Summary
- GDT’s new AI Factory Center of Excellence helps enterprises validate AI use cases before major infrastructure investments.
- Hands-on testing assesses workload requirements, technical feasibility, and business value.
- NVIDIA, Cisco, and NetApp technologies demonstrate how an integrated AI factory supports production workloads.
- A structured approach takes customers from use case identification to production planning.
- Right-sized infrastructure helps avoid unnecessary spending, costly mistakes, and deployment delays.
- GDT’s AI strategy workshop helps align stakeholders on priorities, risks, and next steps.
GDT’s AI Factory Center of Excellence
There’s a lot of pressure on organizations to move quickly with AI. Everyone wants to accelerate adoption and start seeing results. But one of the biggest questions I hear from customers is pretty straightforward: What exactly should we be doing with AI?
It’s a fair question, and one that needs to be answered before making major infrastructure investments.
That’s part of the thinking behind GDT’s new AI Factory Center of Excellence (COE) in Dallas. Launched on October 22, 2026, this environment offers customers the opportunity to explore AI use cases, test workloads, and see what a production AI environment looks like without making a significant investment just to get started. Rather than investing in infrastructure to find out whether a use case will work, our COE allows customers to test their ideas, validate requirements, and make more informed decisions about what to build.
What does an AI factory actually look like?
Think about a traditional factory. Raw materials come in, they’re processed, and a finished product comes out the other side.
An AI factory works much the same way. Your data is the raw material, and compute, networking, storage, and other infrastructure work together to process that data and produce AI outputs, including tokens.
The important thing to understand, though, is that an AI factory isn’t just a collection of GPUs. Yes, GPUs are essential, but they’re only one piece of the equation. Networking, storage, and compute all need to work together to support AI workloads.
Using technologies from NVIDIA, Cisco, and NetApp, we can show customers how the full stack works together. More importantly, we can:
- Demonstrate AI capabilities in an integrated environment
- Run proofs of concept and test different workload scenarios
- Help customers understand the infrastructure required for their use cases
- Validate assumptions before customers make major investments
In the past, we could explain what these environments were capable of and help customers build them. Now, we can demonstrate what that journey will look like.
Understanding the value before making the investment
One of the first things we look at with any customer is the use case. This means asking questions like: What are you trying to accomplish? Is it technically feasible? And most importantly, is it going to deliver meaningful business value?
It’s important to ask these questions because a great idea for AI doesn’t necessarily mean that it will be a great investment. We typically look for use cases that can do at least one of three things:
- Save money by improving efficiency or reducing costs
- Make money by creating new revenue opportunities
- Reduce risk by addressing business or operational challenges
Validating these ideas used to require spinning up cloud infrastructure or bringing equipment on-premises to run a proof of concept. Our COE gives customers another option: We can run proofs of concept, test scenarios, and help development teams understand what it takes to build AI workloads before committing to an entire AI factory. It’s a more practical way to determine what’s possible and what it’s going to take to get there.
Taking AI from an idea to a production plan
When we start working with a customer, we don’t begin by talking about how many GPUs they need or what type of storage they should buy. Instead, we start with an AI strategy workshop. There’s so much pressure to be “doing AI” that organizations often feel they need to start investing before they’ve identified what they actually want to accomplish. Our approach helps customers work through four key stages:
- Identify the use cases: Bring business, IT, and development stakeholders together to identify opportunities that deliver meaningful business value.
- Evaluate AI foundations: Assess data readiness, technical capabilities, and whether the organization has the development resources to support its use cases.
- Test and validate in the COE: Run proofs of concept, explore different scenarios, and evaluate workload and infrastructure requirements.
- Plan for production: Determine the right architecture and deployment approach, whether on-premises, in the cloud, or in a neocloud environment.
By the time we’re ready to discuss infrastructure investments, customers have a much clearer picture of what they actually need, including validated use cases, defined requirements, architecture recommendations, and a clear path forward. The result is greater confidence in investment decisions and less risk of purchasing infrastructure that doesn’t meet the organization’s needs.
Sometimes you have to go slow to go fast
One of the things we say at GDT is that sometimes you have to go slow to go fast. I know that sounds counterintuitive when everyone is pushing to accelerate AI adoption. But taking the time to understand your use cases and requirements upfront can save a tremendous amount of time later.
We see organizations that want to start with factory sizing. How many GPUs do we need? How much storage? How many switches? These are certainly important questions, but if we’re asking them at the beginning, we’re having that conversation too early.
AI workloads are also much harder to size than traditional data center workloads. Model training, inference, and computer vision all have different requirements, and buying the biggest, most powerful GPUs doesn’t necessarily mean you’re buying the right infrastructure.
We’ve seen customers become so focused on securing GPUs that they overlook the networking and storage needed to support them. Without the right components, that expensive GPU can quickly become an expensive paperweight. And with some AI infrastructure components taking months to arrive, purchasing the wrong equipment can mean more than wasted money. It can set your entire AI initiative back while you wait for the right technology.
Instead, we first need to understand the use cases, analyze the data requirements, and determine what usage will look like. That’s what should drive the architecture. But this analysis can’t happen in an IT silo. Business leaders, development teams, and IT stakeholders all need to work together to understand what success looks like. Our COE gives those teams an opportunity to test workloads, validate requirements, and make more informed decisions before moving into production. The goal isn’t to slow down AI adoption. It’s to avoid the false starts and costly mistakes that can slow you down even more.
Start with the what, then figure out the how
If there’s one thing I’d encourage organizations to think about before investing in AI infrastructure, it’s this: Identify what you want to accomplish before deciding how you’re going to accomplish it. That’s what our AI Factory Center of Excellence is designed to help customers do.
But you don’t need to have your entire AI strategy figured out before engaging with us. Our AI strategy workshop is a good place to start. We’ll bring the right stakeholders together, identify use cases that matter to your business, and work through the requirements and potential risks. From there, we can use the COE to test those ideas, validate infrastructure needs, and develop a practical plan for production.
Because ultimately, the success of your AI initiative isn’t going to be measured by how many GPUs you purchased. It’s going to be measured by the value you deliver to your business.
Ready to take the next step with AI? Connect with GDT to schedule an AI strategy workshop and start building a plan around your business priorities.
