Artificial intelligence is currently facing a paradox.
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.
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.
The problem isn’t a technological one.
The problem is organizational.
For two decades, companies have learned to integrate new tools into their existing processes. ERP, CRM, the cloud, collaborative platforms—each innovation served to strengthen an already established organization.
AI works differently.
It doesn’t fit naturally into the old model. It challenges it.
Most companies today use artificial intelligence as an assistant—a co-pilot that helps draft text, generate code, summarize a meeting, or analyze a document.
This approach improves individual productivity by a few percent.
But it doesn’t transform the company.
Real change occurs when AI stops merely assisting with work and becomes an integral part of the work itself.
This is what the speakers referred to as the “symbiotic enterprise.”
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.
The example of Amazon is telling.
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.
The result is not just a minor improvement.
This is a new production architecture.
This logic is also evident in software development.
Code-generation tools now enable developers to produce five to ten times as many lines of code as before.
However, overall productivity is growing only slightly.
Why?
Because the real bottleneck is no longer writing code.
Human validation has become the bottleneck.
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.
Companies are speeding up certain tasks.
They aren’t reinventing the system.
This is precisely what experts refer to as the “Human in the Loop” trap.
For years, this concept has been presented as a guarantee of safety.
Today, it sometimes gets in the way.
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.
AI then speeds up the work locally while leaving the overall structure intact.
The promise remains limited.
True transformation involves rebuilding the workflows themselves.
Tomorrow, the question will no longer be, “How can we integrate AI into our organization?”
The question will be: “What kind of organization would we design if AI had already existed for twenty years?”
This consideration becomes even more important with the advent of physical AI.
For several decades, industrial robots have performed exceptionally well in perfectly controlled environments. Their weakness was not mechanical but cognitive.
As soon as an unexpected situation arose, human intervention became necessary.
Recent advances in multimodal models, vision-action systems, and digital twins are gradually changing this equation.
Robots no longer just follow instructions.
He is beginning to understand his surroundings.
For the first time, the cognitive revolution and the physical revolution are converging.
The potential impact is considerable.
Experts estimate that approximately 60% of economic activities could eventually be automated or largely supported by intelligent systems.
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.
But the direction now seems clear.
This shift also calls into question the traditional foundations of competitive advantage.
Expertise is becoming easier to replicate.
Size no longer poses the same barriers to entry.
The coordination of large ecosystems can be automated.
Even transaction costs are beginning to disappear thanks to interactions between agents.
In this context, companies will no longer be able to differentiate themselves solely on the basis of their expertise or scale.
Three new strategic assets are emerging.
The first is proprietary intelligence: exclusive data, domain expertise encoded in the agents, specialized models, and internal learning loops.
The second is monitoring critical touchpoints in customer journeys and ecosystems.
The third is the ability to effectively coordinate people, agents, and robots within a coherent system.
In other words, AI isn’t just transforming productivity.
It is transforming the very nature of the company.
Executives who still view artificial intelligence as merely an IT tool risk achieving only marginal improvements in their efficiency.
Those who view it as a new labor force will rethink their business model.
The difference between the two approaches could determine who the economic leaders of the next decade will be.
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