From misconceptions to momentum: Building private AI from the ground up

AI burst onto the scene full of promise — and it’s delivering. It uncovers patterns buried in mountains of data, accelerates decision-making and even makes human interactions more human. It’s reshaping industries from healthcare and finance to manufacturing and public sector organizations. No one is immune to the excitement, and everyone wants in.

But wanting to be part of the AI revolution and being AI-ready are two very different things.

Yes, you can use AI that isn’t yours — public models, public endpoints and shared infrastructure — and in many use cases, that’s fine. But the moment your data becomes sensitive, regulated, proprietary or core to your competitive edge, everything changes.

Real transformation happens when AI runs where your data lives — when it’s inside your environment, under your governance, with infrastructure designed to your needs and with full control of your data. Private AI is key to this, but it isn’t plug-and-play. It needs thoughtful planning and an architectural rethink across your whole technology stack, including compute, storage, networking and security.

4 big misconceptions about private AI (and what’s true)

The funny thing about private AI is that what tends to derail the deployment isn’t the technology, which works. It’s the assumption that you can just plug in some GPUs, point your data at a model and expect everything to run smoothly. Private AI demands more than that.

This brings us to what the misconceptions are that repeatedly trip up private AI before it ever reaches its potential.

Misconception 1: “Private AI is mostly about computing”

The most persistent assumption is that private AI begins and ends with computing — usually GPUs — and often in whatever density the budget allows. It’s understandable; GPUs drive the acceleration behind modern AI models. But computing is only one part of the ecosystem. Without the right mix of processors, memory architecture and supporting infrastructure, even the most advanced accelerators are underused. Private AI isn’t about piling on hardware — it’s about creating a balanced, heterogenous environment where computing, memory and workloads align.

Misconception 2: “If we have the data, the storage will take care of itself”

Traditionally, storage is simply a place where data is kept. AI doesn’t work like this. Its models are data-hungry, and if the storage layers can’t deliver fast, consistent input, it creates bottlenecks — leaving costly GPUs sitting idle. That’s why technologies like nonvolatile memory express storage systems and well-designed, scalable data lakes are essential. In AI, storage is an active part of the engine. Without the right architecture and tiering strategy beneath it, even the best-trained models will struggle to perform.

Misconception 3: “Our existing network will be fine; it already supports everything else”

Networks are easy to take for granted. If your network handles day-to-day applications, the assumption is it can handle AI. But AI workloads are different. They move continuously at high volumes and lightning-fast speeds, and even the slightest increase in latency can cause a bottleneck. For private AI to do what it’s meant to, it needs ultra-low latency and a high-throughput connection data superhighway that keeps GPUs and data pipelines fed in real time. This might mean rethinking your network fabric altogether, whether through modern high-performance Ethernet or specialized options such as InfiniBand. Before deploying AI, a thorough network assessment is essential to identify the weak spots that will starve GPUs and slow inference.

Misconception 4: “Private automatically means secure”

There’s a common belief that bringing AI in-house solves any security challenges by default. The thinking, “If the data stays inside my walls, it’s automatically safe,” is faulty. Private AI doesn’t automatically inherit security just because it runs on your infrastructure. In fact, because it introduces new patterns of data movement and new access points, it also introduces new risks that traditional controls weren’t designed for.

AI models need constant high-value data flowing through them and without the right guardrails in place, that creates unwanted exposure. Zero trust — where every user, device and workload is continuously authenticated and authorized — is your baseline, not an aspiration. Encryption is needed everywhere; at rest, in transit and increasingly “in use.”

Access needs to be granular and tightly governed. Even security features built directly into hardware are important, because breaches can originate in places as fundamental — and innocuous — as the chip.

• ALSO READ → Proactive risk management: The secret to securing your AI journey

NTT DATA: Delivering private AI the right way

At NTT DATA, we understand why these misconceptions exist. It’s not about bad decisions or lack of expertise; they’re logical assumptions — if you base your expectations on traditional AI thinking. But private AI is different — it changes the well-established rules.

This is where NTT DATA advisory becomes invaluable, guiding you through the intricacies of building a high-performing, secure and future-proof AI infrastructure. We look across your entire technology stack — from the computing that powers your models and the storage that keeps them fed to the networks that move your data at speed and the security that delivers resilience and regulatory compliance.

Private AI only works when all these layers are in place, and that’s where our expertise comes in. We help you design an environment that’s balanced, resilient and ready for the realities of modern AI, so your teams can focus on what the technology delivers, not what it demands behind the scenes.

• ALSO READ → Secure networking: Is your network ready to support AI workloads?

Build your private AI success with our expertise

Building a private AI platform isn’t an incremental upgrade; it's a fundamental change in how you think about your data center strategy. Without specialized knowledge across compute, storage, networking and security, you risk costly missteps, underperforming hardware, hidden bottlenecks, security and compliance gaps and architectures that simply can’t scale with the pace of AI.

That’s why strong advisory support is a must. It helps you avoid the traps, make the right decisions, strengthen security and compliance and build an environment that is future-ready and future-proof.

With the right strategic guidance, private AI becomes a strategic advantage. And that’s what NTT DATA’s data center advisory delivers. We have deep expertise and a clear understanding of both AI workloads and enterprise data center design — and the experience needed to build the infrastructure required to support the demands of modern AI.

The organizations that lead are the ones whose infrastructure strategies are tightly aligned with their business goals. A well-executed data center strategy isn’t just an IT decision; it’s a competitive advantage too.

Our approach helps unlock AI’s full potential — securely, efficiently and on your own terms. Are you ready to take the lead?