The overlap of two revolutions – the AI revolution and the platform revolution

The digital world is characterized by disruptive developments that are constantly challenging existing business models. We have read headlines such as “Software eats the world” or “Browser eats software”. Now we are facing the next transformation: “AI eats SaaS” – artificial intelligence will increasingly replace or at least radically change traditional software-as-a-service models. But what does this mean in the context of the platform economy? And what new opportunities will open up for platforms and marketplaces if AI takes on the role of the backend?

To understand these changes, it is worth taking a look at the structure of classic SaaS solutions. A typical SaaS application consists of three levels. At the bottom is the database, where all the information is stored – user profiles, transaction data, product catalogs and so on. Above this is the backend, which defines which rules and workflows apply within the platform. This is where processes are defined that trigger actions in the front end, i.e. the user interface. For example, if a user clicks on the “Buy now” button, a process is triggered in the backend: The payment is processed, a confirmation email is sent and the order is noted in the system.

The days of backends are numbered

The backend is therefore the heart of every SaaS solution, but this is also where the problem lies. All new functions and processes have to be programmed in the backend, which becomes a bottleneck for innovation. Every new feature has to be prioritized, developed and tested. Development teams decide whether the effort is worth it – often based on user feedback and the expected acceptance of the feature. However, this also means that features that only affect a few users are often never developed, as the potential return on investment (ROI) is not sufficient. SaaS providers concentrate on the large, profitable functions, while individual, niche requirements are neglected.

Edge-use cases are made possible with

This is where the AI revolution comes into play. What if we could partially replace the backend with an AI? Instead of hard-coded workflows, an intelligent AI would respond to natural language input and access the database directly. Users could formulate their requirements in the front end in natural language or enrich the “prompt” with simple drop-down menus and checkboxes. An LLM could interpret this input and dynamically offer solutions without having to program new processes in the backend.

E-commerce AI use case enhances shopping experience

A vivid example of this is the Cologne-based start-up Homie (Yourhomie.ai), which specializes in the shopping experience in consulting-intensive sectors, such as DIY stores. DIY stores have huge inventories and countless use cases for this inventory. Customers aren’t just looking for a product – they’re looking for solutions for specific construction & remodeling projects. Someone may want to renovate their child’s room and not have a clear idea of what materials, tools and colors they need. Instead of scrolling through an endless list of products and setting filters, the customer can describe their use case to Homie’s chatbot: “I want to renovate my child’s room.” The AI then asks specifically for further details (filters): “How big is the room? What colors do you prefer? Should it have a certain theme?” At the end, the chatbot creates a complete shopping list with all the products required – like a recipe and the ingredients for the renovation.

Implementing features quickly with the help of AI

This dynamic, AI-based filter technology would have consumed enormous development resources using a classic SaaS model. Hundreds of specific workflows would have had to be programmed to cover all these use cases (construction & renovation projects). The ROI would have been uncertain, as it is not clear how many users would actually benefit from a specific workflow. The integration of an AI-supported chatbot eliminates this effort – the AI adapts flexibly to the individual needs of each user. This makes the backend partially obsolete.

This approach leads to the hyper-personalization of platforms and marketplaces. While traditional SaaS solutions are designed to satisfy as many users as possible with the same functions, AI-supported platforms can offer individual solutions for each user. The platform is no longer limited by predefined processes, but by the creativity of the users, who formulate their requirements in natural language. This also opens up new possibilities for niche features that were previously not feasible.

A DIY store, for example, would never have developed a digital solution specifically tailored to renovating a garden shed. The effort would have been too great and the potential market too small. With an AI-based platform, however, precisely this use case can be covered – without the need for additional programming work. AI also opens up the possibility of realizing long-tail use cases – i.e. niche use cases that would have few users and little ROI.

Platform & AI revolution

The overlap of the two revolutions – the platform revolution and the AI revolution – marks a turning point in the digital economy. Platforms are not only becoming places of exchange, but also dynamic problem solvers that adapt individually to each user. The role of the backend is changing fundamentally, and platform operators must learn to see AI as an integral part of their strategy.

In short: AI eats SaaS – and the platform economy is about to enter a new chapter.