AI will Transform the Enterprise. But There are Some Tough Infrastructure Challenges to Solve First

There’s little historical precedent for the speed at which AI has evolved and swept through the enterprise technology landscape. As AI continues to mature, it will increasingly power essential business processes, at scale. From engineering and R&D to finance, sales, and customer support, virtually every function stands to benefit from AI’s capacity for faster decision-making, increased productivity, and decreased operational costs.

Yet, as integral as AI promises to become, integrating it effectively into an organization’s unique technology ecosystem is no small undertaking, because:

1. Legacy infrastructure isn’t AI ready
   One of the most significant barriers to enterprise AI adoption is outdated data center infrastructure that can’t easily accommodate resource-intensive AI workloads. CPU resources, storage and network bandwidth are already operating at, or near, full capacity. But simply adding more racks of AI-capable hardware isn’t the answer when data center floor space is in short supply, and power and cooling demands are already concerningly high. Meanwhile, maintaining older infrastructure is a drain on funding and manpower that diverts resources from emerging AI initiatives.
   
   
2. AI is Increasing demand for confidential computing
   Trust can make or break any new technology and as AI becomes a ‘killer application’ safeguarding its confidentiality and integrity is paramount. Many cutting-edge AI deployments rely on heterogeneous hardware—CPUs, GPUs, and specialized accelerators—spread across multiple sites. Ensuring a secure, trusted boundary in these complex environments calls for features like Secure Encrypted Virtualization (SEV). An AI-ready data center must integrate such capabilities at scale as part of a robust, end-to-end security posture.
   
   
3. It’s difficult to invest decisively while staying flexible
   AI initiatives generally split into two categories: productivity enhancements, like AI chatbots and workflow augmentation tools, and innovation, like AI customer service tools, IT observation tools, and customer facing AI products and services. But each carries different infrastructure requirements, ranging from CPU-based inference workloads to GPU-intensive training for cutting-edge developments. Coupling this diversity of requirements with rapid advances in compute, networking, and AI software complicates cost-performance calculations, further underscoring the need for a flexible infrastructure strategy. This flexibility can be achieved by choosing open standards AI solutions that don’t lock customers into closed ecosystems. This means the next major development in AI, regardless of its source, remains accessible. 

AI is reshaping the modern enterprise

AI is poised to become embedded across every department and industry vertical, adapted to specific tasks, and specialized by domain.

Thanks to its broad set of compute engines — from CPUs, GPUs, and accelerators to open-source software offerings — AMD is uniquely positioned as a strategic AI partner for enterprises. AMD is also committed to open standards and open ecosystem AI development, with strong partnerships across AI technology leaders in compute, networking, software, and more. This holistic approach not only helps customers modernize their technology infrastructure. It also empowers them to accelerate time-to-results, scale AI initiatives across the organization, and plan a long-term future of AI success. As AI reshapes the modern enterprise, AMD is accelerating business outcomes and enabling sustained success with high-performing foundational technologies and a collaborative ecosystem.

For a deeper dive into navigating these challenges to accelerate business outcomes and enable sustained success with AI, tune in to Advancing AI 2025.