SaaS is dead, long live SaaS!

For two decades, the answer to every business need was acquiring a new SaaS subscription. Faced with a prospecting, project management, or invoicing issue, the central question was selecting the best provider on the market. This logic is faltering today in the face of a now-credible alternative: custom building facilitated by artificial intelligence.

Companies no longer just ask which tool to buy, but whether they should simply have the exact system they need generated. This paradigm shift does not mean the disappearance of software as a service, but marks the displacement of its strategic value. Code, once a major barrier to entry, is becoming an accessible commodity.

The ultimate argument of SaaS salespeople relied on simple arithmetic: the cost of internal development. For a specialized tool at €50 per month, the calculation was quick. Developing an application capable of importing files, applying business rules, and generating reporting historically cost between €20,000 and €50,000 in engineering.

Today, with AI-assisted development and code generation platforms, a functional prototype meeting that same need can be built in a few days. The economic advantage of a subscription over internal development has shrunk by a factor of 10. The financial barrier that protected thousands of micro-SaaS is collapsing.

Exposure to AI disintermediation follows a logical curve related to the complexity and criticality of the data. Purely transactional or formatting tools are the first to be threatened, while systems anchored in complex processes maintain a structural lead.

Risk Level|Software Type|Example Function Very High|Simple utilities|Content generators, basic dashboards High|Standard workflows|Simple CMS, standard recruitment tools Medium|Management systems|CRM, collaborative project management Low|Infrastructure|ERP, payment gateways, cybersecurity

The most endangered category boils down to a simple equation: an interface, a database, a few workflows, and two API calls. This type of structure is now reproducible on demand by any technical department equipped with generative AI tools.

If code has become easy to produce, six pillars remain out of reach for rapid AI-assisted development. These barriers define a publisher's survival in this new technological cycle.

• Critical Infrastructure: A payment interface can be cloned, but not the banking license, fraud management, and global compliance (e.g., Stripe).
• Proprietary Data: The algorithm is accessible, but unique and non-public data history is not (e.g., Semrush).
• Network Effect: The value of a tool sometimes lies in the number of users connected to each other, which no code can replace (e.g., LinkedIn).
• System of Record: The tool that holds the "truth" about the customer and the history of modifications remains the pivot of the company.
• Compliance and Trust: You don't entrust payroll or health data to a script generated on a Tuesday afternoon; certification and legal liability are assets.
• Integration Ecosystem: Maintaining 50 connectors whose APIs constantly change requires human maintenance that generated code does not handle natively.

The gap no longer separates those who know how to code from those who don't, but those who own certified data from those who only own the interface.

The risk doesn't just come from "home-made" competition, but from a radical change in consumption: the erasure of the user interface. In this model, humans no longer log into ten different dashboards; they pilot an AI agent that interacts with software via their APIs.

Yesterday, the flow was: User → SaaS Interface → Action. Tomorrow, it becomes: User → AI Agent → SaaS API → Action. For the publisher, this means losing visual contact with the user and, by extension, the difficulty of justifying a price based on the number of seats or advertising exposure.

Imagine a request: "Prepare the launch in Germany." The agent queries the CRM for targets, an image bank for visuals, and a translation tool for texts. The user never sees these software programs. They become invisible back-ends, transactional engines whose value resides solely in the reliability of their API response.

Software still exists, but no one opens it anymore.

The addressable market for generic software is contracting in favor of result-oriented solutions. Selling access to a mailing tool to 10,000 companies becomes complex when each can have its own hyper-personalized automated system. Customer expectations are evolving: "I no longer want to rent your tool, I want the work to be done."

This shift to "Software as a Result" forces publishers to rethink their business model. Success is no longer measured by time spent on the platform, but by the quality of the output generated. This favors players capable of guaranteeing surgical business precision rather than average versatility.

This change does not condemn publishers but pushes them higher up the value chain. They become process guarantors, result certifiers, and integrators of complex flows. Selling an empty interface for the user to fill is an endangered model.

Artificial intelligence transforms software from a work tool into an autonomous production engine.

Faced with this mutation, companies must streamline their technical stack. At Good Morning AI, we apply a strict audit grid to determine the fate of each active subscription. The goal is to free up budget for innovation by eliminating unjustified technological rents.

Arbitration criteria include the nature of the data (is it recreatable?), the required level of compliance, and the real cost of exit. It is crucial never to replace a system carrying the company's memory with an unstable script.

Auditing the portfolio is no longer an accounting option; it is a strategic necessity.

AI should not be seen as the gravedigger of SaaS, but as its catalyst into a new era. In the 2000s, the shift to the cloud did not kill software; it simply made the CD-ROM format obsolete in favor of agility and recurrence. AI operates a similar transition by moving value from the user interface to orchestration and data.

SaaS is not dying; it is emancipating itself from the screen to become the invisible engine of automation. The winning companies will be those that can distinguish structural tools, which must be protected, from surface tools, which must be automated or internalized.

At Good Morning AI, we support this transition to transform your software infrastructure into a performance asset. A 30-minute meeting is often enough to identify the first pockets of optimization in your application landscape.

Value no longer resides in possessing the tool, but in mastering the result.