Are SaaS Solutions Becoming a Commodity?

The technological landscape has radically evolved over the past decade. Where software was once installed on internal servers, the Software-as-a-Service (SaaS) model has become the norm. Today, every business need seems to have a dedicated SaaS solution: email management, time optimization, administrative automation… But this omnipresence raises a crucial question: how far can this model go?

With the rise of artificial intelligence, SaaS is no longer the only vector of automation and efficiency. Large Language Models (LLMs) and advanced AI architectures now enable:

• Automating complex tasks without relying on a specific interface,
• Deploying autonomous agents capable of interacting with various systems,
• Building Retrieval-Augmented Generation (RAG) systems tailored to enterprise knowledge bases.

While SaaS has successfully established its economic model and accessibility, the question arises: will it eventually be absorbed by AI?

Evolution Towards Personalization and Autonomy

Traditionally, a SaaS solution offers a standardized interface, allowing users to perform tasks within a defined framework. AI, on the other hand, introduces a radically different flexibility: it can interact directly with data, execute instructions in natural language, and adapt to the specific needs of the user.

We are witnessing a convergence where:

• LLMs allow direct interaction with existing SaaS systems without requiring specific training,
• Autonomous agents reduce the need for siloed SaaS solutions by directly orchestrating actions across multiple tools,
• Companies can train and customize models for specific tasks, making the adoption of dedicated SaaS solutions unnecessary.

The implication is significant: over time, SaaS may lose its differentiating advantage and become a mere underlying infrastructure leveraged by more intelligent and autonomous AI systems.

Two Strategic Axes for the Future

How can SaaS and AI players position themselves in this transformation?

1. Specializing in Specific Verticals

Generalist models, as powerful as they may be, have a limitation: they lack deep domain expertise. The future of SaaS and AI will depend on strong specializations, integrating:

• Models trained for specific use cases (finance, logistics, industry, legal…)
• Tailored knowledge bases that allow AI to generate contextualized recommendations,
• Specialized frameworks and libraries to facilitate the adoption of custom solutions,
• Advanced prompt and model fine-tuning to produce relevant insights and analyses.

In other words, the value will no longer lie solely in access to a tool but in the ability to understand and structure information for precise applications.

2. Combining Consulting and Product

AI alone is not enough. A model, however powerful, will never generate relevant results if the input data is incorrect or poorly structured. The adage "Garbage in, garbage out" remains more relevant than ever.

This is where a hybrid approach combining technology and human expertise comes into play:

• Cleaning and structuring data to ensure a reliable foundation,
• Integrating and orchestrating AI with existing systems,
• Implementing continuous optimization models to refine results and adapt recommendations.

This hybrid model addresses the main weakness of traditional SaaS solutions: they are often standardized and rigid. The future belongs to solutions that combine technological power with strategic guidance.

Conclusion: Towards an Invisible SaaS?

While SaaS is not going to disappear overnight, its role is clearly evolving. The emergence of AI is changing the game: access to functionalities is becoming less important than the ability to structure and leverage information effectively.

Companies may no longer seek an "off-the-shelf" SaaS but rather a solution that dynamically adapts to their processes and data. In this context, the future of SaaS could be less visible, more modular, and entirely AI-driven.

So, is SaaS as we know it today becoming a commodity? The answer will depend on the ability of industry players to integrate and harness this new wave of technology.