Name: 哪個國家的 AI 使用最廣泛？分析領先國家的發展策略（Which Country is Using AI the Most?）
Uploaded: 2026-03-04T17:41:04.000Z
Duration: 5 min 53 s

情態副詞

Today, roughly one in six working age adults use AI tools and 88% of organizations now using AI in at least one business function.

如今，大約六個工作年齡成人中就有一個使用AI工具，而88%的組織至少在一項業務功能中使用AI。

But adoption is uneven and the gap is widening.

但採用情況不均，差距正在擴大。

A recent Microsoft report identifies only two countries where more than half the working age population use AI in the past year.

最近一份Microsoft報告指出，只有兩個國家在過去一年中超過一半的工作年齡人口使用AI。

In the UAE, usage stands at about 64%, and in Singapore just shy of 61%.

在阿拉伯聯合大公國，使用率約為64%，而在新加坡則接近61%。

So why are these countries pulling ahead?

那麼，為什麼這些國家會領先呢？

And what does AI adoption actually mean in practice, where AI actually affects the real economy?

並且AI採用在實踐中到底意味著什麼，AI實際上如何影響實體經濟？

It's when it starts to be used by industry, used by employers, used by employees.

當產業、僱主、僱員開始使用它時。

And so adoption really means a translation of theory to practice, when AI starts to actually change workflows.

是以，採用實際上意味著理論實踐的轉化，當AI開始實際改變工作流程時。

Despite its leadership in AI infrastructure and frontier model development, the US ranks 24th globally in AI usage among working age adults, trailing behind smaller, more digitized economies.

儘管美國在AI基礎設施和前沿模型開發方面處於領先地位，但在工作年齡成人AI使用率方面卻位居全球第24名，落後於規模較小、數位化程度更高的經濟體。

The fascinating contradiction that some of the world leaders in innovation are lagging behind in adoption.

一個有趣的矛盾是，一些全球創新領導者卻在採用方面落後。

So the United States, world leader in innovation, really struggles to adopt.

所以美國，這個創新的世界領導者，在採用上真的舉步維艱。

And I think that's partly because the things depend differently on size, that innovation really depends on scale.

我認為這部分是因為事情的發展方式取決於規模，而創新確實取決於規模。

You need resources, you need people, you need that pyramid of talent to be at the forefront.

你需要資源、你需要人才、你需要人才金字塔才能站在最前沿。

But that size also becomes a problem when it gets to adoption size in terms of just the variation across the market, the variation of regulatory systems.

但當涉及到採用規模時，這種規模也成了一個問題，因為市場的差異性、監管系統的差異性。

You've got the federal structure in the United States.

That's a great melting pot for innovation experimentation, but not really a great platform for coherent adoption.

這是一個進行創新實驗的絕佳大熔爐，但對於協調一致的採用來說，它並不是一個很好的平臺。

The UAE and Singapore share a common playbook.

阿拉伯聯合大公國和新加坡有著共同的策略。

Both invested in early and nationwide digital infrastructure.

兩國都投資於早期和全國性的數位基礎設施。

Both pushed AI adoption inside government first and tied AI used to productivity, not experimentation.

兩國都優先在政府內部推動 AI 採用，並將 AI 使用與生產力連結，而非實驗。

Maybe two things that are unique around what the UAE has done.

阿拉伯聯合大公國的獨特之處，可能有兩點。

One is there is as much focus on adopting AI as there is on creating AI.

一是他們在採用 AI 和創造 AI 上，同樣投入了極大的心力。

And we see this in global partnerships that they've made, large investment funds that they set up, like MGX.

我們從他們建立的全球夥伴關係、大型投資基金，例如 MGX，就可以看到這一點。

They set up G42 as a holding company to build AI.

他們成立了 G42 作為控股公司來建構 AI。

I think the second thing that the UAE has done differently is around really formally encouraging adoption in a really dramatic way.

我認為阿拉伯聯合大公國做得不同的第二點，是他們以非常戲劇化的方式，正式鼓勵 AI 採用。

This involves talking about AI, even from schools, but

這包括從學校就開始談論 AI，但

also then working to make AI tools nationally available in some cases.

有時也致力於讓 AI 工具能在全國普及。

And so all of this makes adoption easier and creates an environment where everyone is adopting.

所有這些都讓 AI 採用變得更容易，並創造了一個大家都在採用的環境。

Singapore, I think, is uniquely positioned to take advantage of AI precisely because we're so small, small, highly educated, highly connected.

新加坡，我認為，因為我們很小、受過高度教育、高度連結，所以有獨特的優勢可以利用 AI。

Those are kind of ripe parts of an economy that's ready for AI adoption.

這些都是一個準備好 AI 採用的經濟體中，很適合的環節。

And I think the government really leaning into AI offers a good example of positive use cases.

我認為政府大力投入 AI，是正面使用案例的好榜樣。

And it means that the government is walking the walk as well as talking the talk.

