HPE’s AI Vision: Chad Smykay On Partnerships And Scale
Chad Smykay, AI CTO and Distinguished Technologist at Hewlett Packard Enterprise, is witnessing a transformation that’s rewriting the rules of enterprise technology adoption.
The pace of change has left even seasoned technologists breathless. Where traditional enterprise software cycles once measured progress in years, AI developments now arrive monthly. “Screw three years,” Chad says, dismissing conventional timelines. “The last three months, it’s changing every month.”
The constant acceleration in the AI sector right now has fundamentally changed how executives like Chad are approaching AI strategy. The cautious, committee-driven adoption processes that characterised previous waves of technological uptake have given way to urgent implementation timelines, driven by competitive pressure and customer expectations.
HPE, the US$28bn tech giant that emerged from a 2015 split with HP Inc, has positioned itself right at the centre of this revolution. Gone are the days of printers and laptops. Today, HPE focuses exclusively on enterprise infrastructure, cloud services and the networking backbone that makes modern AI possible.
Chad brings a unique perspective to this role, shaped by a 25-year career in enterprise IT, with 12 of those years in the machine learning space. His journey began during the big data era, when combining disparate data sources required significant technical expertise. Those early experiences with customers implementing the kinds of fraud detection systems that are now ubiquitous in banking taught him some valuable lessons about how businesses take to new technologies.
“I worked on some of the early projects with customers when that was a very new thing,” he recalls, describing credit card fraud alerts that consumers now take for granted. The transition from revolutionary to routine represents the arc that AI is currently traversing across multiple industries.
A tale of two eras
The evolution from CPU-based big data processing to GPU-based AI, which Chad’s career has straddled, represents more than just a simple hardware upgrade. It reflects a fundamental shift in how organisations process information and make decisions. Chad’s background at Rackspace, where he helped build the company from 30 employees to more than 5,000 during its eight-year journey to going public, gave him an understanding of what it means to scale a company, a technology and a philosophy.
That experience now informs his approach at HPE, where the company serves customers across every conceivable industry vertical. From life sciences and healthcare to manufacturing, hospitality, retail, financial services, insurance and energy, each sector has its own unique regulatory constraints and operational requirements.
The HPE GreenLake platform speaks to this comprehensive approach that the firm has to have. Rather than traditional infrastructure sales, HPE GreenLake operates as a cloud service model, one which allows customers to consume AI-capable resources on demand. This shift addresses the capital expenditure concerns that often stall AI initiatives while providing predictable operational costs.
The recent US$14bn acquisition of Juniper Networks underscores HPE's commitment to AI-enabled infrastructure. Juniper's AI-driven network operations capabilities, combined with HPE's existing Aruba networking portfolio, create an integrated offering that addresses the often-overlooked networking requirements of AI implementations.
“Networking gets left out,” Chad explains. “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.”
Understanding business needs before technology selection
HPE's approach prioritises understanding business objectives before recommending specific technologies. This approach can be traced back to Chad's experience across a few different industry verticals, each with distinct regulatory environments and operational constraints that technology has to accommodate, rather than dictate.
HPE’s Private Cloud AI solution sees this philosophy in action. Rather than forcing customers into public cloud environments that may not meet regulatory requirements, this turnkey solution includes Nvidia’s GPU infrastructure, pre-configured software, such as Nvidia AI enterprise and open-source stacks and professional services deployed on customer premises.
It’s an approach that proves particularly valuable for healthcare organisations, financial services firms and government agencies, where maintaining strict data governance is a must. Public cloud AI services, while convenient, often can’t satisfy stringent compliance requirements that govern these sectors.
“Let’s come in and figure out what you are trying to solve at a business level first,” Chad explains. This consultative approach helps avoid the common pitfall of implementing impressive technology that fails to address real business problems.
The methodology has gained urgency as customer attitudes have shifted dramatically. Organisations that spent 2023 questioning whether they needed AI strategies are now focused on implementation details and governance frameworks.
An ever-changing conversation
The transformation in customer discussions represents perhaps the most significant shift Chad has seen in his career. The tentative inquiries about AI feasibility have been replaced by urgent requests for implementation guidance and architectural recommendations.
“Customers used to say: ‘Do I need to do it?’ But we’re no longer having that conversation,” he reveals. This evolution has compressed typical enterprise technology adoption cycles from years to months, creating unprecedented demand for implementation expertise.
Organisations are now discussing advanced concepts like agentic AI, where autonomous agents make decisions and take actions independently. Some customers are bypassing basic implementations like chatbots and knowledge bases altogether, as they look to focus on more sophisticated applications that can deliver measurable business impacts right away.
The shift toward serious AI implementation is evidenced by customers' increased focus on data quality and infrastructure requirements. Previously, organisations would discuss AI concepts without addressing underlying data challenges. Now, conversations immediately turn to data location, quality, accessibility and governance frameworks.
“Here’s how I know they’re serious in 2025,” Chad says. “Last year, I knew people weren’t really serious about their AI use cases, because they weren't actually asking us questions about their data. Now they are.”
This change indicates that organisations have moved beyond experimental phases toward production-ready implementations that require robust data foundations and enterprise-grade infrastructure.
The partnership imperative at scale
HPE's global reach creates a series of scale challenges that no single organisation can address independently. The company serves customers across all continents, industries and use case categories. As such, it needs an extensive network of partners that can help it to maintain service quality while it meets growing demand.
"We just can't execute without them," Chad says, regarding HPE’s many collaborators. A general shortage of qualified AI and data science professionals across the global economy only compounds this challenge, with much talent concentrated in specific organisations rather than distributed across the broader market.
