Realizing value with AI inference at scale and in production

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Realizing value with AI inference at scale and in production

In partnership withHPE Training an AI model to predict equipment failures is an engineering achievement. But it’s not until prediction meets action—the moment that model successfully flags a malfunctioning machine—that true business transformation occurs. One technical milestone lives in a proof-of-concept deck; the other meaningfully contributes to the bottom line. Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, believes “the true value of AI lies in inference”. Inference is where AI earns its keep. It’s the operational layer that puts all that training to use in real-world workflows. Partridge elaborates, “The phrase we use for this is ‘trusted AI inferencing at scale and in production,'” he says. “That’s where we think the biggest return on AI investments will come from.”Getting to that point is difficult. Christian Reichenbach, worldwide digital advisor at HPE, points to findings from the company’s recent survey of 1,775 IT leaders: While nearly a quarter (22%) of organizations have now operationalized AI—up from 15% the previous year—the majority remain stuck in experimentation. Reaching the next stage requires a three-part approach: establishing trust as an operating principle, ensuring data-centric execution, and cultivating IT leadership capable of scaling AI successfully. Trust as a prerequisite for scalable, high-stakes AI Trusted inference means users can actually rely on the answers they’re getting from AI systems. This is important for applications like generating marketing copy and deploying customer service chatbots, but it’s absolutely critical for higher-stakes scenarios—say, a robot assisting during surgeries or an autonomous vehicle navigating crowded streets. Whatever the use case, establishing trust will require doubling down on data quality; first and foremost, inferencing outcomes must be built on reliable foundations. This reality informs one of Partridge’s go-to mantras: “Bad data in equals bad inferencing out.” Reichenbach cites a real-world example of what happens when data quality falls short—the rise of unreliable AI-generated content, including hallucinations, that clogs workflows and forces employees to spend significant time fact-checking. “When things go wrong, trust goes down, productivity gains are not reached, and the outcome we’re looking for is not achieved,” he says. On the other hand, when trust is properly engineered into inference systems, efficiency and productivity gains can increase. Take a network operations team tasked with troubleshooting configurations. With a trusted inferencing engine, that unit gains a reliable copilot that can deliver faster, more accurate, custom-tailored recommendations—”a 24/7 member of the team they didn’t have before,” says Partridge. The shift to data-centric thinking and rise of the AI factory In the first AI wave, companies rushed to hire data scientists and many viewed sophisticated, trillion-parameter models as the primary goal. But today, as organizations move to turn early pilots into real, measurable outcomes, the focus has shifted toward data engineering and architecture. “Over the past five years, what’s become more meaningful is breaking down data silos, accessing data streams, and quickly unlocking value,” says Reichenbach. It’s an evolution happening alongside the rise of the AI factory—the always-on production line where data moves through pipelines and feedback loops to generate continuous intelligence. This shift reflects an evolution from model-centric to data-centric thinking, and with it comes a new set of strategic considerations. “It comes down to two things: How much of the intelligence–the model itself–is truly yours? And how much of the input–the data–is uniquely yours, from your customers, operations, or market?” says Reichenbach. These two central questions inform everything from platform direction and operating models to engineering roles and trust and security considerations. To help clients map their answers—and translate them into actionable strategies—Partridge breaks down HPE’s four-quadrant AI factory implication matrix (see figure): Source: HPE, 2025 Run: Accessing an external, pretrained model via an interface or API; organizations don’t own the model or the data. Implementation requires strong security and governance. It also requires establishing a center of excellence that makes and communicates decisions about AI usage. RAG (retrieval augmented generation): Using external, pre-trained models combined with a company’s proprietary data to create unique insights. Implementation focuses on connecting data streams to inferencing capabilities that provide rapid, integrated access to full-stack AI platforms. Riches: Training custom models on data that resides in the enterprise for unique differentiation opportunities and insights. Implementation requires scalable, energy-efficient environments, and often high-performance systems. Regulate: Leveraging custom models trained on external data, requiring the same scalable setup as Riches, but with added focus on legal and regulatory compliance for handling sensitive, non-owned data with extreme caution. Importantly, these quadrants are not mutually exclusive. Partridge notes that most organizations—including HPE itself—operate across many of the quadrants. “We build our own models to help understand how networks operate,” he says. “We then deploy that intelligence into our products, so that our end customer gets the chance to deliver in what we call the ‘Run’ quadrant. So for them, it’s not their data; it’s not their model. They’re just adding that capability inside their organization.” IT’s moment to scale—and lead The second part of Partridge’s catchphrase about inferencing—”at scale”— speaks to a primary tension in enterprise AI: what works for a handful of use cases often breaks when applied across an entire organization. “There’s value in experimentation and kicking ideas around,” he says. “But if you want to really see the benefits of AI, it needs to be something that everybody can engage in and that solves for many different use cases.” In Partridge’s view, the challenge of turning boutique pilots into organization-wide systems is uniquely suited to the IT function’s core competencies—and it’s a leadership opportunity the function can’t afford to sit out. “IT takes things that are small-scale and implements the discipline required to run them at scale,” he says. “So, IT organizations really need to lean into this debate.” For IT teams content to linger on the sidelines, history offers a cautionary tale from the last major infrastructure shift: enterprise migration to the cloud. Many IT departments sat out decision-making during the early cloud adoption wave a decade ago, while business units independently deployed cloud services. This led to fragmented systems, redundant spending, and security gaps that took years to untangle. The same dynamic threatens to repeat with AI, as different teams experiment with tools and models outside IT’s purview. This phenomenon—sometimes called shadow AI—describes environments where pilots proliferate without oversight or governance. Partridge believes that most organizations are already operating in the “Run” quadrant in some capacity, as employees will use AI tools whether or not they’re officially authorized to. Rather than shut down experimentation, it is now IT’s mandate to bring structure to it. And enterprises must architect a data platform strategy that brings together enterprise data with guardrails, governance framework, and accessibility to feed AI. Also, it’s critical to keep standardizing infrastructure (such as private cloud AI platforms), protecting data integrity, and safeguarding brand trust, all while enabling the speed and flexibility that AI applications demand. These are the requirements for reaching the final milestone: AI that’s truly in production. For teams on the path to that goal, Reichenbach distills what success requires. “It comes down to knowing where you play: When to Run external models smarter, when to apply RAG to make them more informed, where to invest to unlock Riches from your own data and models, and when to Regulate what you don’t control,” says Reichenbach. “The winners will be those who bring clarity to all quadrants and align technology ambition with governance and value creation.” For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

