Companies are investing heavily in artificial intelligence. They\u2019re purchasing licenses, testing co-pilots, deploying agents, and announcing internal programs. Yet, in most cases, the bottom line remains unchanged. <\/p>\n\n
The problem is no longer a technological one. It is an organizational one. <\/p>\n\n
This was the conclusion reached during a presentation focused on what its authors call \u201cthe symbiotic enterprise\u201d: a model in which humans, software agents, and robots do not simply overlay the old world of work, but entirely reshape the way it is produced.<\/p>\n\n
Since ChatGPT\u2019s arrival in 2022, companies have gone through several phases. The first was one of wonder: a tool capable of producing fluent language, writing, summarizing, and rephrasing. Useful, but still static, with no working memory or deep connection to internal systems. <\/p>\n\n
The second phase involved agents. They were more autonomous and capable of performing a sequence of tasks, but were often too generic\u2014like a brilliant young analyst who lacked knowledge of the context, unwritten rules, and internal constraints of the organization. <\/p>\n\n
The third phase is now beginning. The models are becoming more effective. Agents are acquiring specialized skills. Companies can gradually encode their best internal know-how into systems capable of replicating certain practices of their most effective employees. <\/p>\n\n
But this promise hits a wall: the old operational model.<\/p>\n\n
Most organizations integrate AI into workflows designed for humans. They add a co-pilot to an existing process, an agent to a validation chain, or a code-generation tool to an unchanged development method. The result is often modest: a few isolated gains, but rarely a comprehensive transformation. <\/p>\n\n
The phrase \u201chuman in the loop,\u201d long presented as a safeguard, becomes a limitation here. When every decision, every validation, and every execution must be referred back to a human, AI speeds up locally but does not change the system. It produces results faster, but within a slow architecture. <\/p>\n\n
Software development is a telling example. AI tools already make it possible to produce more code. But if the organization sticks to its two-week cycles, the same validation steps, and the same bottlenecks, overall productivity improves only slightly. The real breakthrough comes when the company rethinks the process itself: shifting from a two-week sprint to a one-day cycle, reorganizing teams around results, automating repetitive decisions, and reserving human intervention for high-value decisions. <\/p>\n\n
The symbiotic company is built on this shift.<\/p>\n\n
In this model, humans do not disappear. Their role changes. They are less directly involved in production. Instead, they guide, supervise, prioritize, and mediate. Agents take on part of the cognitive work. Robots, for their part, extend this transformation into the physical world. <\/p>\n\n
This physical aspect becomes crucial. For thirty years, industrial robots have been effective in closed, stable, and predictable environments. Even the slightest deviation often required human intervention. This was not just a mechanical problem, but a cognitive one. <\/p>\n\n
Recent advances in world models, vision-language-action systems, embedded intelligence, and digital twins are a game-changer. A robot can learn in a simulated environment, anticipate the effects of its movements, and adapt more quickly to the variability of the real world. The production system is no longer merely automated. It is becoming trainable. <\/p>\n\n
Amazon provides a concrete example: its most advanced warehouses are not traditional warehouses to which robots have simply been added. They have been redesigned to facilitate collaboration between humans, software, and machines. <\/p>\n\n
That is precisely what many companies have not yet understood about AI.<\/p>\n\n
The question isn’t, “What tool should we buy?” It becomes, “What work model should we rebuild?”<\/p>\n\n
This transformation goes beyond productivity. Productivity gains are often eventually replicated and then partially captured by customers in the form of lower prices or improved service. The real competitive advantage will come from elsewhere: the ability to innovate faster, continuously adapt offerings, create new revenue streams, and scale through software rather than by adding labor. <\/p>\n\n
But this change is undermining the old safeguards.<\/p>\n\n
Expertise becomes less of a competitive advantage when agents can replicate some of the specialized skills. Scale offers less protection when small, AI-driven structures can operate with the reach of a large organization. Coordination becomes less of a rarity when agents orchestrate complex workflows across teams, partners, and customers. <\/p>\n\n
Another risk is emerging: dependence on AI infrastructure providers. As models become integrated into critical processes, an increasing share of the value may be captured by those who control the technical layers. Companies risk paying a kind of \u201ccognitive tax\u201d: the more central AI becomes, the more strategic the infrastructure becomes. <\/p>\n\n
The solution lies in building proprietary AI. This does not necessarily mean training your own large language model, but rather combining internal data, domain expertise encoded as agent-based capabilities, learning loops, and architectures that are modular enough to avoid vendor lock-in. <\/p>\n\n
Transformation cannot, therefore, be left solely to the IT department. It involves the CEO, the CHRO, the transformation team, business units, data governance, and operations. It touches on the very design of the company. <\/p>\n\n
There are two pitfalls that leaders must watch out for.<\/p>\n\n
The first is incrementalism: moving too slowly, optimizing the old model, and expanding use cases without changing the architecture. The second is rushing headlong into things: automating too quickly, without technological maturity, without internal buy-in, and without an understanding of operational risks. <\/p>\n\n
There is a middle ground: defining the future business model, reinventing key functions sector by sector, building modular AI foundations, and forming a leadership coalition capable of driving this transformation as a corporate initiative\u2014not as a software program.<\/p>\n\n
AI will not reward the companies that spend the most. It will reward those that can adapt the fastest. <\/p>\n","protected":false},"excerpt":{"rendered":"
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