80 % of large companies plan to integrate AI into their IT processes by 2026, while its global market is expected at more than $ 500 billion by 2028.
This explosion underlines a crucial question: how far will AI transform the engineering of platforms? Because if some go so far as to predict the end of the human in their conception, their deployment and their management, the operational reality underlines all the limits of the AI. Faced with constraints of robustness, security and scalability, humans retains an essential role, that of arbitrator, architect and project manager, in order to ensure its operation, evolution and optimization.
Few use cases indeed go beyond simple operational assistance. It is enough to observe the terrain: most of the cases presented under the “IA” label, whether the generation of code, the creation of case studies or the writing help of documentation, are already covered by more robust, simpler and easier to manage methods.
Yes, AI is precious when it comes to accelerating certain tasks, up to prototyping. It is particularly effective when it takes care of recurring cases of study, or when it serves as a productivity lever.
But when you go to the production scale, it highlights all its shortcomings: the generated code is rarely fairly robust, maintainable or secure, and requires significant manual recovery.
Humans, guaranteeing the robustness and evolution of systems
This is particularly the case in platform engineering, a complex profession, which goes far beyond the writing of a few dozen lines of code, where discipline consists not only in assembling technological bricks, but also giving them meaning, global coherence and a capacity for evolution. It requires in -depth operational experience, a perfect knowledge of business use cases, and the ability to arbitrate competing solutions in order to offer the maximum value, both technically and business.
To stick to AI without human intervention is to run the risk of accumulating technical debt, to increase the complexity of the systems and to erode your internal skills. This experience, this critical look, this operational know-how are however essential to ensure their relevance, their robustness and their sustainability-and therefore their ability to support the evolution of the trades in the long term.
Reconcile innovation and efficiency with discernment
The environmental and operational cost of AI is itself a factor that is too often underestimated. Each interaction with a generative model consumes energy, and when each developer uses it daily, the global imprint takes on alarming proportions all the more in a context already complicated by the accumulation of study cases, deployments and tools.
However, AI keeps its interest as a lever for aid or acceleration. It could be particularly effective when it comes to generating documentation, accelerating the writing of study cases or assembling reusable modules that can be integrated into a development platform. It is a productivity factor and not an architectural solution in itself.
The true future of platform engineering therefore belongs to humans: to their ability to arbitrate, assemble and anticipate, by defining robust models, evolutionary operational practices and reliable abstractions. Yes, AI can strengthen this experience, provided they make a lever, not a substitute, in order to offer teams all the conditions to innovate and gain in long -term efficiency.
