MDDI 演講稿 · 2026-01-14

劉佩芬政務次長在新加坡國立大學 AI 與數碼轉型高管碩士課程啟動儀式上的開幕致辭

Jasmin Lau · MDDI 政務次長 · 新加坡國立大學 AI 與數碼轉型高管碩士課程啟動儀式

要點

  • AI 落地不是技術練習,而是領導力練習。Jasmin 列出 3 個領導力挑戰:①深度理解組織、知道 AI 在哪裡能創造真實價值(衛生部護士登記 100 步流程的例子——先「歸零」再數字化);②評估能力、決定 build/borrow/buy 的混合配比;③理解 AI 對人的影響,知道「即便能用也不該用」的時刻。
  • 新加坡部長們也在去年底接受了兩輪 AI 與數字產品開發培訓——四個月後大概又要再來一輪。
  • 對工程師角色的描述:編碼助手讓工程師把時間從「寫程式碼」轉向「指揮模型、驗證輸出、做更高階決策」——結果是腦力負擔更重,因為重複任務被剝離、留下批判性思考。
  • 「無悔之舉」(no-regrets move)是為自己與組織投資學習與再學習——理解哪些工具對自己角色重要,如何指導落地,如何對輸出做判斷。

完整譯文(繁體中文)

MDDI 英文原文譯文 · 翻譯日期: 2026-05-02

各位早安。

「AI」與「數字轉型」我們聽得太多了。我相信在座許多人已經領會到它們的重要性——所以我不會再花時間說服各位。

我想談三個領導力挑戰——這些挑戰在我與「在 AI 時代領導組織」的同行交談時反覆出現。

把 AI 用得好,不是技術練習——這要求領導者付出實在的個人投入去推動組織變革,並以清晰、勇氣和穩健的判斷力去做這件事。

我們最常遇到的第一個挑戰是——領導者要足夠深入地理解自己的組織,才能知道 AI 在哪裡能創造「真實價值」,而不只是「增量效率」。

今天許多領導者已經鼓勵員工用 AI 來改進既有流程、或者提升生產力——比如更快整理報告、加速重複任務。這些努力確實提升了生產力——但 AI 的潛能遠不止於此。

組織從 AI 或數字轉型中提取不到更多價值的一個原因是——領導者也許不夠了解組織里詳細的工作流與流程。許多領導者花了多年精力在「升職階梯、利益相關方互動、公關」上——現在我們必須回到組織里,理解這些流程與工作流多年來是怎麼演化的。

我用我在衛生部的一段經歷舉個例子。2023 年走出疫情時,我們面臨護士短缺,但搞不清原因。深入挖下去之後我們發現——我們的護士註冊流程居然有 100 多個步驟!我們看著整個流程說:「這要做得更快」——而要點不是把 100 步「數字化」,而是先想清楚——「這 100 步是不是都需要?」我相信你們在各自的組織里也會面臨類似的挑戰。

起點是回到基本面,理解流程是怎樣演化過來的——作為領導者,要有信念與勇氣說一句「讓我們從零開始」。

貫穿這門課程的過程中,是個好時機問自己:你是否真正理解組織里最迫切的問題——以及哪些是數字轉型或 AI 能從根本上改變結果的領域?你準備好說「讓我們從零開始」了嗎?

領導者面臨的第二個挑戰是——一旦你搞清楚問題之後,要評估組織的當前能力,決定要「內建」(build)、「借用」(borrow)還是「購買」(buy)這些能力。

我自己也花了一些時間學 AI 的基礎。我也很幸運,之前在公共部門工作時參與過幾次 IT 系統升級。但這是枯燥而辛苦的工作。技術發展的節奏,又比我們學習的能力快得多。

沒有一種方案對所有組織都適用。如果組織決定依賴外部供應商——你就要承擔一些「員工去技能化」的風險,可能流失多年來熟悉這些流程的資深員工,喪失監督 AI 系統所需的領域專長。如果你嘗試把所有能力都放在內部——轉型節奏可能比你期望的更慢,變革的慣性會非常高。

這些都是領導者必須穿行其中的真實張力——決定變革的節奏、採用的深度,並與團隊就「新的現實」進行坦誠的對話。

我們多數人最終走的是「混合方案」——引入一些外部能力,同時說服團隊向更懂的人學習。我希望各位自己探索——你給自家組織的「混合配比」是什麼?