這代表政府不僅說到，也做到了。

They're not just telling industry, you should use AI, they're showing industry that we are using AI in a productive way.

他們不只是告訴產業應該使用 AI，而是向產業展示我們正在以有生產力的方式使用 AI。

But even in leading countries, conversation is shifting.

但即使在領先的國家，對話也在轉變。

Not long ago, the story was about AI's potential.

沒多久以前，大家談論的是 AI 的潛力。

Now the focus is on something harder, making it work at scale.

現在的重點是更難的事：讓它大規模運作。

McKinsey found that nearly 2/3 of organizations are still stuck in the experimentation or piloting phase.

McKinsey 發現，近三分之二的組織仍停留在實驗或試點階段。

Only about one third have begun to scale AI across their enterprise.

只有大約三分之一的組織開始在整個企業中擴展 AI。

When we look at the ones that are being able to show real value that they've created from it, it's barely 10%.

而那些真正能展現出價值成果的，比例不到一成。

So there is a real gap between initial adoption, experimentation and value creation.

所以，從初步採用、實驗到價值創造之間，確實存在落差。

And what we find is that the companies that are effective in scaling AI and getting value from AI are the ones that don't treat AI as a side project.

我們發現，那些在擴展 AI 並從中獲取價值的公司，並不會將 AI 視為一個額外的專案。

They're the ones that put it in the core of their business.

他們會將 AI 融入其業務的核心。

I once had a professor, Adam Roberts, who used to say that when you were writing an essay, you could tell if someone had taken a theory and just sprinkled it on top like curry powder sprinkled on top as opposed to cooking it through.

我以前有位教授 Adam Roberts，他常說寫論文時，你可以分辨出作者是把理論像咖哩粉一樣灑上去，還是真的把它融入烹調過程。

And I think AI adoption is a bit like that.

You can sort of sprinkle on some AI, add some generative AI images or something, and

你可以稍微灑點 AI，加些生成式 AI 圖片或什麼的，

然後就聲稱自己是家 AI 優先的公司。

I don't think that's really transforming anything.

我認為那並沒有真正轉變任何事情。

What's really seeming to work is when industry, when employers empower their employees to experiment with AI, to approach existing workflows creatively, think, okay, well, what could be accelerated?

真正奏效的是，當產業、當僱主賦予僱員實驗 AI 的權力，讓他們有創意地處理現有的工作流程，思考「嗯，什麼可以加速？」。

This is also changing how investors evaluate companies.

這也改變了投資者評估公司的方式。

Rather than rewarding cost cutting or layoffs, many are now watching how firms bring workers forward into AI driven transformation.

許多人不再獎勵削減成本或裁員，而是觀察公司如何帶領員工走向 AI 驅動的轉型。

Leading companies are the ones that are bringing employees along at every step of the way.

領先的公司是在每一步都讓員工參與進來。

Our research shows that for every $1 you're spending on technology, you have to be spending at least $3 on training, adoption, change management.

我們的研究顯示，你花在科技上的每一塊錢，至少要有三塊錢花在培訓、採用、變革管理上。

There aren't very many jobs that get entirely replaced by AI, but

並不是有很多工作會被 AI 完全取代，但是

almost every single job is going to be significantly affected and changed because of AI.

幾乎每一份工作都會因為 AI 而受到重大影響和改變。

The more that companies involve their employees in the journey and invest in training them and supporting them, the more companies are able to scale AI.

公司讓員工參與的程度越高，並投入培訓和支持他們，公司就越能擴大 AI 的應用。

The dilemma for both companies and for government is that the fastest way a company can transform is by trying to replace employees or replace junior employees.

公司和政府面臨的兩難是，公司轉型最快的方式就是試圖取代員工或取代基層員工。

And I think that's a very short sighted approach.

我認為那是一種非常短視的做法。

I understand the temptation in the short term to cut costs and in particular to cut entry level costs.

我明白短期內為了節省成本，特別是入門級的成本，會有這樣的誘惑。

But many industries are now realizing that if you do that, you might get some short term gains, but

但現在許多產業都意識到，如果這樣做，或許能獲得一些短期利益，

you undermine your long term sustainability.

Because if you don't have entry level people coming in, sure you might only need a smaller number of senior people, but

因為如果沒有入門級的人才加入，你可能確實只需要較少的高階人才，

where does that next generation of senior people come from if you aren't training up the next generation?

但如果沒有培養下一代，下一代的高階人才從何而來？

So the industries that I think will hopefully take advantage of AI and succeed in the longer term are the ones that are willing to see AI not as a short term fix to replace cheap employees, but

所以我認為，那些希望利用 AI 並在長期取得成功的產業，將會是那些願意將 AI 視為長期解決方案，而非短期內取代基層員工的手段的產業，

a longer term solution that will maximize the productivity of all of their employees.

這個方案能最大化所有員工的生產力。