We often celebrate the grand achievements in artificial intelligence: the model trained on trillions of parameters, the groundbreaking algorithm that mimics human cognition, the proof-of-concept demonstration that wows stakeholders. These are undeniably significant engineering feats. But let’s be honest, how many of those impressive demos actually translate into tangible business value? How many move beyond the PowerPoint deck and directly impact the bottom line?
The truth is, the real magic, the true business transformation, doesn’t happen when an AI model is built. It happens when that model springs into action, when its predictions meet reality, when it successfully flags a malfunctioning machine, or personalizes a customer experience in real-time. This crucial step, this operational layer where all that sophisticated training finally earns its keep, is what we call AI inference.
Craig Partridge, senior director worldwide of Digital Next Advisory at HPE, puts it perfectly: “The true value of AI lies in inference.” He emphasizes the phrase, “trusted AI inferencing at scale and in production,” as the ultimate goal, and, frankly, where the biggest return on AI investments will be found. The journey there, however, is anything but simple.
Beyond the Hype: Why Inference is AI’s True North
If you’ve dipped your toes into the AI waters, you know the allure of experimentation. There’s a certain thrill in exploring what’s possible. Yet, as Christian Reichenbach, worldwide digital advisor at HPE, points out from recent survey findings, a significant chasm exists between playing with AI and actually operationalizing it. While a respectable 22% of organizations have moved beyond pilots to truly integrate AI—an improvement from the previous year—the vast majority remain stuck in the experimental phase. They’re building magnificent ships but aren’t quite ready to set sail.
This gap isn’t just a technical hurdle; it’s a strategic one. To cross it, to move from fascinating potential to undeniable impact, organizations need a robust, three-pronged approach: establishing trust as a core operating principle, embracing truly data-centric execution, and cultivating IT leadership capable of scaling AI successfully across the enterprise. Without these pillars, even the most brilliant AI models risk becoming expensive shelfware.
Building the Foundation: Trust and the Data-Centric Shift
Think about the journey from an idea to a fully operational, revenue-generating system. It’s rarely a straight line. With AI, two critical elements have emerged as non-negotiable for achieving that “at scale and in production” sweet spot: trust in the intelligence, and a fundamental shift in how we think about the data that feeds it.
The Imperative of Trust in AI
What does “trusted inference” truly mean? At its heart, it’s about confidence—the absolute certainty that the answers and actions derived from your AI systems are reliable, accurate, and consistent. This isn’t just a nice-to-have; it’s a bedrock requirement.
For applications like generating marketing copy or powering customer service chatbots, a hiccup might be annoying. But in higher-stakes scenarios, like an autonomous vehicle navigating city streets or a robot assisting during delicate surgery, trust isn’t just critical, it’s a matter of life and safety. This trust begins and ends with data quality. As Craig Partridge wisely says, “Bad data in equals bad inferencing out.” It’s a simple truth that’s often overlooked.
We’ve all seen the consequences when data quality falls short. Christian Reichenbach highlights the proliferation of unreliable, AI-generated content, often riddled with “hallucinations,” that clogs workflows and forces employees into time-consuming fact-checking exercises. When that happens, trust erodes, productivity gains vanish, and the promised benefits never materialize. Conversely, when trust is meticulously engineered into inference systems, the gains are immense. Imagine a network operations team, usually buried in troubleshooting. With a trusted inferencing engine, they gain a reliable “copilot”—a 24/7 team member providing faster, more accurate, custom-tailored recommendations that elevate their efficiency and decision-making.
From Models to Data: The AI Factory Paradigm
In the early days of AI, the focus was almost entirely on the models themselves. Companies scrambled to hire data scientists, and the race was on to build ever-more-sophisticated, trillion-parameter models. While impressive, this model-centric approach often overlooked the operational realities of deriving value.
Today, as organizations mature from pilots to measurable outcomes, the pendulum has swung. The focus has shifted decisively towards 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,” explains Reichenbach. This evolution is giving rise to what’s often called the “AI factory”—an always-on production line where data flows seamlessly through pipelines and feedback loops, generating continuous, actionable intelligence.
This move from model-centric to data-centric thinking brings new strategic questions to the forefront. Reichenbach distills them into two pivotal queries: “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?”
These questions are foundational, influencing everything from platform choices and operating models to team roles and trust and security protocols. To help organizations navigate this complex landscape, HPE has developed a practical four-quadrant AI factory implication matrix:
- Run: This involves accessing an external, pretrained model via an interface or API, where you own neither the model nor the underlying data. Implementation here demands stringent security and governance, alongside a strong center of excellence to guide AI usage decisions.
- RAG (Retrieval Augmented Generation): Here, external, pre-trained models are combined with your company’s proprietary data to create unique, tailored insights. The focus is on seamlessly connecting your internal data streams to inferencing capabilities, offering rapid access to full-stack AI platforms.
- Riches: This quadrant is about training custom models on your own enterprise data, unlocking unique differentiation and deep insights. This path requires scalable, energy-efficient environments, often leveraging high-performance computing systems.
- Regulate: Similar to Riches, this involves custom models, but trained on external data. It demands the same scalable setup as Riches, with the added, critical emphasis on legal and regulatory compliance for handling sensitive, non-owned data with extreme caution.
It’s important to remember these aren’t exclusive silos. Most organizations, even HPE itself, operate across multiple quadrants. As Partridge notes, HPE might build its own models (Riches) and then deploy that intelligence into its products for customers to “Run,” thereby extending the value chain.
IT’s Defining Moment: Scaling AI for Enterprise Impact
The “at scale” part of Partridge’s “trusted AI inferencing at scale and in production” catchphrase highlights a core challenge in enterprise AI. What works beautifully for a handful of boutique use cases often crumbles when you try to apply it across an entire organization. “There’s value in experimentation,” Partridge concedes, “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.”
This challenge—transforming isolated pilots into robust, enterprise-wide systems—is precisely where the IT function’s core competencies shine. And critically, it’s a leadership opportunity IT cannot afford to miss. IT departments are the masters of taking small-scale solutions and instilling the discipline, governance, and infrastructure required to run them reliably and efficiently at scale. They truly need to lean into this debate.
For IT teams that might be tempted to linger on the sidelines, history offers a stark warning. A decade ago, during the early wave of enterprise cloud adoption, many IT departments hesitated, allowing individual business units to independently deploy cloud services. The result? Fragmented systems, duplicated spending, and security vulnerabilities that took years—and significant resources—to untangle. We’re facing a similar dynamic with AI, as “shadow AI” proliferates with teams experimenting outside of IT’s structured oversight.
Instead of shutting down innovation, IT’s mandate is now to provide structure, guardrails, and governance. This means architecting a comprehensive data platform strategy that brings together enterprise data securely and accessibly to feed AI initiatives. It involves standardizing infrastructure, perhaps through private cloud AI platforms, protecting data integrity, and safeguarding brand trust—all while enabling the speed and flexibility that cutting-edge AI applications demand. These are the non-negotiable requirements for achieving that final, coveted milestone: AI that is truly in production, delivering real, measurable value.
Ultimately, as Reichenbach wisely concludes, success comes down to a clear understanding of where your organization plays in the AI landscape: “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.” The winners in this new era of AI won’t just be those who experiment, but those who bring clarity to all these quadrants, aligning their technological ambition with robust governance and the unwavering pursuit of value creation.