但你必須支援自己的人走過這次轉型。你必須看到——他們會焦慮——你必須為他們的能力投資,而不是奔向「空洞的效率」。

第三個挑戰——也許是最重要的——是理解 AI 與數字轉型對「人」的影響,並知道「即便能用,何時不該用 AI」。

並不是組織里的每一個決策、每一道流程或工作流,都該純粹為了速度、規模或成本來最佳化。

作為領導者,你必須問自己:

這次 AI 的使用,會不會侵蝕「信任」?

它是否在共情與情感重要的場合,削弱了人的判斷?

它是否讓我們這些領導者遠離了「問責」?

時不時問問自己——什麼時候應該把 AI 擋在自家組織門外?因為人比效率更重要。

這就是「領導力」最關鍵的地方。AI 不自帶道德羅盤,但你們都自帶。我鼓勵大家在這門課程中思考自己的羅盤——並讓它在課程之後引導你的領導決策。

對領導者而言,一個明確的「無悔之舉」是——為自己與組織投資學習與再學習。我們都需要學如何與 AI 共事——理解哪些工具對自身角色最關鍵、如何有效引導工具的落地、如何對其輸出做合理判斷。

我用軟體工程舉個例子——伴隨編碼助手的興起,今天許多工程師(包括 GovTech 的工程師)花在「實際寫程式碼」上的時間在減少,更多時間用於指揮 AI 模型、驗證輸出、做更高階決策。

許多工程師告訴我——一開始他們曾擔心工作會不會被取代、在校所學是否還相關。但現在他們的工作其實更費腦力——因為重心從「常規重複」轉移到了「批判性思考」。

這種模式很可能會在許多職業與行業裡復現。

因此——像我們這樣的領導者必須保持「上手參與」、培育終身學習的心態、深入思考 AI 如何重新塑造我們的工作與責任。

新加坡的部長們去年也接受了 AI 與數字產品開發的培訓——去年底我們做了兩次。坦白說,再過四個月,我們大概又要再來一輪!

我很高興出席新加坡國立大學(NUS)「AI 與數字轉型高管碩士課程」的啟動儀式。致首屆班的所有同學——祝賀你們邁出這一步。在工作、生活與學業之間取得平衡,是對「在變化的世界中擔當領導」的嚴肅承諾;這同時也是一種幸運——我們當中許多人都希望自己有同樣的時間與資源,在如此結構化的環境中、與世界各行各業的同學一起學習。希望各位充分利用這次機會。

我也希望這門課程不僅裝備你們以技術技能與知識,更裝備你們以判斷力、信心與倫理上的清晰——好把轉型領導得漂亮。

感謝新加坡國立大學開啟這一重要倡議——祝各位前路真正具有「轉型意義」。

謝謝。

英文原文

MDDI 官網原始記錄 · 抓取日期: 2026-05-02

Good morning, everyone.

We hear the words “AI” and “digital transformation” all the time. I’m sure many of you already appreciate their importance, so I will not spend more time convincing you of that.

Instead, let me share three leadership challenges that repeatedly surface in my conversations with leaders who are leading their organisations through this AI age.

Using AI well is not a technical exercise. It requires significant personal commitment from leaders to drive organisational change. And to do so with clarity, courage, and sound judgment.

The first challenge we often encounter is for leaders to understand your organisations deeply enough to know where AI can create real value, not just incremental efficiency.

Today, many leaders already encourage their staff to use AI to improve existing processes or to become more productive, in organising reports or speeding up routine tasks. These efforts do improve productivity, but AI has the potential to do so much more.

One reason why organisations cannot seem to extract more value from AI or digital transformation is because leaders may not know well, the detailed workflows and processes being used within the whole organisation. Leaders may have spent years raising the ranks, stakeholder engagement, public relations etc, and we now need to go back into our organisations to understand how processes and workflows have evolved over time.

