In recent years, the rapid rise of AI has driven many enterprises to accelerate the deployment of experimental projects. Many models achieve high accuracy and impressive results in testing environments. However, when put into operation, these systems often fail to deliver the value initially expected. This is no longer an issue faced by any single enterprise.
According to McKinsey’s The State of AI 2025 survey, nearly two-thirds of organizations are still at the experimentation or pilot stage and have yet to scale AI across the enterprise. Despite the widespread adoption of AI, only 39% of organizations report significant enterprise-level impact on business performance. These figures highlight a notable reality: the challenge is no longer whether enterprises can build a functional AI model, but rather the gap between a model that performs well and a system capable of delivering sustainable value in a real-world enterprise environment.
In many projects, PoCs are built on cleaned datasets, within controlled problem scopes, and with a limited number of users. However, once deployed in practice, AI is no longer dealing solely with a modeling problem. It must operate within the broader context of an enterprise: continuously changing data, complex business processes, security requirements, integration with existing systems, performance, operational costs, and user experience. These are the factors that ultimately determine whether AI can become an integral part of business operations.
Drawing on experience in deploying enterprise AI systems, Fabbi believes that the success of an AI project should not be measured by model accuracy alone, but by its ability to operate reliably and deliver value over years of use. Therefore, AI should not be designed from the perspective of a technology demonstration, but from the actual operational context of the enterprise.
This is also how Fabbi approaches AI projects: bringing together research, engineering, and product development from the outset to address challenges at operational scale, rather than stopping at proving that a technology can work. After all, in the era of AI, the value of a system is not determined by how intelligent its model is, but by its ability to create sustainable impact in real-world operations.
