No organization can afford complacency while competitors harness artificial intelligence (AI) technologies to innovate and improve. But this enthusiasm is tempered by the realities of implementation and integration, coupled with fear of over-depending on rapidly evolving AI cloud service providers.
Business and IT leaders know that speed is crucial to gaining or preserving competitive advantage. The breakthroughs from cloud-based leaders such as OpenAI, Microsoft, Amazon Web Services, Google, and others are impressive, but their focus on outdoing each other and the huge investments needed to do so might be obscuring the more measured approach their customers prefer.
Many CIOs and CTOs are assessing the risks of placing too much faith in public cloud AI platforms. Substantial numbers of organizations will always be wary of service providers or unable to fully entrust their fate to them, especially in such a rapidly changing field. Others may fear vendor lock-in and the prospect of escalating licensing fees to fund large-scale AI investments.
Controlling your own destiny
Addressing concerns related to security, infrastructure, ethics, trust, and financial viability is essential for harnessing the transformative power of AI.
Many organizations, especially in regulated industries, are loath to risk compliance violations due to data transfer. Others worry about vendors eager to harvest data to feed the insatiable demands of large language models. That’s why the desire to control their own destiny depends in varying degrees on their own internal capabilities.
That desire is tempered by many challenges, including the often huge cost of building custom environments and integrating with existing infrastructure. That may help explain why just under half of the companies that participated in a recent Foundry survey have a dedicated AI budget and even fewer believe they have the right data and technology in place to use AI effectively1.
Almost all of the survey participants reported challenges in implementing AI initiatives, including lack of in-house expertise, lack of a compelling business case or investment justification, competing priorities, and the cost of implementing AI in the existing technology stack, among others. Moreover, no AI application has managed to achieve a satisfaction level exceeding 64%.
Rather than mortgaging IT budgets and surrendering data to hyperscalers leading the AI revolution, forward-thinking organizations need options that enable them to efficiently build and run LLMs in house.
Integrated tech stack for simplifying AI implementation
Platforms such as ASUS’s innovative AI POD offer a promising alternative.
The ASUS AI POD integrates 72 NVIDIA Blackwell Tensor Core GPUs and 36 NVIDIA Grace CPU Superchips within a unified NVIDIA NVLink domain, which interconnects GPUs and CPUs for the high-speed, low-latency communication essential for efficient parallel processing.
The turnkey, optimized stack from ASUS delivers an enterprise-ready, fully integrated platform that simplifies AI deployment, secures sensitive data, and accelerates time to value — all without traditional cloud vendor lock-in.
Learn how ASUS AI POD can reduce time-to-value and support diverse teams.
1 Foundry, “AI Priorities Study 2025,” https://foundryco.com/tools-for-marketers/research-ai-priorities/
