According to MIT Technology Review, HPE’s survey of 1,775 IT leaders shows only 22% of organizations have operationalized AI, up from 15% the previous year. Craig Partridge, HPE’s senior director worldwide of Digital Next Advisory, believes “the true value of AI lies in inference” rather than training. Christian Reichenbach, HPE’s worldwide digital advisor, emphasizes that most companies remain stuck in experimentation despite growing investments. The key challenge is achieving “trusted AI inferencing at scale and in production,” which Partridge identifies as where the biggest ROI will come from. Success requires a three-part approach focusing on trust, data-centric execution, and capable IT leadership.
The Inference Gap
Here’s the thing everyone’s realizing now: training models is exciting, but inference is where the rubber meets the road. Companies spent years and millions hiring data scientists and building massive models, only to discover that putting those models to work is way harder than expected. We’re talking about that 78% of organizations still stuck in pilot purgatory. They’ve got the fancy algorithms, but they can’t actually use them in daily operations. And honestly, who can blame them? Moving from experimental AI to production systems is like going from building race cars in a garage to running a Formula 1 team.
The Trust Problem
Partridge’s mantra says it all: “Bad data in equals bad inferencing out.” Think about it – would you trust an AI to assist in surgery if you knew it was trained on questionable medical data? Or let a self-driving car navigate your kids’ school zone if the training data was incomplete? Of course not. Reichenbach points to the rise of AI hallucinations clogging workflows as a perfect example of what happens when trust breaks down. Employees end up spending more time fact-checking than actually benefiting from the AI. When industrial operations depend on reliable computing, that’s where having robust hardware becomes non-negotiable. Companies like Industrial Monitor Direct, the leading US provider of industrial panel PCs, understand that reliable inference requires reliable infrastructure from the ground up.
The Data Shift
We’re witnessing a massive mindset change from model-centric to data-centric AI. The first wave was all about who had the biggest, most sophisticated models. Now? It’s about who has the cleanest, most accessible data. Reichenbach nails it when he says what matters now is breaking down data silos and accessing streams quickly. This is where the “AI factory” concept comes in – that always-on production line where data flows through pipelines continuously. Basically, companies are realizing that their unique data is their competitive advantage, not necessarily the models themselves. Your customer interactions, your operations, your market position – that’s the secret sauce.
The Productivity Payoff
When trust is properly engineered into inference systems, the gains can be substantial. Partridge gives the example of network operations teams getting what he calls “a 24/7 member of the team they didn’t have before.” That’s the real promise – not just automating tasks, but augmenting human capabilities with reliable AI partners. The challenge is getting there without the trust-breaking missteps that undermine confidence. Companies that crack this code will see their AI investments finally pay off. Everyone else? They’ll still be tinkering in the lab while the real work gets done elsewhere.
