Storefront Next and the End of Expensive SFCC Ownership

For years, Salesforce Commerce Cloud has had a cost problem; and it was never just licensing.

The real cost lived in the ecosystem around it: custom templating languages, hard-to-find specialists, slow iteration cycles, and a persistent dependency on external partners for even modest changes. Teams didn’t just pay for SFCC, they paid to operate it.

Storefront Next changes that equation in a fundamental way.

By moving to a modern, standard frontend stack - React, composable architecture, familiar tooling - you eliminate one of the biggest hidden taxes in the system: specialization. No more hunting for niche ISML experts. No more translating between modern frontend thinking and legacy templating constraints. You can hire from the broader market, use proven patterns, and move at the speed your team is actually capable of.

That alone lowers total cost of ownership. But it’s just the starting point.

What really changes is how companies choose to operate.

Two Paths To Tread

Once the barrier of “expensive to change” disappears, companies will likely split into two operational camps.

The first group will optimize for steady state. They want the site to run cleanly, predictably, and with minimal intervention. Their goal is a near-zero backlog, a very small team, and a system that largely takes care of itself. Updates are incremental. Automation handles the routine. Human involvement is reserved for exceptions. 

For these teams, Storefront Next is about cost control. It allows them to finally run SFCC themselves as a stable, low-maintenance platform instead of an ongoing project.

The second group sees the opposite opportunity. If the cost of change drops, why not do more?

More experimentation. More landing pages. More personalization. More campaigns. More integration across systems. The same team, or an even smaller one, can now produce significantly more output because they are no longer constrained by platform friction.

This is where Storefront Next can be more about growth than cost.

And of course in practice, most companies should do both.

Stabilize, Then Expand

This is the pattern we recommend for dealing with the confusing world of AI. Don’t be carried away in the hurly burly of innovation. Go up the learning curve in stages. Iterate and learn over time. Keep moving forward but don’t go faster than your organization (probably still quite slow) can handle.

First, get to a solid steady state. Reduce the noise around support. Eliminate the backlog. Automate the obvious operational work. Bring the cost of “keeping the lights on” as close to zero as possible. This is a business process question as well as a development and marketing one. Do what you do better and quicker and more automatically. Stop doing things that humans require but machines don’t - about 50% of the meetings you do each day? 

Then take that recovered time and reinvest it.

Not into rebuilding what you already have or adding fancy so called AI-native tools - but into extracting more value from the stack you’re already paying for.

That means connecting the pieces of your stack that have historically operated in silos:

CMS feeding landing page generation
Personalization driving dynamic content decisions
Search influencing merchandising and discovery
Campaign tooling tied directly into experience delivery

Individually, these systems are powerful. Connected, they become multipliers. (You already knew all this but there wasn’t time or mental energy to actually achieve the achievable)

And once connected, all these newly exploited existing systems become candidates for automation. Without spending an extra penny on an AI enablement platform. 

Automation Before AI

There’s a misconception that AI transformation starts with buying an AI product.

It doesn’t. It mustn’t.

It starts with getting your stack into a state where it can actually support automation. Clean data flows. Defined workflows. Clear system boundaries. Reliable integrations.

If you automate your current workflows and your current workflows are designed around human frailties then you will just have automated mediocrity. What a waste. Get the most out of what you already have. Organize yourselves to be efficient, and let automation take care of as much of the resulting work burden as “humanly” possible.

Only then does AI have something to work with.

What happens next is not a switch you flip, it’s a gradual process. You automate pieces of the workflow. Then you introduce AI to optimize, generate, or orchestrate within those workflows. Over time, more of what was previously human-gated becomes system-driven.

But none of that works if your frontend is slow, rigid, or dependent on scarce skills.

This is where Storefront Next quietly becomes strategic.

Why SFCC Becomes More Valuable

SFCC has always been flexible. That was never the issue.

The issue was that most teams couldn’t fully use that flexibility. The cost of implementation, the friction of the frontend, and the dependency on specialized skills meant that much of the platform’s capability remained theoretical.

Storefront Next changes that.

When teams can actually build, iterate, and integrate without friction, SFCC’s flexibility turns into a real advantage. It becomes the center of a composable, connected, and increasingly automated commerce stack.

And importantly, it becomes a platform that can evolve.

Not through large, expensive replatforming efforts, but through continuous, incremental improvement.

That’s the shift.

Lower cost of ownership is the entry point. Operational leverage is the outcome. And long-term adaptability is the real payoff.

If you’re already on SFCC, the decision is straightforward. The question isn’t whether Storefront Next is worth it - it’s whether you want to keep paying for constraints that no longer need to exist.