In partnership withHPE

Training an AI model to predict equipment failures is an engineering achievement. But it’s not until prediction meets action—the moment that model successfully flags a malfunctioning machine—that true business transformation occurs. One technical milestone lives in a proof-of-concept deck; the other meaningfully contributes to the bottom line.

Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, believes “the true value of AI lies in inference”. Inference is where AI earns its keep. It’s the operational layer that puts all that training to use in real-world workflows. Partridge elaborates, “The phrase we use for this is ‘trusted AI inferencing at scale and in production,'” he says. “That’s where we think the biggest return on AI investments will come from.”

Getting to that point is difficult. Christian Reichenbach, worldwide digital advisor at HPE, points to findings from the company’s recent survey of 1,775 IT leaders: While nearly a quarter (22%) of organizations have now operationalized AI—up from 15% the previous year—the majority remain stuck in experimentation.

Reaching the next stage requires a three-part approach: establishing trust as an operating principle, ensuring data-centric execution, and cultivating IT leadership capable of scaling AI successfully.

Trust as a prerequisite for scalable, high-stakes AI

Trusted inference means users can actually rely on the answers they’re getting from AI systems. This is important for applications like generating marketing copy and deploying customer service chatbots, but it’s absolutely critical for higher-stakes scenarios—say, a robot assisting during surgeries or an autonomous vehicle navigating crowded streets.

Whatever the use case, establishing trust will require doubling down on data quality; first and foremost, inferencing outcomes must be built on reliable foundations. This reality informs one of Partridge’s go-to mantras: “Bad data in equals bad inferencing out.”

Reichenbach cites a real-world example of what happens when data quality falls short—the rise of unreliable AI-generated content, including hallucinations, that clogs workflows and forces employees to spend significant time fact-checking. “When things go wrong, trust goes down, productivity gains are not reached, and the outcome we’re looking for is not achieved,” he says.

On the other hand, when trust is properly engineered into inference systems, efficiency and productivity gains can increase. Take a network operations team tasked with troubleshooting configurations. With a trusted inferencing engine, that unit gains a reliable copilot that can deliver faster, more accurate, custom-tailored recommendations—”a 24/7 member of the team they didn’t have before,” says Partridge.

