IMD ranking puts U.S., China below governance leaders

IMD 2026 – IMD’s 2026 World Competitiveness Ranking places the U.S. at 10th and China at 12th, with Singapore, Hong Kong, and Switzerland topping the list—arguing that institutional credibility, not cheapness or speed, is now the core of competitiveness. The same logic i

This month, the conversation around competitiveness landed with a quiet shock: the largest economies didn’t top the scoreboard.

IMD—one of Europe’s leading business schools. where Faisal Hoque serves as an Executive Fellow and North America Program Co-Director—released its 2026 World Competitiveness Ranking. In a keynote delivered at the U.S. launch of the index at the Swiss Embassy in Washington. Hoque said the results sit awkwardly with the stories the world’s largest economies tell about being world-leading.

In the ranking, the U.S. holds 10th place globally, while China is 12th. The most competitive economies in the world are Singapore, Hong Kong, and Switzerland.

The common thread across those top performers is governance. Hoque’s reading of the data is blunt: competitiveness now turns on institutional credibility—predictable rules. enforceable commitments. and the capacity of a state to govern—more than on cost. scale. or even speed of innovation. In his view, governance isn’t the price a country pays to become competitive. It’s becoming the source of competitiveness.

That message challenges a widespread assumption in global business and policy circles: that the less a country governs its markets and its innovators, the more competitive it will be—treating regulation as a tax on dynamism. Hoque argues the opposite is visible in the economies that compete best.

Governments, he says, can steer industries without necessarily slowing innovation. They control levers that shape how businesses develop and what behaviors governments reward—through tax codes. through requirements connected to government procurement. through what firms are required to disclose. and through how incentives are structured. With AI specifically. the design of those levers can determine whether deployments are aimed at replacing workers or making them more capable.

He uses a clear example from tax policy: a tax code that taxes labour more heavily than investments in automation steers businesses toward automation. But governance can flip that incentive. Leaving governance “flipped” the way it currently is. he warns. doesn’t automatically create a low-regulation economy—it can instead create an economy where regulation is set by historical circumstances rather than current needs.

The same steering can reach beyond the obvious. Facing falling literacy and numeracy, Norway is keeping generative AI out of its primary classrooms so children still learn to read, write, and count for themselves. For Hoque, the outcome is a choice governed by policy rather than left to drift.

The business parallel runs right into the AI race. Hoque points to a 2025 study by BCG saying that only around 5% of large companies are capturing significant value from AI. even as almost every large company implements some form of AI in its workflows. Those models, he says, are mostly bought rather than built in-house—meaning competitors can purchase the same technology.

If the tech is common, then what distinguishes the winners is not the tool itself. Hoque argues it has to be the organizational structures that decide why AI is used and how it is implemented—where an AI system is allowed to operate and where it isn’t. what it may touch. how its output is checked and by whom. and where teams keep the ability to change course. “Governance. ” in that framing. is the discipline that turns a tool anyone can buy into a result only you could have produced.

That discipline has a named shape: the CARE framework—Catastrophize the ways a deployment could fail; Assess possible failure modes in depth; Regulate what each system is allowed to do; and Prepare an exit plan that you can set in motion if the situation calls for it.

He returns to a central claim as the argument tightens: nations now compete on institutional credibility. and businesses are powered by the same fuel. The difference between competitiveness and mere capability isn’t access to innovation. It’s whether institutions—and companies—govern the conditions under which innovation is allowed to operate.

Putting governance in place, though, isn’t something that happens overnight. Hoque lays out three steps for companies moving on AI governance.

First: map where AI is allowed to act. That means listing where AI is already working in the business—including shadow workstreams where team members are bringing their own AI tools. Then decide what kinds of outputs each use case involves. and whether an AI can make autonomous decisions in that role. develop draft ideas and texts. or should not play any part in the process. Hoque says most organizations have never drawn that line on purpose. which leaves it to be drawn “one tool at a time.”.

Second: rehearse the failure before it happens. For any consequential use of AI. ask how it could go wrong—a confident wrong answer. a biased pattern. or a drift in scope or quality—and decide in advance what you will watch for and when you would pull back. The goal, he says, is to make failure imagination cheaper than production discovery.

Third: develop metrics that capture both success and failure. Teams often record the time an AI model saves them. he notes. but not the extra work caused or the errors it propagates. Without good metrics, problems can remain invisible and spread. Hoque’s instruction is to decide how you will measure whether a deployment is working and track failures as deliberately as wins.

Alongside those steps, he adds one operational requirement: name who oversees outputs, and what that oversight means. For each system—or each type of system—assign responsibility to a specific person who answers for every output. then require a comprehensive review process appropriate to the model and the use case.

And he insists on a final safeguard: keep a route out. Before committing to a model or vendor. write down how you would leave—what switching would cost. what you would lose. and what you would need to hold in-house to maintain flexibility. Treat the absence of an exit plan as a major threat to business integrity. even if the model you’re using has always delivered in the past.

None of it, Hoque stresses, is a brief for heavy regulation, and none of it asks businesses to implement fewer AI initiatives. The push is the opposite of hesitation: a deliberate decision about how AI runs inside a company, rather than a default arrived at “one tool at a time.”

Because the technology competitors can access is already the same, he argues the contest is already partly drawn. What remains open is whether you govern AI better than others do—where you let it act, who answers for what it produces, and which work stays in human hands.

IMD’s competitiveness leaders, in Hoque’s telling, earned their place by treating governance as the substance of competition rather than a brake on it. For companies, the lesson is the same: the advantage doesn’t sit in the tools; it sits in the judgment brought to implementing them.

IMD 2026 World Competitiveness Ranking institutional credibility governance competitiveness Singapore Hong Kong Switzerland United States China AI governance BCG study 2025 CARE framework organizational structures