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近日，作為復(fù)旦大學(xué)管理學(xué)院恢復(fù)建院40周年系列活動(dòng)的亮點(diǎn)，2025全球商科教育博覽會(huì)在復(fù)旦大學(xué)管理學(xué)院政立院區(qū)舉行。來(lái)自全球五大洲近50所頂尖商學(xué)院、商科教育國(guó)際組織領(lǐng)導(dǎo)及代表齊聚，共同回顧國(guó)際化交流成果、探討商科教育的創(chuàng)新與未來(lái)。

《第一財(cái)經(jīng)》記者在現(xiàn)場(chǎng)獨(dú)家對(duì)話圣路易斯華盛頓大學(xué)奧林商學(xué)院副院長(zhǎng)、組織行為學(xué)教授、復(fù)旦大學(xué)-華盛頓大學(xué)EMBA項(xiàng)目授課教授Markus Baer，深入探討了AI時(shí)代，我們?yōu)槭裁催€需要商學(xué)教育。

第一財(cái)經(jīng)In this age of abundant online knowledge, what is the unique and irreplaceable value of a world-class business school degree today? In other words, why do we still need formal business education?

在知識(shí)觸手可及的互聯(lián)網(wǎng)時(shí)代，當(dāng)今世界頂級(jí)商學(xué)院學(xué)位不可替代的價(jià)值是什么？我們?yōu)槭裁慈匀恍枰虒W(xué)教育？

Markus BaerThat's a great question. If you look at past technological developments and the actual research, it suggests that adopting technology—such as AI which makes expertise or knowledge highly accessible—does not automatically transit into organizational benefits. Basically, you need a catalyst, a way of extracting the value of any technology. Business education is fundamental to that. Even older studies looked at the effects of management and improvements in management practices often show a profound impact on an organization's ability to capitalize on technology.

So, what I'm saying is thattechnology itself is not always the panacea people want it to be.We see this currently with investments in technology and AI; companies aren't yet reaping the full benefits because, on the management and business education side, we haven't kept up the pace. Many still lack a clear idea of how to best integrate technology into their processes and systems.

So, I think this is highly relevant.In other words, adopting technology has never been enough, and it won't be this time either. Adopting the right processes and cultivating a culture that sustains the competitive advantage technology may provide—that's where I believe we can play an important role.

回顧過(guò)去的技術(shù)發(fā)展和相關(guān)研究，雖然人工智能使專業(yè)知識(shí)變得極易獲取，但僅僅利用技術(shù)并不總能為組織帶來(lái)效益。本質(zhì)上，我們需要一個(gè)催化劑，一種能夠釋放技術(shù)價(jià)值的方法。而商學(xué)教育正是這個(gè)關(guān)鍵。

即使是早期的管理研究也顯示，管理實(shí)踐的改進(jìn)往往能極大地提升組織利用技術(shù)的能力。所以我想強(qiáng)調(diào)的是，技術(shù)本身并非人們想象中的萬(wàn)能解藥。當(dāng)前企業(yè)在人工智能領(lǐng)域的投資尚未帶來(lái)預(yù)期回報(bào)，正是因?yàn)樯虒W(xué)院的教育還沒(méi)有跟上步伐。很多企業(yè)還不清楚如何將技術(shù)有效融入自身的流程和體系。因此，這個(gè)問(wèn)題顯得尤為關(guān)鍵。

歷史告訴我們，僅僅采用技術(shù)從來(lái)不夠，這次也不例外。建立支持技術(shù)優(yōu)勢(shì)的流程和文化，才是維持競(jìng)爭(zhēng)力的關(guān)鍵。在這方面，商學(xué)教育可以發(fā)揮重要作用。

第一財(cái)經(jīng)Compared with twenty or thirty years from now, how do you think business schools are shifting their priorities?

展望未來(lái)二三十年，您認(rèn)為商學(xué)院的教育重點(diǎn)正在發(fā)生怎樣的轉(zhuǎn)變？

Markus BaerWhat I would like to see is that as expertise becomes more widely available and its cost decreases due to AI, we must focus on training certain types of skills.These skills need to essentially transcend technology, meaning they remain relevant regardless of the underlying model that might be used.Take large language models, for instance. You might ask, what are those skills?

First is asking the right question.Currently, you can think about prompt engineering. Even though that becomes less relevant in the future, AI isn't yet at the point to say which questions are more valuable to ask. I study personal problem formulation—understanding an ambiguous situation, finding the right meaning within it, and acting on it. That is something we can teach, we know how to teach, and that skill will remain valuable and relevant.

The second is creativity.With all the world's knowledge at your disposal, you are no longer limited by what you already know. This means the potential combinations of new ideas become endless. Figuring out which of those ideas are better becomes a crucial skill.

For example, if you ask a large language model for business ideas on how to turn ten thousand dollars into a hundred thousand, you have to select the better from the worse ideas. Developing the ability to make that selection is an important skill. So, analytical thinking, problem understanding, creative thinking, and systems thinking—because the world is interdependent, organizations are interdependent. We saw this during the pandemic; it immediately became a worldwide problem.Trade and tariff issues have become worldwide. We are highly interconnected. Thinking about the world as larger systems of interdependent parts will remain, and frankly become, more important. So, business education will remain relevant.