Trace3, a Denver-based systems integrator with more than 90 technology partners, represents the type of strategic relationship HPE requires to serve enterprise customers effectively. The company has built a dedicated AI practice over 13 years, predating the current AI boom and providing credibility with customers who recognise the difference between established expertise and opportunistic positioning.
Josh Lindstrom, the Senior Director of Data & Analytics at Trace3, explains how the partnership addresses real customer needs. "We've been doing this for a long time, and in fact, we've been doing it a long time with HP," he says, emphasising the relationship's foundation in proven delivery rather than marketing promises.
The partnership enables comprehensive customer solutions that neither organisation could deliver independently. Trace3 provides consulting services, data science expertise, and implementation capabilities while HPE contributes technology infrastructure and enterprise-grade support.
One notable collaboration involves a healthcare organisation using computer vision for 3D heart imaging analysis. The project leverages HPE's Private Cloud AI environment and embedded machine learning software to detect anomalies in real-time medical imaging, representing the evolution from "art of the possible" demonstrations to practical implementations delivering measurable outcomes.
Navigating regulatory complexity in AI implementation
The regulatory landscape presents significant challenges that require careful navigation across multiple jurisdictions and industries. EU regulations, state-level legislation and industry-specific compliance requirements create a complex web of obligations for AI implementers that extends far beyond technical considerations. Modern businesses have to encompass legal, ethical and reputational risks too.
“Now, more than ever, it’s important that legal’s involved from the start,” Chad explains, emphasising the shift from treating compliance as an afterthought to integrating it into project planning. This proactive approach prevents costly retrofitting of compliance measures and reduces overall project risk.
Legal considerations extend beyond regulatory compliance to intellectual property protection and licensing models. LLMs operate under various open-source licences with different restrictions and obligations that organisations must understand to avoid future complications.
Chad recommends architecture approaches that remain flexible enough to adapt to changing regulations without requiring complete system rebuilds. This includes avoiding vendor lock-in scenarios and ensuring systems can pivot between different AI models as compliance requirements evolve.
The recent pricing model changes announced by major AI providers underscore the importance of maintaining architectural flexibility. Organisations locked into specific platforms may face unexpected cost increases or capability limitations as the market continues to evolve at breakneck speed.
Healthcare breakthroughs on the horizon
Among the various AI applications that Chad encounters across industry verticals, life sciences research generates the most excitement due to its potential for widespread societal impact. Specialised LLMs designed specifically for genomics and chemistry datasets promise significant healthcare breakthroughs that could revolutionise medical research and treatment development.
“The project that has me most excited that’s world-changing is in the life sciences,” he says, forecasting some major advances within three to five years. These domain-specific models incorporate scientific knowledge and constraints that improve accuracy for research applications beyond what general-purpose AI systems can achieve.
The computational requirements for genomics research have historically limited breakthrough pace, but modern GPU architectures and optimised algorithms are removing these constraints. The combination of AI capabilities and healthcare expertise creates opportunities for breakthrough discoveries that might otherwise take decades to achieve through traditional research methods.
In the healthcare sector, the potential applications for AI go beyond research and into practical patient care scenarios where AI can analyse medical imaging with unprecedented accuracy and speed. These implementations require robust infrastructure capable of handling large image datasets while maintaining strict data privacy and regulatory compliance standards.
HPE’s solutions enable researchers to process vast datasets while maintaining HIPAA compliance and other healthcare regulations. The company's high-performance computing capabilities and secure cloud services provide the computational foundation for advanced applications that could accelerate drug discovery timelines and reduce healthcare costs.
The future of autonomous AI agents
Looking ahead, Chad anticipates widespread adoption of agentic AI systems where autonomous agents collaborate through open marketplaces to accomplish complex tasks without human intervention. Projects like Agntcy from the Linux Foundation and MIT's NANDA represent early examples of this vision becoming reality.
These agent marketplaces could enable AI systems to communicate and collaborate independently across organisational boundaries. Agents might handle routine tasks like server maintenance, financial data updates or supply chain coordination without requiring human oversight for standard operations.
HPE is developing strategies to support the agentic AI era through various internal committees and protocol development work. The company aims to provide infrastructure and services that enable secure, scalable agent deployment across enterprise environments whilst maintaining governance controls that organisations require.
The rapid pace of change on this front is both an opportunity and a challenge for tech companies and their customers alike. Major developments occur every three months rather than the traditional three-to-five-year technology adoption cycles that previously characterised enterprise markets.
Scale challenges persist despite technological advances
Despite growing market demand and technological maturity, scale remains HPE's biggest focus in AI implementation across its global customer base. The company requires extensive partner networks to meet customer demand across multiple industries, geographic regions and use case categories while maintaining the technical expertise standards that enterprise customers require.
The scale challenge extends beyond human resources to encompass the complexity of modern AI implementations. Today's AI projects require integration across multiple systems, careful data preparation, ongoing model maintenance and continuous monitoring for performance degradation or bias issues.
Partners like Trace3 provide essential capabilities that complement HPE's technology offerings, creating comprehensive solutions that address both infrastructure, solutions and implementation challenges. This collaborative approach enables faster customer deployments while reducing project risk through proven methodologies.
Chad’s vision for HPE centres on becoming the primary partner for AI enablement across cloud and networking solutions. The company's role involves helping organisations navigate the complexity of AI adoption while maintaining focus on business outcomes rather than technological novelty.
The transformation from experimental AI projects to production implementations requires more than advanced hardware and software. It demands the type of enterprise-grade partnership approach that HPE and its ecosystem partners provide to organisations navigating this revolutionary period.
“Our goal isn’t to reinvent everything: we’re here to bring together what works, scale what’s needed, and build only when the use case truly demands it. That’s how real AI impact happens,” Chad concludes.