The shift to data-centric thinking and rise of the AI factory

In the first AI wave, companies rushed to hire data scientists and many viewed sophisticated, trillion-parameter models as the primary goal. But today, as organizations move to turn early pilots into real, measurable outcomes, the focus has shifted toward data engineering and architecture.

“Over the past five years, what’s become more meaningful is breaking down data silos, accessing data streams, and quickly unlocking value,” says Reichenbach. It’s an evolution happening alongside the rise of the AI factory—the always-on production line where data moves through pipelines and feedback loops to generate continuous intelligence.

This shift reflects an evolution from model-centric to data-centric thinking, and with it comes a new set of strategic considerations. “It comes down to two things: How much of the intelligence–the model itself–is truly yours? And how much of the input–the data–is uniquely yours, from your customers, operations, or market?” says Reichenbach.

These two central questions inform everything from platform direction and operating models to engineering roles and trust and security considerations. To help clients map their answers—and translate them into actionable strategies—Partridge breaks down HPE’s four-quadrant AI factory implication matrix (see figure):

Source: HPE, 2025

Run: Accessing an external, pretrained model via an interface or API; organizations don’t own the model or the data. Implementation requires strong security and governance. It also requires establishing a center of excellence that makes and communicates decisions about AI usage.
RAG (retrieval augmented generation): Using external, pre-trained models combined with a company’s proprietary data to create unique insights. Implementation focuses on connecting data streams to inferencing capabilities that provide rapid, integrated access to full-stack AI platforms.
Riches: Training custom models on data that resides in the enterprise for unique differentiation opportunities and insights. Implementation requires scalable, energy-efficient environments, and often high-performance systems.
Regulate: Leveraging custom models trained on external data, requiring the same scalable setup as Riches, but with added focus on legal and regulatory compliance for handling sensitive, non-owned data with extreme caution.

Importantly, these quadrants are not mutually exclusive. Partridge notes that most organizations—including HPE itself—operate across many of the quadrants. “We build our own models to help understand how networks operate,” he says. “We then deploy that intelligence into our products, so that our end customer gets the chance to deliver in what we call the ‘Run’ quadrant. So for them, it’s not their data; it’s not their model. They’re just adding that capability inside their organization.”

IT’s moment to scale—and lead

The second part of Partridge’s catchphrase about inferencing—”at scale”— speaks to a primary tension in enterprise AI: what works for a handful of use cases often breaks when applied across an entire organization.

“There’s value in experimentation and kicking ideas around,” he says. “But if you want to really see the benefits of AI, it needs to be something that everybody can engage in and that solves for many different use cases.”

In Partridge’s view, the challenge of turning boutique pilots into organization-wide systems is uniquely suited to the IT function’s core competencies—and it’s a leadership opportunity the function can’t afford to sit out. “IT takes things that are small-scale and implements the discipline required to run them at scale,” he says. “So, IT organizations really need to lean into this debate.”

For IT teams content to linger on the sidelines, history offers a cautionary tale from the last major infrastructure shift: enterprise migration to the cloud. Many IT departments sat out decision-making during the early cloud adoption wave a decade ago, while business units independently deployed cloud services. This led to fragmented systems, redundant spending, and security gaps that took years to untangle.

The same dynamic threatens to repeat with AI, as different teams experiment with tools and models outside IT’s purview. This phenomenon—sometimes called shadow AI—describes environments where pilots proliferate without oversight or governance. Partridge believes that most organizations are already operating in the “Run” quadrant in some capacity, as employees will use AI tools whether or not they’re officially authorized to.

Rather than shut down experimentation, it is now IT’s mandate to bring structure to it. And enterprises must architect a data platform strategy that brings together enterprise data with guardrails, governance framework, and accessibility to feed AI. Also, it’s critical to keep standardizing infrastructure (such as private cloud AI platforms), protecting data integrity, and safeguarding brand trust, all while enabling the speed and flexibility that AI applications demand. These are the requirements for reaching the final milestone: AI that’s truly in production.

For teams on the path to that goal, Reichenbach distills what success requires. “It comes down to knowing where you play: When to Run external models smarter, when to apply RAG to make them more informed, where to invest to unlock Riches from your own data and models, and when to Regulate what you don’t control,” says Reichenbach. “The winners will be those who bring clarity to all quadrants and align technology ambition with governance and value creation.”

