Artificial intelligence is currently facing a paradox.<\/p>\n\n
Never before have companies invested so much in this technology. Never before have boards of directors devoted so much time to AI. Yet the economic benefits remain largely invisible. <\/p>\n\n
According to data presented at VivaTech, nearly 80% of large companies are investing heavily in artificial intelligence. However, only 6% report a significant impact on their income statement. <\/p>\n\n
The problem isn’t a technological one.<\/p>\n\n
The problem is organizational.<\/p>\n\n
For two decades, companies have learned to integrate new tools into their existing processes. ERP, CRM, the cloud, collaborative platforms\u2014each innovation served to strengthen an already established organization. <\/p>\n\n
AI works differently.<\/p>\n\n
It doesn’t fit naturally into the old model. It challenges it. <\/p>\n\n
Most companies today use artificial intelligence as an assistant\u2014a co-pilot that helps draft text, generate code, summarize a meeting, or analyze a document. <\/p>\n\n
This approach improves individual productivity by a few percent.<\/p>\n\n
But it doesn’t transform the company.<\/p>\n\n
Real change occurs when AI stops merely assisting with work and becomes an integral part of the work itself.<\/p>\n\n
This is what the speakers referred to as the \u201csymbiotic enterprise.\u201d<\/p>\n\n
In this model, humans, AI agents, and robots no longer work in separate silos. They operate within a single system designed from the outset to leverage the capabilities of each. <\/p>\n\n
The example of Amazon is telling.<\/p>\n\n
The group’s warehouses are not simply traditional warehouses with a few robots added to them. They have been completely redesigned to enable constant collaboration between software, machines, and human operators. <\/p>\n\n
The result is not just a minor improvement.<\/p>\n\n
This is a new production architecture.<\/p>\n\n
This logic is also evident in software development.<\/p>\n\n
Code-generation tools now enable developers to produce five to ten times as many lines of code as before.<\/p>\n\n
However, overall productivity is growing only slightly.<\/p>\n\n
Why?<\/p>\n\n
Because the real bottleneck is no longer writing code.<\/p>\n\n
Human validation has become the bottleneck.<\/p>\n\n
The process remains the same. Decisions, checks, and trade-offs continue to follow the same hierarchical channels as they did before the introduction of AI. <\/p>\n\n
Companies are speeding up certain tasks.<\/p>\n\n
They aren’t reinventing the system.<\/p>\n\n
This is precisely what experts refer to as the \u201cHuman in the Loop\u201d trap.<\/p>\n\n
For years, this concept has been presented as a guarantee of safety.<\/p>\n\n
Today, it sometimes gets in the way.<\/p>\n\n
Every manual approval creates a backlog. Every check creates a bottleneck. Every decision is sent up the chain of command before being sent back down. <\/p>\n\n
AI then speeds up the work locally while leaving the overall structure intact.<\/p>\n\n
The promise remains limited.<\/p>\n\n
True transformation involves rebuilding the workflows themselves.<\/p>\n\n
Tomorrow, the question will no longer be, \u201cHow can we integrate AI into our organization?\u201d<\/p>\n\n
The question will be: \u201cWhat kind of organization would we design if AI had already existed for twenty years?\u201d<\/p>\n\n
This consideration becomes even more important with the advent of physical AI.<\/p>\n\n
For several decades, industrial robots have performed exceptionally well in perfectly controlled environments. Their weakness was not mechanical but cognitive. <\/p>\n\n
As soon as an unexpected situation arose, human intervention became necessary.<\/p>\n\n
Recent advances in multimodal models, vision-action systems, and digital twins are gradually changing this equation.<\/p>\n\n
Robots no longer just follow instructions.<\/p>\n\n
He is beginning to understand his surroundings.<\/p>\n\n
For the first time, the cognitive revolution and the physical revolution are converging.<\/p>\n\n
The potential impact is considerable.<\/p>\n\n
Experts estimate that approximately 60% of economic activities could eventually be automated or largely supported by intelligent systems.<\/p>\n\n
This transition will obviously be neither immediate nor linear. The history of electricity, digital technology, and automation shows that the widespread adoption of major technologies often takes several decades. <\/p>\n\n
But the direction now seems clear.<\/p>\n\n
This shift also calls into question the traditional foundations of competitive advantage.<\/p>\n\n
Expertise is becoming easier to replicate.<\/p>\n\n
Size no longer poses the same barriers to entry.<\/p>\n\n
The coordination of large ecosystems can be automated.<\/p>\n\n
Even transaction costs are beginning to disappear thanks to interactions between agents.<\/p>\n\n
In this context, companies will no longer be able to differentiate themselves solely on the basis of their expertise or scale.<\/p>\n\n
Three new strategic assets are emerging.<\/p>\n\n
The first is proprietary intelligence: exclusive data, domain expertise encoded in the agents, specialized models, and internal learning loops.<\/p>\n\n
The second is monitoring critical touchpoints in customer journeys and ecosystems.<\/p>\n\n
The third is the ability to effectively coordinate people, agents, and robots within a coherent system.<\/p>\n\n
In other words, AI isn’t just transforming productivity.<\/p>\n\n
It is transforming the very nature of the company.<\/p>\n\n
Executives who still view artificial intelligence as merely an IT tool risk achieving only marginal improvements in their efficiency.<\/p>\n\n
Those who view it as a new labor force will rethink their business model.<\/p>\n\n
The difference between the two approaches could determine who the economic leaders of the next decade will be.<\/p>\n","protected":false},"excerpt":{"rendered":"
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