As enterprises transition AI technology from early experimentation to real-world application, the core challenge shifts from "model design" to "execution." The availability of practical operational capabilities, such as infrastructure preparation, data governance, and organizational strength, is becoming crucial in determining success or failure. During this transformation phase, HPE is seeking differentiated development through a strategy centered on hybrid cloud and AI infrastructure, aiming to bridge the execution gap in enterprise AI applications.
To transform AI experiments into repeatable results, HPE is heavily promoting the "AI Factory" concept. This concept aims to restructure traditional data centers into factory-like structures that optimize ultra-high-speed computing, energy efficiency, and data-driven operations, supporting the entire AI lifecycle from training to inference, deployment, and monitoring through automated pipelines. The AI Factory market, centered on accelerated computing technologies and high-performance network architectures, is projected to surpass $1 trillion (approximately 1440 trillion Korean won) by 2031, with an estimated annual growth rate of 38%.
Another foundation for AI execution is the hybrid cloud. Given factors such as data location, latency, sovereign regulation, and cost, there is increasing pressure to flexibly deploy AI models across multiple environments—on-premises, edge, and public cloud—rather than in a single environment. To address this, HPE has integrated telemetry, orchestration, and centralized network control capabilities into its GreenLake infrastructure to provide a unified management platform, and recently released three major solutions for GPU optimization and multi-tenant operations.
The partnership with NVIDIA is also a key pillar in strengthening execution capabilities. The "NVIDIA AI Computing by HPE" portfolio, formed through their strategic collaboration, encompasses private cloud AI solutions, large-scale scalable infrastructure, and national-level security and data sovereignty platforms. Of particular note is HPE's AI Factory, which employs a federated architecture to achieve unified operation across GPU generations, preparing for future scalability.
Real-world examples are already emerging. In response to public safety and transportation challenges arising from a population surge, the small Colorado town of Weil introduced HPE's smart city solution. AI-powered virtual civil servants handle citizen inquiries and document verification, while sensor systems provide early warnings of wildfire risks. This is considered a prime example of a hybrid cloud-driven agile execution system achieving high-level AI applications even in resource-constrained environments.
The importance of security and data governance is becoming increasingly apparent alongside the rapid adoption of AI. An HPE survey reveals that 74% of IT leaders believe protecting AI-generated data is crucial, especially since AI models store sensitive data during training, posing a risk of malicious information being repeatedly reproduced. To address this, HPE protects models and data within independent firewalls and collaborates with companies like NVIDIA, Intel, and AMD to enhance confidential computing capabilities.
The networking and storage sectors, pillars of AI infrastructure, are also undergoing significant upgrades. HPE is reducing network latency through AI-dedicated switches and transforming storage from simple memory into a "data intelligence layer" that supports real-time processing and classification. This will become a core element of AI-agent-based job automation.
This expansion of technology strategies is driving the development of a collaborative ecosystem. Through its Unleash AI Partner Program, HPE collaborates with independent software vendors in cybersecurity, regulatory compliance, and orchestration, enabling customers to more easily apply their validation frameworks to complex AI environments. For example, in the Weill case, the customized agent and platform were provided by Kamiwaza and SHI International, respectively, while HPE provided integration and operational support.
In short, AI is facing a transition from experimentation to scalable commercial results. This requires moving beyond traditional general-purpose data centers and building a new infrastructure model: the AI factory. HPE is leading this transformation by collaborating with NVIDIA on design, building an advanced partner ecosystem, and providing a unified platform based on hybrid cloud. These cases clearly demonstrate that when enterprises attempt to substantially apply AI in real-world business scenarios, what they truly need is not complex technology, but rather the infrastructure and strategy to turn their ideas into "execution."
