Hewlett Packard Enterprise is leveraging strategic partnerships to accelerate the adoption of artificial intelligence across a wide range of industries, navigating a landscape where the pace of technological change has compressed traditional enterprise adoption cycles from years to months. The company is focusing on providing the essential infrastructure, cloud services, and networking backbone required for modern AI, moving beyond its historical focus on printers and laptops to meet the urgent demands of businesses implementing AI strategies.
Chad Smykay, AI CTO and Distinguished Technologist at HPE, emphasizes that the conversation around AI has shifted dramatically. Customers are no longer questioning the need for an AI strategy but are instead focused on the practical details of implementation, governance, and data quality. This shift requires a collaborative approach, where HPE and its partners work together to provide comprehensive solutions that address specific business problems, from real-time medical imaging analysis in healthcare to AI-driven network operations. Through its GreenLake platform and strategic acquisitions like Juniper Networks, HPE is positioning itself as a central player in the enterprise AI revolution, enabling organizations to deploy AI solutions at scale while managing regulatory complexities and controlling costs.
Evolving from Big Data to AI
The transition from CPU-based big data processing to GPU-powered AI represents a fundamental shift in how organizations handle information and make decisions. Smykay’s career, which has spanned this evolution, provides him with a unique perspective on the challenges and opportunities presented by this technological leap. His early experiences with big data projects, such as implementing fraud detection systems for banking clients, offered valuable lessons in how businesses adopt and integrate new technologies. These systems, once considered revolutionary, are now commonplace, illustrating the trajectory that AI is currently following across numerous sectors.
This evolution is not merely a hardware upgrade but a change in the entire data processing paradigm. Smykay’s time at Rackspace, where he was part of the company’s significant growth, gave him a deep understanding of what it takes to scale a technology and a business. This experience now informs his strategy at HPE, which serves a diverse client base across industries like life sciences, manufacturing, financial services, and energy. Each of these sectors has its own set of regulatory and operational requirements, demanding a flexible and adaptable approach to AI implementation. HPE’s focus is on providing the foundational infrastructure that allows these varied industries to harness the power of AI effectively.
Infrastructure and Cloud Services
HPE’s GreenLake platform is a key component of its strategy to facilitate enterprise AI adoption. By offering a cloud service model, GreenLake allows customers to consume AI-capable resources on demand, which helps to address the significant capital expenditure concerns that can often hinder AI initiatives. This approach provides businesses with predictable operational costs and the flexibility to scale their AI resources as needed. The platform is designed to meet the needs of a wide array of industries, each with unique operational and regulatory constraints.
Strategic Acquisitions for Enhanced Capability
The recent $14 billion acquisition of Juniper Networks highlights HPE’s commitment to building a comprehensive AI-enabled infrastructure. The integration of Juniper’s AI-driven network operations capabilities with HPE’s existing Aruba networking portfolio creates a powerful, integrated offering. Smykay points out that networking is a critical but often overlooked component of AI implementations. “Data’s important, but when you use any kind of application on your computer or your phone, you’ve got to have a network to communicate with it,” he explains. This acquisition strengthens HPE’s ability to provide the robust networking backbone that is essential for modern AI applications.
A Consultative and Business-First Approach
HPE prioritizes a deep understanding of a client’s business objectives before any technology is recommended. This consultative methodology is a direct result of Smykay’s extensive experience across various industry verticals, where he learned that technology must accommodate business and regulatory constraints, not the other way around. This philosophy is embodied in HPE’s Private Cloud AI solution. This turnkey offering provides Nvidia’s GPU infrastructure, pre-configured software, and professional services, all deployed on the customer’s premises. This approach is particularly beneficial for organizations in sectors like healthcare, financial services, and government, which must adhere to strict data governance and compliance requirements that often cannot be met by public cloud AI services.
The urgency of this business-first approach has grown as customer attitudes toward AI have evolved. While in 2023 many organizations were still questioning the necessity of an AI strategy, they are now actively seeking guidance on implementation and governance. Smykay notes that customers are now asking critical questions about their data—its location, quality, accessibility, and the governance frameworks surrounding it. This shift in focus from theoretical discussions to practical implementation details indicates that businesses have moved beyond the experimental phase and are now committed to deploying production-ready AI solutions that can deliver tangible business value.
The Critical Role of Partnerships
Given its global reach, HPE faces significant challenges in scaling its AI implementation services to meet the diverse needs of its customers across all continents and industries. Smykay is candid about the necessity of a robust partner network, stating, “We just can’t execute without them.” The global shortage of qualified AI and data science professionals further complicates this issue, making strategic collaborations essential. One such key partner is Trace3, a Denver-based systems integrator with a dedicated AI practice that has been in operation for 13 years. This long-standing expertise provides credibility and a proven track record of successful delivery.
Collaborative Solutions for Complex Problems
The partnership between HPE and Trace3 exemplifies how collaboration can lead to comprehensive solutions that neither company could deliver alone. Trace3 offers consulting services, data science expertise, and implementation capabilities, while HPE provides the underlying technology infrastructure and enterprise-grade support. A notable example of their joint work is a project with a healthcare organization that uses computer vision to analyze 3D heart images. This project leverages HPE’s Private Cloud AI environment and embedded machine learning software to detect anomalies in real-time, showcasing the move from conceptual demonstrations to practical applications with measurable outcomes. This collaborative model allows for faster deployments and reduced project risk.
Navigating the AI Regulatory Landscape
The complex and evolving regulatory environment presents a major challenge for AI implementation. Businesses must navigate a web of EU regulations, state-level legislation in the U.S., and industry-specific compliance requirements. These obligations extend beyond technical considerations to include legal, ethical, and reputational risks. Smykay emphasizes the importance of involving legal teams from the very beginning of any AI project. “Now, more than ever, it’s important that legal’s involved from the start,” he advises. This proactive approach to compliance helps to avoid the costly process of retrofitting solutions to meet regulatory standards and reduces overall project risk.
Legal considerations also encompass intellectual property protection and the complexities of software licensing. Large language models, for example, operate under a variety of open-source licenses with different restrictions and obligations that organizations must understand to avoid future legal complications. Smykay recommends adopting flexible architectural approaches that can adapt to changing regulations without requiring a complete system overhaul. This includes avoiding vendor lock-in and ensuring that systems can pivot between different AI models as compliance requirements evolve. The recent changes in pricing models from major AI providers underscore the importance of this architectural flexibility, as being locked into a specific platform can lead to unexpected cost increases or limitations in capabilities.
Future of AI: Life Sciences and Autonomous Agents
Among the many applications of AI, Smykay is most excited about its potential to drive breakthroughs in life sciences research. He believes that specialized large language models designed for genomics and chemistry datasets will lead to significant advancements in healthcare within the next three to five years. These domain-specific models can incorporate scientific knowledge and constraints, leading to a higher degree of accuracy for research applications compared to general-purpose AI systems. The combination of advanced AI capabilities and deep healthcare expertise is creating opportunities for discoveries that could have once taken decades to achieve through traditional research methods.
The Rise of Agentic AI
Looking further into the future, Smykay anticipates the widespread adoption of agentic AI systems, where autonomous agents collaborate in open marketplaces to perform complex tasks without human intervention. Early examples of this vision include projects like Agntcy from the Linux Foundation and MIT’s NANDA. These agent marketplaces could enable AI systems to communicate and collaborate independently across organizational boundaries, handling routine tasks such as server maintenance, financial data updates, or supply chain coordination. HPE is actively developing strategies to support this new era of agentic AI through internal committees and protocol development, aiming to provide the necessary infrastructure and services for secure and scalable agent deployment in enterprise environments.