"We're years into the generative AI boom, but much of what we see on X or LinkedIn is still mostly consumer-grade demoware. Sure, you can quickly spin up cool stuff with a ChatGPT prompt, but that's not where the real magic (or money) will happen. It's time to get serious about AI. For enterprises trying to put their data to work, the true revolution isn't model creation at all."
"It's inference: the application of those models to real, governed enterprise data to solve actual business problems. Inference will determine who wins in enterprise AI. Put bluntly: You don't have a model problem; you have a data-and-deployment problem. Not that you're alone in this. Follow the money and it's clear this is a systemic issue. In the past few months, OpenAI and others have inked massive contracts with cloud vendors, not to build new models, but rather to ensure capacity to run existing ones."
Enterprises must prioritize inference — applying models to governed business data — over building new models. Business context, data quality, and operational guardrails are essential to ensure reliable, useful outputs when models run daily. Major cloud contracts and large compute commitments signal that organizations are investing to guarantee predictable, industrial-scale capacity for running models. IDC forecasts that global spending on inference infrastructure will surpass training by the end of 2025, reflecting a shift to deployment plumbing. The core challenge is integrating enterprise data, managing costs at scale, and deploying models with governance to deliver real business value.
Read at InfoWorld
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