Let me give you a good example of the experience I had in the Ministry of Health. Coming out of COVID in 2023, we had a nursing shortage, but we couldn't figure out why. When we started digging into it, we found that our nursing registration process had over 100 steps in it. We looked at the whole process and said, we think we need to do this faster, and it was not about digitalising the 100 steps. It was to first think about if you need all of these steps. I think this is the kind of challenge you will face in your respective organizations.

It starts with going back to the basics, understanding how processes have evolved over time, and as a leader, having the conviction and the courage to say, let's start from zero.

As you go through this programme, it is a good time to ask yourselves: Do you really understand your organisation’s most pressing problems, and the areas where digital transformation or AI can fundamentally change outcomes? Are you ready to say, let's start from zero?

The second challenge leaders often face, once you figure out what the problems are, is assessing your organisation’s current capabilities to carry out the transformation, and then deciding whether you want to build in-house, borrow new capabilities or buy these capabilities.

It took me some time to learn basics of AI. I was also fortunate to have had to work through some IT system enhancements during my time in the public service. But this is dry and hard work. And the pace of technology development is much faster than our ability to learn.

There is no one size fits all solution for all organisations. If your organisation decides to rely on external vendors, you will risk some deskilling of your workforce, lose experienced staff who worked on the processes over the years, and lose domain expertise needed to supervise your AI systems. If you try to build all your capabilities in-house, the pace of transformation may be slower than you want, and the inertia to change will be very high.

These are real tensions that leaders must navigate. Deciding the pace of change, the depth of adoption, and having honest conversations with your teams about new realities.

Now, most of us end up on a hybrid – we bring in some external capabilities, and then we convince our teams to learn from those who know better. I would like you to explore on your own, what's the mix that you bring to your organisation?

But you will have to support your people through the transformation. You have to recognise that they will be anxious, and you have to invest in their capabilities, rather than hollow efficiency.

The third challenge, and perhaps the most important one, is understanding the impact of AI and digital transformation on people, and knowing when not to use AI, even when you can.

Not every decision, process or workflow in your organisation should be optimised purely for speed, scale, or cost.

As leaders, you will have to ask yourselves:

Does this use of AI erode trust?

Does it reduce human judgment where empathy and feelings matter?

Does it distance us as leaders from accountability?

Now and then, you ask yourself, when do you keep AI out of your organisation? Because the humans matter more.

This is where leadership matters most. AI does not come with a built-in moral compass, but all of you do. I encourage all of you to think about your own compass as you go through this course, and let it guide you through your leadership decisions after the course.

One clear “no-regrets” move for leaders is to invest in learning and relearning, for yourselves and for your organisations. We all need to learn how to work with AI: to understand which tools matter for our roles, how to guide the implementation of the tools effectively, and how to exercise sound judgment over their outputs.

Let me give you an example on software engineering. Nowadays, with the rise of coding assistants, many of our engineers, including those in GovTech, spend less time on actual coding tasks and more time directing AI models, validating outputs, and making higher-order decisions.

Many engineers tell me that at the start, they were wondering whether their job will be replaced and whether what they learned in school is no longer relevant. But now their current work is a lot more mentally demanding, because the focus has shifted to critical thinking, rather than routine and repetitive tasks.

This pattern will likely repeat across many professions and sectors.

That is why leaders like all of us must stay hands-on, cultivate a lifelong learning mindset, and think deeply about how AI reshapes our work and responsibility.

Even our ministers in Singapore have undergone training in AI and digital product development. We had our two training sessions towards the end of last year, and to be honest, I think in four months’ time, we probably need another round of these training sessions!

I am happy to be here with you today at the launch of NUS’s Executive Master in AI and Digital Transformation. To all of you in the inaugural cohort, congratulations on taking this step. Balancing your work, life, and studies is a serious commitment to leadership in a changing world. But it's also a blessing, as there are many of us here who wish that we had the same time and the resources to spend on learning in a very structured environment and with students from different industries all over the world. I hope that all of you make the most of it.

I also hope that this programme equips you not just with technical skills and knowledge, but with the judgment, confidence, and ethical clarity to lead transformation well.

I thank NUS for this important initiative, and I wish you all a truly transformative journey ahead.

Thank you.