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By Samuel Zürcher
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Most companies are treating AI as a technology upgrade. They buy the tools, run a pilot, and wait for the productivity gains. The gains do not arrive. We are blaming technology while ignoring the organization around it.
McKinsey & Company, a global management consulting firm, surveyed more than 10,000 senior executives across 15 countries. It found that 86 percent of leaders feel their organizations are not well prepared to use AI in day-to-day operations.
That is not a technology gap. It is an organizational one.
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The prevailing approach makes the problem worse. Companies layer AI onto workflows that were already broken.
A partner at a professional services firm once told me he had no time to write a proposal, so he used AI. He won the deal. He did not know what he had offered because he had not read it.
The client did not know what they had accepted because they had not read it either. AI made a proposal to AI, and a human signed off on something nobody understood.
That is not transformation. That is automation of a process that should never have existed.
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The Problem With Existing Structures
The organizational problem is structural. The hierarchy most companies still use comes from the Industrial Revolution.
A factory produced something predictable. The person with the knowledge was the boss. That worked when the input, the process, and the output were all stable. That world is gone. Markets are unpredictable. Customer demands are individualized. A rigid chain of command cannot adapt fast enough.
AI needs to be at the core, not at the edge.
The alternative is to redesign work itself. That means treating human time as the highest capital in the company. It means investing human skill where it matters most: in empathy, connection, creativity, and out-of-the-box thinking.
AI can rearrange existing information. It cannot create something that has never existed. So the question for every task is simple. Is this work better done by AI because it involves analysis, pattern-finding, or speed? Or does it need a human because a decision has to be made, accountability has to be carried, or empathy is required? That is a division of responsibilities, not a replacement.
An organizational structure has to work more like an organism than a straight process. It has to adapt to changing input from the market, from people, and from customers.
The Human Cost of Poor AI Adoption
The human consequences of getting this wrong are measurable. Microsoft surveyed 20,000 knowledge workers in 10 countries. It found that organizational factors, including culture, manager support, and talent practices, account for 67 percent of reported AI impact. Individual factors account for 32 percent.
Only 26 percent of AI users say their leadership is clearly and consistently aligned on AI. Only 13 percent say they are rewarded for reinventing work with AI when short-term results fall short. That last number is the one leaders should sit with.
Employees are told to adapt. They are told AI is the future. But the incentives still reward the old way of working. People are running forward while the system pulls them back. It is a design problem, mostly mistaken for a people problem.
Redesigning Work Around AI
What should leaders do?
First, audit the work before automating it. Ask what problem the process solves, who owns the decision, and whether the process should exist at all.
Second, give decision rights a clear owner. An AI system that makes a recommendation is not the same as an AI system that makes a decision. Someone has to be accountable for the outcome.
Third, value human capital differently. Treat human time as an investment, not a cost. Fourth, measure outcomes, not adoption. The number of AI tools deployed is not a metric. The number of hours returned to high-value work is.
And be cautious about cost. What companies pay for AI today is not what they will pay in the future. AI is not yet generating enough revenue to cover the cost of running it.
The bill will come due, and the companies that measured their use cases carefully will be the ones still standing.
Economic Stakes
The wider stakes are economic. The next phase of AI competition will not be decided by access to the most advanced models. It will be decided by whether organizations can rethink how work gets done.
The companies that redesign themselves around AI and human capability will pull ahead. The ones that automate the past will find themselves with a more expensive version of the same organization.
And the same shift will reach software itself. We are moving back toward highly individualized tools built around how a specific organization operates, thinks, and works. That is where the competitive advantage will sit.
The AI revolution is more likely to fail because organizations continue to rely on old organizational structures that no longer fit.
Samuel Zürcher is Group CEO of Experts Inside and the founder of FLAIMS, an organizational model built around human-centered AI; all opinions are the author’s own.
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