“Chatgpt does not understand your job”: 95 % of AI projects fail in business, according to MIT
Without real adaptation to trades, the generative AI remains at the heart … but next to the plate. © Stokkete
We thought generative AI Capable of upsetting the operation of companies, automating all-out, boosting sales and chasing thankless tasks. But in the field, enthusiasm comes up against a much more raw truth: in 95 % of cases, projects never exceed the stage of experimentation. The latest MIT report, The Genai Dividelifts the veil on a huge technological misunderstanding.
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The figures that deflate the bubble
The MIT does not go there by four paths: after the analysis of 300 public deployments of AI, 150 interviews with managers and a survey with 350 employees, the verdict falls. Only 5 % of pilot projects manage to generate a real impact on turnover or operations.
Only 5 % of generative AI initiatives produce real acceleration of income.
The rest ? Noise, waved budgets, and demobilized teams.
Not the fault of the AI, but of those who implement it
In companies, the use of generative AI often escapes official circuits, a discreet but disorganized revolution. © Shutershock
Contrary to what one might think, failure is not due to the quality of the models. Nor is it a regulation problem. For Aditya Challapally, MIT researcher and principal author of the report, the real knot of the problem is elsewhere: “Some leaders blame the performance of the models or the regulations, but in reality, it is their own organization which does not progress quickly enough to integrate these tools.”
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Models like Chatgpt shine for individual use, but prove to be unsuitable for the structured environment of the company. They struggle to learn internal processes, to adapt to specific workflows. Result: frustration, abandonment, and stagnation.
Tools like Chatgpt excellent for individuals, but fail in business because they do not know how to learn internal processes or adapt to it.
Those who succeed do the opposite
In reverse of large centralized organizations, agile start-ups succeed where the giants fail. Some young shoots, led by founders 19 or 20 years old, are even “Get from zero to 20 million turnover” in one year. Their recipe? Identify a single point of pain, attack it with a simple solution, and above all, do not want to build everything yourself.
Almost wherever we went, companies wanted to build their own tool.
The report is formal: the projects developed internally fail in two out of three cases, while those built on third -party solutions succeed twice more often. In other words, the obsession with “homemade” is expensive… for nothing.
Poorly oriented budgets, low -measured effects
Another classic error: companies are massively betting on commercial or marketing tools, while the real return on investment is hidden elsewhere. The MIT underlines that the automation of support functions, the reduction of subcontracting and the rationalization of operations offer much more measurable gains.
But for lack of relevant indicators, the directions are groped. The report evokes a generalization of the “ghost ia”: tools as a chatgpt used by employees, without validation or supervision … and above all, without any reliable impact measure.
In companies, the generative AI often unfolds through the service door, without validation or clear strategy © Shutershock
An effect on discreet employment … for the moment
No massive layoffs in sight, says the MIT, which rather notes a non-replacement of the vacancies in the functions perceived as not very critical: customer, administrative, office automation service. The change is already there, but it advances masked.
In parallel, the most advanced companies explore a new border: Agent AIs, or IA agentscapable of acting alone, memorizing, learning, and taking initiatives in a defined framework. A new generation of tools, which could rebound all the cards.
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