Chad Smykay, AI CTO and Distinguished Technologist at Hewlett Packard Enterprise, is witnessing a transformation that is reshaping the landscape of enterprise technology adoption.

The rapid pace of change has left even experienced technologists astonished. While traditional enterprise software cycles used to measure progress in years, AI advancements are now emerging monthly. “Forget three years,” Chad asserts, dismissing conventional timelines. “In just the last three months, things are changing every month.”

This relentless acceleration in the AI sector has fundamentally altered how executives like Chad approach AI strategy. The cautious, committee-driven adoption processes that characterized earlier waves of technological uptake have been replaced by urgent implementation timelines, propelled by competitive pressures and customer expectations.

HPE, the US$28 billion tech giant that emerged from a 2015 split with HP Inc., has positioned itself at the forefront of this revolution. The days of focusing on printers and laptops are gone. Today, HPE is dedicated exclusively to enterprise infrastructure, cloud services, and the networking backbone that enables modern AI.

Chad offers a unique perspective in this role, shaped by a 25-year career in enterprise IT, including 12 years in the machine learning domain. His journey began during the big data era, when integrating disparate data sources required substantial technical expertise. Those early experiences with customers implementing fraud detection systems, now commonplace in banking, taught him valuable lessons about how businesses adopt new technologies.

“I worked on some of the early projects with customers when that was a very new concept,” he recalls, describing credit card fraud alerts that consumers now take for granted. The transition from revolutionary to routine illustrates the journey that AI is currently navigating across various industries.

A tale of two eras

The evolution from CPU-based big data processing to GPU-based AI, which Chad’s career has encompassed, signifies more than just a hardware upgrade. It represents a fundamental shift in how organizations process information and make decisions. Chad’s background at Rackspace, where he helped grow the company from 30 employees to over 5,000 during its eight-year journey to going public, provided him with insights into scaling a company, technology, and philosophy.

This experience now informs his approach at HPE, where the company serves clients across diverse industry verticals. From life sciences and healthcare to manufacturing, hospitality, retail, financial services, insurance, and energy, each sector has its own regulatory constraints and operational requirements.

The HPE GreenLake platform exemplifies this comprehensive approach. Rather than traditional infrastructure sales, HPE GreenLake operates as a cloud service model, allowing customers to consume AI-capable resources on demand. This shift alleviates capital expenditure concerns that often hinder AI initiatives while providing predictable operational costs.

The recent US$14 billion 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 needs of AI implementations.

“Networking often gets overlooked,” Chad explains. “Data is crucial, but when you use any application on your computer or phone, you need a network to communicate with it.”

Understanding business needs before technology selection

HPE’s approach prioritizes understanding business objectives before recommending specific technologies. This philosophy stems from Chad’s experience across various industry verticals, each with distinct regulatory environments and operational constraints that technology must accommodate rather than dictate.

HPE’s Private Cloud AI solution exemplifies this philosophy in action. Instead of forcing customers into public cloud environments that may not meet regulatory requirements, this turnkey solution includes Nvidia’s GPU infrastructure, pre-configured software like Nvidia AI enterprise and open-source stacks, along with professional services deployed on customer premises.

This approach is particularly valuable for healthcare organizations, financial services firms, and government agencies, where strict data governance is essential. Public cloud AI services, while convenient, often cannot meet the stringent compliance requirements that govern these sectors.

“Let’s come in and determine 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 urgency of this methodology has increased as customer attitudes have shifted dramatically. Organizations that spent 2023 questioning the need for 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 observed in his career. The tentative inquiries about AI feasibility have been replaced by urgent requests for implementation guidance and architectural recommendations.

“Customers used to ask: ‘Do I need to do this?’ But that conversation is no longer happening,” he reveals. This evolution has compressed typical enterprise technology adoption cycles from years to months, creating unprecedented demand for implementation expertise.

Organizations are now discussing advanced concepts like agentic AI, where autonomous agents make decisions and take actions independently. Some customers are skipping basic implementations like chatbots and knowledge bases altogether, focusing instead on more sophisticated applications that can deliver measurable business impacts immediately.