For more, register to watch MIT Technology Review’s EmTech AI Salon, featuring HPE.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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“There are very beefy workloads that you must have that performance for to run the enterprise,” he said. “The Fortune 500 mainstream enterprise customers are now … adopting Epyc faster than anyone. We’ve seen a 3x adoption this year. And what that does is drives back to the on-prem enterprise adoption, so that the hybrid multi-cloud is end-to-end on Epyc.” One of the key focus areas for AMD’s Epyc strategy has been our ecosystem build out. It has almost 180 platforms, from racks to blades to towers to edge devices, and 3,000 solutions in the market on top of those platforms. One of the areas where AMD pushes into the enterprise is what it calls industry or vertical workloads. “These are the workloads that drive the end business. So in semiconductors, that’s telco, it’s the network, and the goal there is to accelerate those workloads and either driving more throughput or drive faster time to market or faster time to results. And we almost double our competition in terms of faster time to results,” said McNamara. And it’s paying off. McNamara noted that over 60% of the Fortune 100 are using AMD, and that’s growing quarterly. “We track that very, very closely,” he said. The other question is are they getting new customer acquisitions, customers with Epyc for the first time? “We’ve doubled that year on year.” AMD didn’t just brag, it laid out a road map for the next two years, and 2026 is going to be a very busy year. That will be the year that new CPUs, both client and server, built on the Zen 6 architecture begin to appear. On the server side, that means the Venice generation of Epyc server processors. Zen 6 processors will be built on 2 nanometer design generated by (you guessed

Building the Regional Edge: DartPoints CEO Scott Willis on High-Density AI Workloads in Non-Tier-One Markets

When DartPoints CEO Scott Willis took the stage on “the Distributed Edge” panel at the 2025 Data Center Frontier Trends Summit, his message resonated across a room full of developers, operators, and hyperscale strategists: the future of AI infrastructure will be built far beyond the nation’s tier-one metros. On the latest episode of the Data Center Frontier Show, Willis expands on that thesis, mapping out how DartPoints has positioned itself for a moment when digital infrastructure inevitably becomes more distributed, and why that moment has now arrived. DartPoints’ strategy centers on what Willis calls the “regional edge”—markets in the Midwest, Southeast, and South Central regions that sit outside traditional cloud hubs but are increasingly essential to the evolving AI economy. These are not tower-edge micro-nodes, nor hyperscale mega-campuses. Instead, they are regional data centers designed to serve enterprises with colocation, cloud, hybrid cloud, multi-tenant cloud, DRaaS, and backup workloads, while increasingly accommodating the AI-driven use cases shaping the next phase of digital infrastructure. As inference expands and latency-sensitive applications proliferate, Willis sees the industry’s momentum bending toward the very markets DartPoints has spent years cultivating. Interconnection as Foundation for Regional AI Growth A key part of the company’s differentiation is its interconnection strategy. Every DartPoints facility is built to operate as a deeply interconnected environment, drawing in all available carriers within a market and stitching sites together through a regional fiber fabric. Willis describes fiber as the “nervous system” of the modern data center, and for DartPoints that means creating an interconnection model robust enough to support a mix of enterprise cloud, multi-site disaster recovery, and emerging AI inference workloads. The company is already hosting latency-sensitive deployments in select facilities—particularly inference AI and specialized healthcare applications—and Willis expects such deployments to expand significantly as regional AI architectures become more widely

Key takeaways from Cisco Partner Summit

Brian Ortbals, senior vice president from World Wide Technology, which is one of Cisco’s biggest and most important partners stated: “Cisco engaged partners early in the process and took our feedback along the way. We believe now is the right time for these changes as it will enable us to capitalize on the changes in the market.” The reality is, the more successful its more-than-half-a-million partners are, the more successful Cisco will be. Platform approach is coming together When Jeetu Patel took the reigns as chief product officer, one of his goals was to make the Cisco portfolio a “force multiple.” Patel has stated repeatedly that, historically, Cisco acted more as a technology holding company with good products in networking, security, collaboration, data center and other areas. In this case, product breadth was not an advantage, as everything must be sold as “best of breed,” which is a tough ask of the salesforce and partner community. Since then, there have been many examples of the coming together of the portfolio to create products that leverage the breadth of the platform. The latest is the Unified Edge appliance, an all-in-one solution that brings together compute, networking, storage and security. Cisco has been aggressive with AI products in the data center, and Cisco Unified Edge compliments that work with a device designed to bring AI to edge locations. This is ideally suited for retail, manufacturing, healthcare, factories and other industries where it’s more cost effecting and performative to run AI where the data lives.

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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