The hype around artificial intelligence (AI) has been persistent for years. Today, companies across the Asia-Pacific (APAC) region are under pressure to turn that hype into real, measurable return on investment (ROI). As the technology continues to evolve, organisations must move beyond pilot projects and isolated use cases to align AI with long-term business goals. The shift now is from experimentation to impact.
Joe Hsieh, chief operating officer of ASUS, underscores the importance of building a purpose-driven AI ecosystem—one that’s rooted in strategy, sustainability, and human-centric design.
“Building a purpose-driven AI ecosystem means ensuring that AI development and deployment are anchored in meaningful, long-term goals,” Hsieh said. “It’s not just about short-term efficiency or automation, it’s about using AI to solve real-world problems, elevate human potential, and enable sustainable business growth.”
This approach, he noted, is particularly crucial in the diverse APAC landscape, where digital maturity and regulatory priorities vary greatly by market.
“We’re seeing a strong push toward Sovereign AI, where countries are encouraging enterprises to keep data local and invest in domestic AI infrastructure, such as AI servers and data centres,” he said.
Hsieh views this movement not just as a regulatory requirement, but as a foundation for building trust and resilience in data management.
“Devices that support local AI processing, like AI PCs, are becoming key enablers of privacy, performance, and responsiveness in the workplace. These trends collectively support a more agile, secure, and meaningful AI strategy across the enterprise,” he added.
Common pitfalls in AI adoption
When asked about the common pitfalls companies face when transitioning from AI pilots to enterprise-wide impact, Hsieh pointed to a lack of scalability and data readiness.
“Starting with isolated AI pilots that never scale is a common challenge,” he said. “Organisations often lack a clear roadmap to integrate those AI tools into core business operations.”
Another major barrier? Poor data quality.
“Without clean, accessible, and well-governed data, even the most advanced AI models struggle to deliver results.”
In APAC, the diversity of regulatory environments adds further complexity. Hsieh noted that many companies are now investing in local AI infrastructure to address compliance and performance needs.
“Security is also often underestimated,” he warned. “As enterprises adopt edge computing and deploy AI-enabled devices across the workforce, it’s critical to ensure those endpoints are protected. Privacy, durability, and trust must be built in from the start.”
Disconnected teams are another hurdle: “When teams across departments aren’t aligned, adoption stalls,” he said. “Business value comes when AI is approachable, understandable, and tailored to real operational needs.”
He stressed the importance of working with trusted partners to implement end-to-end solutions—from hardware to software and services.
“A unified, end-to-end approach enables organisations to unlock the full potential of AI while staying aligned with their goals, compliance needs, and user expectations.”
Accessibility comes from designing AI strategies with the end user in mind. Joe Hsieh
Ensuring AI accessibility and relevance
For AI to succeed, Hsieh emphasised that it must be accessible and relevant to users at all levels and across industries.
“Accessibility comes from designing AI strategies with the end user in mind,” he said. “That means moving away from a purely technical mindset and focusing instead on how AI can solve practical problems across departments—whether it’s automating workflows in HR, enhancing decision-making in finance, or optimising supply chains in manufacturing.”
Hsieh highlighted the growing adoption of AI-enabled devices that can perform processing locally, especially crucial in scenarios with sensitive data or unreliable internet connectivity.
“This kind of infrastructure makes AI more tangible and usable, especially for teams without deep technical expertise.”
He added that adaptability is essential in a region as diverse as Asia.
“A one-size-fits-all strategy won’t work. Organisations need modular, adaptable AI frameworks that can scale and adjust to local needs, industry regulations, and user expectations.”
Above all, trust remains central: “When people understand how AI supports their goals, adoption follows naturally.”
A balancing act: innovation and responsibility
While the pace of AI innovation is thrilling, Hsieh cautioned that it must be matched with responsibility.
“The pace of AI innovation is exciting, but it also demands a thoughtful approach,” he said. “In the rush to implement new tools, it’s easy to overlook foundational issues like ethics, security, and long-term sustainability. For ASUS, true innovation is not just fast, it’s also responsible.”
He applauded efforts by APAC governments to strengthen data privacy and AI governance frameworks saying that “Businesses that prioritise responsible AI by being transparent in how models make decisions, protect user data, and minimise bias are better positioned to build long-term trust.”
Infrastructure investment also plays a critical role.
“Investments in local AI servers and data centres are helping enterprises align with government expectations while also improving performance and resilience,” Hsieh said. “Edge-based AI, such as AI PCs that process data locally, also supports this balance. It reduces the risks associated with data transfer and centralisation while improving efficiency, especially in remote or security-conscious environments.”
Build with intention
Leaders in Asia must especially pay attention to the diverse regulatory and cultural landscape. Joe Hsieh
To build a truly purpose-driven AI ecosystem, Hsieh advises tech leaders and CIOs to begin with clear intent. Hsieh said, “Define how AI can help achieve your company’s strategic goals, whether that’s enhancing customer experience, driving efficiency, or supporting sustainability.”
He emphasised the need for cultural awareness and regulatory alignment. “Leaders in Asia must especially pay attention to the diverse regulatory and cultural landscape. The growing government focus on building AI infrastructure can offer opportunities for partnerships and compliance.”
He also encouraged a balance between cloud and edge technologies. “AI PCs and similar endpoint technologies allow AI to run securely and efficiently close to users, especially in sectors handling sensitive data or facing connectivity challenges like healthcare, education, and public services.”
Equally important is fostering a culture of AI literacy and collaboration: “Bring together stakeholders from across the business early, and ensure governance models evolve alongside your technology.”
“A purpose-driven AI ecosystem isn’t just about deploying the latest tools,” Hsieh concluded. “It’s about creating a clear, trusted foundation that drives long-term impact across the enterprise.”