The shift toward serious AI implementation is evident in customers’ heightened focus on data quality and infrastructure requirements. Previously, organizations would discuss AI concepts without addressing underlying data challenges. Now, conversations quickly turn to data location, quality, accessibility, and governance frameworks.

“Here’s how I know they’re serious in 2025,” Chad says. “Last year, I could tell people weren’t genuinely serious about their AI use cases because they weren’t asking us questions about their data. Now they are.”

This change indicates that organizations have progressed 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 presents a series of scale challenges that no single organization can tackle alone. The company serves customers across all continents, industries, and use case categories. Consequently, it requires an extensive network of partners to maintain service quality while meeting growing demand.

“We simply can’t execute without them,” Chad states, referring to HPE’s numerous collaborators. A general shortage of qualified AI and data science professionals across the global economy exacerbates this challenge, with much talent concentrated in specific organizations rather than distributed throughout the broader market.

Trace3, a Denver-based systems integrator with over 90 technology partners, exemplifies the type of strategic relationship HPE needs to effectively serve enterprise customers. The company has built a dedicated AI practice over 13 years, predating the current AI boom and providing credibility with customers who recognize 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 emphasizes, highlighting the relationship’s foundation in proven delivery rather than marketing promises.

This partnership enables comprehensive customer solutions that neither organization 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 organization utilizing 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, marking the evolution from “art of the possible” demonstrations to practical implementations that deliver 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 must also address legal, ethical, and reputational risks.

“Now, more than ever, it’s crucial that legal is involved from the outset,” Chad explains, emphasizing 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 include intellectual property protection and licensing models. LLMs operate under various open-source licenses with different restrictions and obligations that organizations must understand to avoid future complications.

Chad recommends architectural approaches that remain flexible enough to adapt to changing regulations without necessitating 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. Organizations locked into specific platforms may face unexpected cost increases or capability limitations as the market continues to evolve rapidly.

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. Specialized LLMs designed specifically for genomics and chemistry datasets promise significant healthcare breakthroughs that could revolutionize medical research and treatment development.

“The project that excites me the most, which could change the world, is in the life sciences,” he says, predicting major advances within three to five years. These domain-specific models incorporate scientific knowledge and constraints that enhance accuracy for research applications beyond what general-purpose AI systems can achieve.

The computational demands of genomics research have historically limited the pace of breakthroughs, but modern GPU architectures and optimized algorithms are removing these barriers. The combination of AI capabilities and healthcare expertise creates opportunities for groundbreaking discoveries that might otherwise take decades to achieve through traditional research methods.

In the healthcare sector, the potential applications for AI extend beyond research to practical patient care scenarios, where AI can analyze medical imaging with unprecedented accuracy and speed. These implementations require robust infrastructure capable of handling large image datasets while adhering to strict data privacy and regulatory compliance standards.

HPE’s solutions empower 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 a reality.

These agent marketplaces could enable AI systems to communicate and collaborate independently across organizational boundaries. Agents might manage 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 efforts. The company aims to provide infrastructure and services that enable secure, scalable agent deployment across enterprise environments while maintaining the governance controls that organizations require.

The rapid pace of change in this area presents both opportunities and challenges for tech companies and their customers alike. Major developments occur every three months, contrasting with the traditional three-to-five-year technology adoption cycles that previously characterized enterprise markets.

Scale challenges persist despite technological advances

Despite growing market demand and technological maturity, scale remains HPE’s primary 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 expect.

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 and implementation challenges. This collaborative approach enables faster customer deployments while mitigating project risk through proven methodologies.

Chad’s vision for HPE centers on becoming the primary partner for AI enablement across cloud and networking solutions. The company’s role involves assisting organizations in navigating the complexities of AI adoption while maintaining a focus on business outcomes rather than merely technological novelty.

The transition from experimental AI projects to production implementations requires more than advanced hardware and software. It necessitates the type of enterprise-grade partnership approach that HPE and its ecosystem partners provide to organizations navigating this transformative period.

“Our goal isn’t to reinvent everything: we’re here to integrate what works, scale what’s necessary, and build only when the use case truly demands it. That’s how real AI impact occurs,” Chad concludes.