Build for the Model You'll Use Next

Key Takeaways

Imagine replacing the engine in your car and being told you need a new dashboard, new controls, and driving lessons. It sounds ridiculous, but it’s surprisingly close to what can happen when a company changes AI models.

The best model for your business today may not be the best one six months from now. A faster model or less expensive model may arrive, or one that simply handles your workloads better. Switching should be relatively uneventful.

Instead, companies can find that the model has become tangled with applications, security controls, integrations, and employee workflows. What looked like a model upgrade becomes a full technology migration.

The problem isn’t that AI keeps changing. That’s a good thing. The problem is that many IT environments weren’t built to change with it.

The Real Cost of an Upgrade

The price of a new AI model is a lot more than just whatever a new model costs.

If applications were built directly around the old model, changing it can mean retesting applications, reviewing security controls, updating integrations, and retraining employees. Suddenly, getting a better model means touching almost everything around it.

That kind of complexity has a measurable cost. If technical debt already accounts for 21% to 40% of IT spending, AI can add another layer of dependency.¹ And organizations can underestimate how closely their data, models, and workflows become tied to vendor-specific APIs and platform tools, limiting technology agility and future negotiating power.²

Unlike an infrastructure refresh that might happen every few years, AI models are changing constantly. If every improvement triggers another round of changes, the cost and disruption repeat with it. That makes architecture a business issue, not just a technical one.

Build the Stack So the Engine Can Change

The answer is separation. Think of AI in three layers.

• At the base is the infrastructure: compute, storage, hosting, and connectivity.
• At the top are the applications and experiences people use every day.
• In the middle is the platform layer that manages how models connect to data, applications, security policies, and users.

The Middleware Layer Matters the Most, But Is Often Skipped

That middle layer allows the model underneath to change without changing the experience above it. Companies can use the model that makes sense for a workload today and replace it when something better comes along.

Without that separation, applications can become hardwired to a particular model, vendor, or technology, sending changes through everything connected to them. A platform absorbs much of that change so security policies, data access, and the employee experience can remain consistent even when the underlying technology changes.

That flexibility is becoming an architectural priority. Gartner recommends designing model-agnostic workflows with orchestration layers that allow organizations to switch between large language models (LLMs) across vendors and regions.³ Microsoft describes the same architectural principle: putting an abstraction between the applications and individual models allows the application to remain consistent while the underlying model changes based on cost, quality, or workload requirements.⁴

It’s the technology equivalent of changing the engine without redesigning the dashboard every time.

Make Model Changes Boring

Here’s how you know your environment is working for you, not against you.

A new model comes out. Your team evaluates it. It performs better for a particular workload, costs less, or gives you greater control. You decide to use it.

And nothing dramatic happens.

Employees keep working. Applications keep running. Security policies remain in place. The technology team isn’t kicking off a months-long migration.

The model changed. The business didn’t.

A Platform Gives You Options

There’s an important difference between subscribing to AI and building a platform for it.

A subscription gives you access to a model or service that meets your needs today. A platform gives you a stable environment where models and the technology underneath them can change without forcing the rest of the organizations to change too.

The faster the AI market moves, the more that flexibility matters. Trying to predict which model will be the best in six months from now is a losing exercise. Build an environment where you don’t have to.

When the Next Model Arrives, Expedient Will Be There

Expedient’s platform and services are built to support the infrastructure at the base, govern AI access above it, and provide the layer in between that keeps your organization’s experience consistent as the technology underneath evolves.

If changing your AI model means changing everything around it, that middle layer may be what’s missing.

Talk to Expedient about a service layer built to absorb the next one.

Let‘s Talk

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