The New Corporate Messiah Is Called AI
The discourse around artificial intelligence has taken on an almost messianic tone. Some hail it as the savior of productivity, others see it as the beginning of the end of human work. Between fascination and fear lies a more complex problem: most companies still think wrongly about AI’s role inside organizations.
An article published in the Journal of Management Studies makes a crucial provocation. The debate on AI in management still rests on two mistaken ideas:
1. the belief in perfect rationality, and
2. the tendency to humanize the machine.
Both distort how we make decisions and design strategies.
The first belief assumes that AI decides better because it has no emotions, no distractions, and makes no mistakes. It’s an old technological myth, that machines are more rational than humans. In reality, AI simply reproduces the patterns of what has already happened. It doesn’t interpret, it doesn’t understand context, and, most importantly, it doesn’t question the premises on which it operates.
When data carries bias, inequality, or historical distortion, the algorithm doesn’t correct them, it reinforces them. And crucially, it reinforces them more efficiently.
Companies that treat AI as a substitute for human judgment end up transferring their own distortions into code. Instead of correcting the error, they automate it and call that efficiency.
The result is highly predictable: cold, blind decisions. Sophisticated models built on faulty assumptions. The same problem as a “perfect” business-plan spreadsheet that ignores reality at algorithmic scale.
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The second mistake is the opposite of the first: treating AI as a smart colleague, a work partner, an “agent” with intention and creativity. That view, which seems harmless, breeds a dangerous kind of complacency. We start projecting onto machines traits they simply don’t possess empathy, intuition, ethics and purpose.
By giving technology a personality, we forget that every AI is a product of human choices.
Code is written by someone, shaped by values, constraints, and priorities defined by people. When we say “AI decided,” we are merely disguising a chain of prior human decisions embedded in data and models.
This confusion of roles produces an intriguing paradox: the more we try to humanize AI, the more dehumanized the organizations that adopt it become. Processes turn opaque, accountability fades, and trust migrates from people to systems.
The point is that AI should not be treated as the organization’s brain, nor as an infallible oracle. It is a structural layer that reorganizes how information circulates, how power is distributed, and how work is executed. It reshapes the company from within, but it should never occupy the place of those who define its direction.
The leader who understands this doesn’t ask, “What can AI decide for me?” but rather, “How can I decide better with AI’s support?”
The difference is enormous, it is abyssal. In the first question, the human disappears; in the second, the human evolves.
Mature companies are learning to use AI as an infrastructure for collective reasoning, a foundation to improve diagnosis, reduce noise, and enhance both the quality and, eventually, the speed of decision-making. But the final word must always remain human, because context, ethics, and purpose are not machine variables.
The competitive advantage of the next decade will not come from AI’s technical power but from companies’ cognitive maturity in dealing with it. Those that treat AI as a tactical tool will get lost in marginal, fleeting gains in productivity.
Those that integrate it strategically will redefine the standards of efficiency and management.
In other words: it’s not AI that will think for the company.
It’s the company that must learn to think differently with AI as an extension of its collective intelligence.
While many are still training algorithms to seem human, the organizations preparing for the future are training people to think better with machines.
Christiano Ranoya writes about AI, loyalty, and the human side of business.
Founder of INDICO and doctoral researcher at FGV, he studies how organizations think in the age of algorithms.
