Why Scaling AI Feels So Hard

Pilots are easy because they’re forgiving. Latency doesn’t matter, costs are hidden inside a small innovation budget, and no one expects perfect governance. But in production those issues can’t be deferred.

The Economics of Reasoning

The most significant shift happening now is that we have reached the age of reasoning. We’ve entered what NVIDIA calls test-time scaling. In plain terms, it means one user query no longer equals one answer. Instead, it triggers a long chain of reasoning: multiple models, retrieval tools, a “judge” model to validate the output, then a final summary. The power is clear, but so is the impact on cost as token usage can jump by 100x or more.

This creates two immediate challenges. First, token costs become unpredictable, straining business cases. Second, power and space limits in data centers start to matter, as each additional billion tokens consumes real energy and compute. In fact, token economics is one of the biggest problem statements that enterprises are facing today in scaling AI.

The fix isn’t to scale back ambition. It’s to make inference efficient and predictable by providing the enterprise support and frameworks needed for production workloads, while supporting end-to-end generative AI development.

This shifts the economics from runaway costs to metrics executives can govern: throughput per dollar and tokens per watt. It turns AI from an open-ended cost center into something you can measure and manage.

What Scaling Really Requires

When organizations think about scaling, just looking at what models can do will not be enough. When you strip away the hype, scaling AI comes down to whether you can put the right operating model around it. We’ve learned that there are three layers you can’t ignore.

First: get the data right.

This data layer is the “first mile.” Every AI model needs enterprise context – your workflows, applications, and history – to create differentiated value and deliver meaningful results. Without that foundation, models remain generic. GPU-accelerated frameworks such as RAPIDS are critical here, enabling data preparation and feature engineering at speeds that keep up with inference demands. Just as important is governance. If you don’t keep your data under enterprise control – on-prem or in a governed cloud – you put both security and competitive advantage at risk.

Second: avoid single model thinking.

We call this the poly-AI layer. Real processes need a combination of models – predictive to forecast, document AI to extract, agentic AI to reconcile and act – and no single model can do all of this is. Enterprises need the flexibility to move between providers – hyperscalers, open-weights, NVIDIA’s optimized Inference Microservices (NIMs) – without tearing up the architecture each time.

Poly-AI also means surfacing AI as a first-class citizen in any solution. That includes the ability to switch providers, validate that entire projects still work, and maintain a robust spin-up environment that tracks usage, model choices, and costs. That’s why we built our Neural Connect capability: to make AI configurable, testable, and auditable, not buried as a prompt in code.

Third: embed intelligence into workflows.

Dashboards are fine, but value only shows up when insights flow into business actions: approvals, transactions, case resolution – with humans in or on the loop as needed. This is the “last mile” or the process and experience layer, where people actually feel the difference with end-to-end pipelines, workflows, and applications that people can consume.

Together, these layers – first mile, poly-AI, and last mile – are what we’ve unified in EdgeVerve AI Next. It’s designed so enterprises don’t have to reinvent the wheel for every use case, whether it’s KYC, order-to-cash, claims processing, or IT operations.

Lessons From the Field

It’s clear that AI value comes not from the model itself, but from how you manage data, deployment choices, and economics together. And we are seeing this proven again and again in the field.

Take investment management for example. Onboarding new financial products involves reviewing long prospectuses to extract compliance rules- figuring out who could invest, under what restrictions, in which jurisdictions, and through what payment mechanisms. Traditionally this process took weeks. By running a privately hosted Llama instance through NIM in a secure private cloud, we cut that cycle down to days. The sensitive documents never left the enterprise perimeter, satisfying regulators and protecting IP. The real takeaway wasn’t just speed. It was speed with sovereignty.

In another instance, a global logistics firm faced nearly 400,000 booking requests a day. Early AI prototypes worked but at a staggering cost: 3.6 billion input tokens and 350 million output tokens daily, or close to $5 million annually. By combining GPU clusters during business peaks with hyperscaler models off-peak, and by tracking consumption through FinOps dashboards, we were able to cut the projected costs by more than half – to under $2.5 million. The lesson was simple: match the workload to the right engine and monitor token economics like you would any other unit cost.

Building the Operating Model to Craft the Next with AI

Scaling AI requires more than technical excellence. It requires an operating model that enterprises can trust. So, what does it mean to run AI with the same discipline as ERP or cloud?

This is where optimized stacks and enterprise platforms converge. NVIDIA provides the inference substrate – GPUs, networking, frameworks, NIMs – engineered for throughput and efficiency. EdgeVerve AI Next brings the enterprise layers – data, workflows, governance, FinOps – so organizations can manage AI as infrastructure, not as a one-off experiment.