Vibe Coding in Industry: Making Decisions in Two Hours

Vibe coding refers to a development practice that involves generating code using generative artificial intelligence tools, often in short, intensive sessions. In an industrial context, this approach is used to explore use cases, test automations, or prototype internal tools without going through lengthy project cycles. It relies on the ability of models to generate functional code from textual instructions and to accelerate already known technical tasks.

Its rise is taking place in an environment where performance, quality, and deadline constraints are intensifying. Industrial teams are seeking to reduce the time between identifying a need and making an operational tool available. Technical departments view these tools as a means of accelerating progress, particularly for prototyping, data exploration, script generation, and technical documentation. From an economic standpoint, the promise is twofold: to reduce experimentation costs and shorten iteration cycles.

But the speed of execution profoundly changes team dynamics. Whereas traditional development allows time to formalize business rules, “vibe coding” requires decisions to be made immediately. Generative models quickly produce implementations but require clear criteria, defined rules, and a stable scope. In the absence of these elements, speed becomes a factor that amplifies ambiguities rather than a real gain in productivity.

The central issue, therefore, lies not in the technical performance of the tools, but in the decision-making maturity of organizations. A two-hour session can result in a usable deliverable, provided that the beneficiary is identified, the success criteria are clearly defined, and a decision-maker is present to arbitrate in real time. Conversely, in the absence of clear guidance, the tool generates successive versions without a clear path to convergence, revealing implicit rules, unresolved edge cases, and differences in interpretation across departments.

There are numerous areas of application: automatic generation of production reports, data analysis scripts, machine configuration aids, creation of internal interfaces, and automation of repetitive tasks. In these contexts, the benefits are tangible: overcoming writer’s block, accelerating prototyping, reducing the initial development effort, and supporting the structuring of specifications.

Limitations, however, become apparent as soon as environments become mission-critical or highly regulated. Systems subject to certification, complex legacy architectures, or processes with high security stakes require traceability, auditability, and thorough validation of the generated code. Furthermore, the quality of the available data directly determines the relevance...