policies or standards
2. Difficulty integrating with existing systems
3. Insufficient IT support/ maintenance

Why workstations

More than half of firms in India undertake AI development using on-premise or colocation facilities, reflecting industry concern over security and loss of intellectual property. Survey data shows these organizations are especially active in AI deployment (77%), data preparation (76%), and model fine-tuning (58%) — tasks where workstation performance is essential.

Main challenges in running AI workloads on the cloud

1. Data privacy or compliance restrictions
2. Data security concerns
3. Integration with existing infrastructure

Primary reasons for planning to use workstations for AI workloads

1. Faster processing and performance
2. Security and compliance requirements
3. Flexibility for compute power sizing, experimentation, and prototyping

Future outlook

Fleet expected to grow in the next five years. As organizations in India look to scale AI, closing the gaps — in IT policies, support, and addressing challenges around system integration — would be critical to sustain and drive innovation.