The next wave of AI productivity will not come from plugging smarter tools into existing workflows. It will come from executives willing to question why those workflows exist in the first place. As AI models become more powerful and accessible, the binding constraint has shifted from technical capability to organizational design.
Nvidia's $12.93 billion acquisition of Hugging Face in 2026 underlined this shift. The deal was not about acquiring model-building technology. Hugging Face operates a platform where millions of developers and more than 200,000 companies discover, evaluate, and deploy AI models. The acquisition signals that industry attention is moving downstream - from building better models to making AI actually work inside companies.
This widening gap between what AI can do and what organizations can absorb is what Rewire CEO Wouter Huygen calls the AI diffusion gap.
The electric motor parallel
The pattern is not new. When factories first adopted electricity, most simply replaced a central steam engine with an electric motor and left everything else unchanged. The real productivity gains arrived later, when factories were redesigned around distributed power, allowing individual machines to operate independently.
AI is entering a similar phase. Most modern companies were built around human constraints - limited memory, coordination capacity, and cognitive bandwidth. Work was divided into departments, management layers, approvals, and handovers to compensate. AI changes those assumptions. Intelligent systems can reason across large volumes of information, coordinate in real time, and increasingly execute workflows end to end.
Yet most organizations are still adding AI to structures designed for a different era. A smarter chatbot here. A copilot there. Individual optimizations layered onto an unchanged operating model.
Redesign the process, not the steps
A Dutch telecommunications company with roughly 1,400 contact center employees illustrates the difference. When a customer reports a technical issue, the employee moves through a sequence: identify the customer, review history, classify the problem, search for information, diagnose, determine action, record outcome.
AI can accelerate each step. Speech recognition transcribes the call. A copilot suggests answers. AI-powered search finds information faster. But the underlying process remains the same.
"An AI-native approach starts with a different question: If we were designing this process today, with AI available from the outset, would we design it this way at all?" Huygen said.
Instead of optimizing a sequence of separate tasks, an intelligent system can bring together customer history, network information, and relevant knowledge; diagnose the problem; recommend action; execute parts of the solution; and record the outcome. The employee stays involved where human judgment adds value. The result is not a faster version of the old workflow. It can reduce call duration, unnecessary engineer visits, and hardware replacements while improving customer experience.
The larger opportunity lies not in making ten steps 20% faster, but in asking why those ten steps exist.
What an AI-native organization looks like
Being AI-native does not mean deploying AI everywhere or removing humans. It means treating AI as a design principle: rethinking how knowledge is organized, how decisions are made, and where human judgment adds the most value. For executives, the question is shifting from "Where can we use AI?" to "How would we design this organization if AI were available from the start?"
This does not require a company-wide transformation program on day one. The opposite approach is often more effective. Choose one important end-to-end process. Redesign it around AI. Learn from the implementation. Build the required knowledge, data, and governance foundations. Then expand.
"The transition to an AI-native organisation can start small. But the thinking behind it needs to be radical," Huygen said.
For management professionals navigating this shift, understanding how to redesign processes rather than simply automate them is becoming a core competency. Resources focused on AI for Management can help leaders identify where process redesign will yield the highest return. The strategic dimension - deciding which processes to tackle first and how to sequence the work - falls squarely into the domain covered by AI for Executives & Strategy.
Why this matters for management
The AI diffusion gap is not a technology problem. It is a management problem. The organizations that capture the next wave of AI productivity will be those whose leaders stop asking how to fit AI into existing boxes and start asking whether the boxes still make sense. For managers, the practical starting point is concrete: pick one end-to-end process, map its steps, and ask which of those steps exist only because a human once had to do them. Then redesign from scratch.
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