Written by Julien Ricciarelli-Bonnal

10 December 2025

The Global Productivity Rebound: Why AI Adoption Is Rising but Efficiency Isn’t

For the past two years, every economic indicator has pointed to the same paradox: companies worldwide are accelerating their adoption of artificial intelligence, automating workflows, redesigning processes and integrating new tools at a pace never seen before. And pourtant, the expected productivity boom — the famous rebound promised by consultants, analysts and tech evangelists — isn’t here. Output per employee barely increases. Operational bottlenecks persist. Decision-making slows down instead of accelerating. The gap between potential and reality widens each quarter. Something fundamental is happening: organisations are equipping themselves faster than they are learning to work differently.

This paradox isn’t the failure of AI.
It’s the failure of organisations that believed technology alone would transform their structure, their culture and their operating logic — without touching anything else. The world is discovering that productivity is not the mechanical consequence of adopting tools but the strategic consequence of understanding how people, processes and decision chains adapt to them.

The world is adopting AI faster than its ability to absorb it

Across continents, from European SMEs to Asian manufacturers and American service companies, AI adoption curves follow the same trajectory: onboarding is fast, expectations are high, internal alignment is weak. Businesses implement tools before they articulate the purpose, the scope, or the operational contours of these tools. The result is predictable: employees spend more time understanding how to combine systems than performing the work itself.

Most organisations underestimate the cognitive load associated with AI integration.
A model is not simply a tool: it modifies the rhythm of work, the sequence of tasks, the nature of decision-making and the expectations placed on teams. When an organisation moves faster than its people can adapt, the system compensates by creating layers of friction: duplicated tasks, parallel processes, inconsistencies between guidelines and execution, and — ironically — a slowdown in output.

This explains why productivity remains stagnant despite the rapid spread of generative models. Adoption is exponential, absorption is not.

Tools multiply, but decision-making structures remain frozen

The underlying problem is not technological but architectural. AI accelerates everything — information flow, exploration, synthesis, iteration — but most companies still operate with slow, hierarchical decision systems designed for a different era. When the velocity of data increases while the velocity of decisions does not, productivity collapses under the weight of its own potential.

Leaders imagine AI as an accelerator.
But an accelerator does not help when the brakes — the structure, the process, the internal alignment — remain fully engaged.

Many organisations continue to centralise decisions, validate every output manually, and maintain approval cycles incompatible with real-time tools. Others diversify their AI stack without clarifying who owns what, which model serves which function, or how teams should interpret outputs.

The result is a landscape where AI reveals weaknesses faster than companies can hide them.
The illusion of efficiency breaks the moment an organisation realises that speed without clarity creates more chaos, not less.

The true productivity gap comes from skills, not software

Another silent factor emerges across markets: the skills mismatch. While AI grows more powerful, the average digital maturity of teams does not progress as fast. Employees are expected to navigate prompts, interpret outputs, validate information and integrate it into meaningful decisions — all without the training or frameworks required to do so.

The myth of “plug-and-play intelligence” has created a generation of disappointed adopters.
AI is not plug-and-play.
It is learn-and-integrate.

Countries with strong upskilling programs — the Nordics, South Korea, Singapore — experience fewer frictions because the workforce is trained to combine human judgment with machine acceleration. Countries that prioritise tool adoption over talent development face stagnation. They deploy the instruments of the future with the habits of the past.

The global productivity plateau is therefore not an AI issue but an alignment issue: technology progresses faster than people’s ability to transform their mental models.

Companies that see real gains follow a simple rule: clarity first, technology second

If we analyse the organisations that achieve measurable productivity improvements, a pattern emerges. Before integrating AI into workflows, they stabilise three things:

1. A clear strategic intention
Not “use AI”, but “use AI to improve X, accelerate Y, reduce Z”.
This alone eliminates 80% of the organisational noise.

2. A coherent operational structure
Roles, responsibilities, decision paths and validation cycles are clarified.
AI enters a stable architecture instead of being injected into a moving target.

3. A disciplined approach to adoption
One tool at a time.
One process at a time.
One team at a time.
Productivity comes from depth, not accumulation.

In these organisations, AI does not replace judgment — it reinforces it.
It does not accelerate confusion — it organises information.
It does not distract from priorities — it amplifies them.

The world is witnessing a shift: productivity comes not from technological abundance but from strategic sobriety.

The global productivity rebound will not come from AI itself — but from the maturity with which companies learn to apply it

The next wave of productivity will not be driven by the companies that deploy the most tools, but by those that understand the difference between technological potential and operational reality. Efficiency will rise when decision-making becomes lighter, when teams understand how to combine intuition and data, when organisations stop mistaking adoption for transformation.

The paradox of 2025 is temporary.
But its lesson is permanent:
AI accelerates everything — including the consequences of organisational inconsistency.

The companies that will lead the next cycle are those that reconcile three forces: speed, clarity and structure. They will not use AI to appear modern but to become genuinely effective. And in a world overwhelmed by complexity, coherence is becoming the most powerful productivity engine of all.