In this episode of The Hitchhiker’s Guide to IT, we explore one of the most important shifts happening in enterprise AI today: the move from model-centric thinking to inference-driven systems.

For years, the focus has been on building bigger, more powerful models. But as organizations move from experimentation to production, the real challenge is no longer just model performance — it’s how those models are deployed, orchestrated, and scaled in real-world environments.

Rob May, CEO of NeuroMetric AI, joins the conversation to break down what it actually takes to operationalize AI at scale. He explains why many organizations struggle to move beyond pilots, pointing to challenges like cost, evaluation complexity, and the need to rethink workflows entirely for an AI-driven world.

The discussion dives into how inference workloads are reshaping infrastructure requirements, from increased demand on GPUs to the need for smarter orchestration across multiple models. Rob also highlights why choosing the “best” model is often the wrong question — and why success depends more on aligning the right models to the right tasks within a well-structured system.

Ultimately, this episode reinforces a critical takeaway: scaling AI isn’t just about technology — it’s about building the right foundation, from infrastructure and data to operational discipline.