Chad Smykay, AI CTO and Distinguished Technologist at Hewlett Packard Enterprise, is leading a revolution in enterprise technology that is reshaping traditional adoption timelines.

While conventional software cycles once measured progress in years, AI advancements are now emerging monthly, significantly altering how executives formulate their AI strategies.

Chad’s career

With 25 years in enterprise IT and 12 years specifically in machine learning, Chad offers unique insights shaped by the transition from big data to AI.

His early work on fraud detection systems—now commonplace in banking—provided him with valuable lessons regarding technology adoption patterns. His experience at Rackspace, where he helped scale the company from 30 to over 5,000 employees during its eight-year journey to going public, offers critical insights into scaling technology and philosophy simultaneously.

HPE’s position

HPE, the US$28bn tech giant focused exclusively on enterprise infrastructure and cloud services, has positioned itself at the forefront of the AI revolution.

The company’s GreenLake platform functions as a cloud service model, enabling customers to access AI-capable resources on demand while addressing capital expenditure concerns that often hinder AI initiatives.

The recent US$14bn acquisition of Juniper Networks highlights HPE’s commitment to AI-enabled infrastructure.

When combined with HPE’s existing Aruba networking portfolio, this creates an integrated offering that addresses the often-overlooked networking needs of AI implementations.

“Networking gets left out,” Chad explains, underscoring the essential role networks play in supporting AI applications.

A business-first approach

HPE’s methodology emphasizes understanding business objectives before recommending technologies.

This consultative approach, informed by Chad’s experience across various industry verticals with distinct regulatory environments, helps avoid the implementation of impressive technology that fails to solve real business challenges.

The company’s Private Cloud AI solution exemplifies this philosophy.

Instead of pushing customers into public cloud environments that may not satisfy regulatory requirements, this turnkey solution includes Nvidia’s GPU infrastructure, pre-configured software stacks, and professional services deployed on customer premises—particularly beneficial for healthcare, financial services, and government agencies that require strict data governance.

An ever-changing market

The shift in customer discussions marks perhaps the most significant change in Chad’s career. Organizations that spent 2023 questioning the necessity of AI strategies are now concentrating on implementation details and governance frameworks. “Customers used to ask: ‘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.

Organizations are now discussing advanced concepts like agentic AI, with some skipping basic implementations like chatbots to focus on sophisticated applications that deliver immediate business impacts.

The importance of partnerships

HPE’s global reach across continents, industries, and use cases presents scale challenges that no single organization can tackle alone.

Strategic partnerships, such as the one with Trace3, a Denver-based systems integrator with 13 years of dedicated AI practice, enable comprehensive customer solutions that neither organization could provide independently.

These collaborations address the general shortage of qualified AI and data science professionals while offering proven delivery capabilities.

One notable partnership involves a healthcare organization utilizing computer vision for 3D heart imaging analysis, leveraging HPE’s Private Cloud AI environment to detect anomalies in real-time medical imaging.

Navigating regulations

The complex regulatory landscape necessitates careful navigation across multiple jurisdictions and industries.

EU regulations, state-level legislation, and industry-specific compliance requirements create obligations that extend beyond technical considerations to include legal, ethical, and reputational risks.

“Now, more than ever, it’s crucial that legal is involved from the start,” Chad says, advocating for proactive compliance integration rather than treating it as an afterthought. This includes maintaining architectural flexibility to adapt to changing regulations without necessitating complete system overhauls.

Looking to the future

Looking ahead, Chad envisions widespread adoption of agentic AI systems where autonomous agents collaborate through open marketplaces to achieve complex tasks.

These agent marketplaces could allow AI systems to communicate independently across organizational boundaries, managing routine tasks without human oversight.

Among various AI applications, life sciences research generates the most excitement due to its potential societal impact.

Specialized LLMs designed for genomics and chemistry datasets promise significant healthcare breakthroughs within three to five years, potentially transforming medical research and treatment development.

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