Composable architecture x AI: Enabler for optimization & innovation

Composability promises to be the solution to many challenges in the e-commerce and digital industries. However, those aiming for a MACH architecture and wanting to be successful with the concept of composable commerce need more than a modular tech stack. In our series, experts shed light on the potential and often overlooked hurdles of composable commerce projects from a business, user, and technology perspective.

• Part 1: From buzzword to business strategy
• Part 2: Why composable commerce projects fail
• Part 3: Quality assurance for long-term composable commerce success

In our fourth edition, we show how a composable architecture can unlock AI potential along all e-commerce and corporate processes and simplify the implementation of intelligent tools.

Composable architecture: What does it mean?

From a specific examination of composable commerce, it becomes clear that its principles are part of a broader concept: composable architecture. In the context of a composable architecture, the principles of modularity and flexibility are applied not only to e-commerce systems but to the entire IT-landscape. This approach is becoming an increasingly important factor for the success of digital projects in dynamic markets. To illustrate the importance and added value of a composable architecture beyond the e-commerce context, the use of artificial intelligence (AI) serves as a good example.

Unlocking AI potential along all corporate processes

There is no doubt that artificial intelligence in e-commerce opens up new possibilities. Above all, it also offers potential for optimization, efficiency enhancement, and automation of almost all corporate processes, including:

• product management – for example, through GenAI-supported processing of customer feedback for product improvement)
• procurement – for example, through AI-driven demand forecasting and recommendations for supplier orders)
• IT and customer service – for example, through semi-automated responses to service inquiries
• quality assurance – for example, automated quality checks and predictive maintenance
• marketing and for customer experience – for example, personalized customer communication, chatbots, and customer segmentation

In short: The possibilities of AI in companies go far beyond simple use cases like the implementation of a GPTchat module.

The prerequisite for value-creating AI use cases

But why do only a few companies manage to achieve real added value through AI integrations? The sobering answer: To be able to use the potentials of AI on a large scale, particularly high demands on data quality and system accessibility must be met – and this proves to be a huge challenge, especially in historically grown legacy infrastructures, because:

• data in the system landscape is highly fragmented and sometimes not systematically retrievable.
• relationships between data are not traceable or unknown.
• systems are only accessible via dedicated, proprietary interfaces, whose implementation is time-consuming and costly.

Flexible architecture as an enabler for composable AI projects

Let’s assume you make a request to an AI chatbot. It takes a minute to respond because it needs to query data from ten different systems, aggregate it, and process it in relation to your inquiry. Would that be satisfactory for you? Probably, you would expect a faster response from an AI. Your request is processed much more efficiently if the required information is already integrated into an AI model. Additional necessary dynamic data should be able to be provided efficiently and with high relevance with as few queries as possible.

Precisely this prerequisite can be created by a composable architecture with its flexible, interlocking components, simplifying the integration of corresponding ai models. Through the abstraction and generic provision of interfaces and services and the integration of data from various systems into central data architectures, the foundations for a modular, adaptable system landscape can be created. This can in turn be the enabler to realize successful composable commerce and AI projects.

Understanding composable architecture & AI as a strategic endeavor

Modern integration solutions can support and accelerate the construction of a composable architecture – particularly in legacy environments. It is crucial to understand that despite all the technological components and their high relevance, this project is not a technology project, but rather a strategy project. Only when all the conditions are considered – the technological, organizational, and procedural – can a goal-oriented implementation be successful. Your company deserves a tailored solution. Let’s talk to advance your AI projects with a composable architecture effectively.

Your company deserves a tailored solution. Let’s talk to advance your AI projects with a composable architecture effectively.