Composable or monolithic: Why system architecture determines AI flexibility

Ten years ago, the question of system architecture was an IT decision. Today, it is a strategic one. Anyone choosing a monolithic system today is also deciding which AI models they will be able to use in three years – and which they will not. Most companies do not realize this when they make the decision.

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What the Difference Really Is

Monolithic systems are closed systems. Everything comes from a single source: data storage, logic, user interface, and integrations. This has advantages: less complexity during implementation, clear responsibilities, and a single point of contact. But it also has one decisive disadvantage: once you are locked into the system, you are dependent on it.

New requirements can only be implemented as quickly as the provider releases them. New AI models can only be integrated if the manufacturer supports them.

Composable architectures work differently. They are not a single piece of software, but a combination of specialized components that communicate through open interfaces. The data foundation is centralized. The applications around it can be replaced.

This means that when a better image scoring model enters the market, it can be integrated. When a new language model becomes more relevant for generating product descriptions, it can be connected. Without having to change the entire system.

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Why This Matters Now

AI models evolve faster than enterprise software cycles allow. What is state of the art today may be outdated in twelve months. That is not a problem, as long as the infrastructure is flexible enough to evolve with it.

Anyone choosing a closed system today is buying the capabilities available today and committing to them. Anyone choosing an open, composable architecture is buying the ability to integrate the best solutions available tomorrow.

This is not a technical argument. It is an economic one.

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What I Observe in Practice

I regularly speak with companies that made their architectural decision two or three years ago and are now dealing with the consequences.

Some invested in a monolithic system – for good reasons, at a time when the decision made sense. Today, they want to scale AI use cases and are encountering limitations that are not technical, but architectural.

Others chose openness, not because they already knew what AI would be capable of, but because flexibility was a guiding principle. These companies now integrate new models in weeks, not quarters.

The difference is not the technology. The difference is the decision that was made years ago.

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Not Everyone Needs the Same Thing

One point I emphasize repeatedly in conversations is that openness and composability are not equally relevant for every company.

A company operating nationally, with a manageable product catalog and only a few channels, does not need a highly composable Enterprise DAM. A solid PIM system is often sufficient and avoids unnecessary complexity.

A company operating internationally, synchronizing thousands of products across dozens of markets and channels, and aiming to use AI at scale, needs a different infrastructure. Here, composability is not a nice to have. It is a prerequisite.

The right architecture is the one that matches the actual complexity of the company. Not the one that promises the most.

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The Question You Should Ask Yourselves

Before making your next system decision, ask yourselves this question: How flexible will our system need to be in three years?

If the honest answer is that you will probably integrate new AI models, connect new channels, and continue developing your architecture, then that is the requirement you need to base your decision on today. Not the range of features shown in the demo.

Monolithic systems almost always win in a direct comparison because they are ready today. Composable architectures win in the long term because they will still be adaptable tomorrow.

That is the decision you are facing.

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Listen to the Conversation

In the Marketing-Börse podcast, I spoke about exactly this question – and about how AI is changing the DAM market, what open systems need to deliver in practice, and why data quality remains the critical prerequisite.

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👉 Listen to the Podcast Now

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