It must shift from teaching specific capabilities and expertise sets, like making you an expert in finance or a certain type of accounting. That type of education will largely fade because it can be automated. But teaching these relevant skills—that's where I hope business schools will focus.

我認(rèn)為，隨著人工智能讓專業(yè)知識(shí)變得更普及、成本更低，商學(xué)院必須轉(zhuǎn)向培養(yǎng)那些超越技術(shù)本身的技能。這些技能應(yīng)該不受底層技術(shù)模型變遷的影響，始終保持價(jià)值。

比如大語(yǔ)言模型時(shí)代，首要技能就是提出正確的問(wèn)題。雖然現(xiàn)在的提示詞工程未來(lái)可能過(guò)時(shí)，但核心在于：人工智能目前還無(wú)法判斷哪些問(wèn)題更有價(jià)值。我在做的研究正是教導(dǎo)人們?nèi)绾卫斫饽：榫?、發(fā)現(xiàn)核心問(wèn)題并采取行動(dòng)——這種能力我們可以教授，而且它會(huì)持續(xù)保值。

其次是創(chuàng)新能力。當(dāng)全世界知識(shí)都能為你所用時(shí)，你不再受限于已知信息，新創(chuàng)意的組合將無(wú)窮無(wú)盡。這時(shí)，辨別最佳創(chuàng)意的能力就變得至關(guān)重要。

舉個(gè)例子，如果你用大語(yǔ)言模型咨詢?nèi)绾斡靡蝗f(wàn)美元賺取十萬(wàn)美元的商業(yè)點(diǎn)子，關(guān)鍵就在于從眾多建議中篩選出最優(yōu)方案。這種選擇能力非常重要，分析思維、問(wèn)題理解、創(chuàng)新思維和系統(tǒng)思考……因?yàn)槭澜缡窍嗷ヂ?lián)系的，組織也是相互依存的。過(guò)往幾年，疫情的全球流行，貿(mào)易關(guān)稅問(wèn)題也變成了全球問(wèn)題。我們必須用系統(tǒng)視角看待這個(gè)相互依存的世界，這種能力將越來(lái)越重要。

商業(yè)教育必須從培養(yǎng)特定專業(yè)能力（比如金融或會(huì)計(jì)）轉(zhuǎn)向教授這些核心技能。前者將逐漸被自動(dòng)化取代，而后者正是商學(xué)院應(yīng)該專注的方向。

第一財(cái)經(jīng)Could you elaborate more on these relevant skills? How can we understand them?

您能否具體說(shuō)明有哪些核心技能？

Markus BaerThe first is asking good questions—figuring out what problem needs to be solved and what that problem actually looks like.The second is finding good answers, which depends on how well you do the first part.Large language models or AI will certainly be helpful here. They're good at analytical thinking. But then comes making good decisions based on the knowledge available to youself. That means making sound choices, ethical choices. Current models are, to some extent, agnostic about right or wrong. They suggest what they believe is the answer most likely relevant to your inquiry.Teaching these skills—analytical thinking, creative thinking, systems thinking, and decision-making—will be important.

首先是提出好問(wèn)題，準(zhǔn)確定義需要解決的問(wèn)題及其本質(zhì)。其次是找到好答案，這很大程度上取決于第一步的質(zhì)量。人工智能擅長(zhǎng)分析思維，但基于可用知識(shí)做出明智決策——包括做出合乎道德的選擇——這至關(guān)重要。目前的AI模型對(duì)是非判斷保持中立，它們只會(huì)提供最相關(guān)的答案。所以教授分析思維、創(chuàng)新思維、系統(tǒng)思維和決策能力將愈發(fā)重要。

第一財(cái)經(jīng)And how do you design a curriculum to ensure your students achieve what they want?

商學(xué)院如何設(shè)計(jì)課程來(lái)確保學(xué)生掌握這些能力？

Markus BaerI think, to some extent,education will become largely individualized. In the future, individuals will probably no longer follow a strictly standardized curriculum.A lot of the expertise, the "what," will be acquired individually. Some will do that more quickly than others, diving deeper depending on their motivation. But the question of "how we think," the theories behind "why the world operates the way it does"—that we need to teach. That, I believe, will largely remain the role of universities. So, the expertise side ("what") will be individualized, likely not happening necessarily in the classroom. Maybe it happens with support of faculty.

Classroom dialogue will mostly focus on nurturing those higher-order skills, which are essentially timeless.In terms of specific exercises, we might not be there yet, but the curriculum will shift dramatically. It will be more individualized, and in content, it will shift from "what people know" to "applying that knowledge"—understanding why things happen and developing the skills to be good at.It will focus more on the application of existing knowledge rather than its initial acquisition, which is currently a big part of what we do.In a introductory course on a particular topic, you mostly learn the substance, not so much the applications, limitations, or how it fits into the broader picture. I think that will be the change.

未來(lái)教育將更加個(gè)性化。專業(yè)知識(shí)的學(xué)習(xí)將主要由學(xué)生自主完成，課堂重點(diǎn)將轉(zhuǎn)向培養(yǎng)思維方式，探討世界運(yùn)行的內(nèi)在邏輯。這應(yīng)該是大學(xué)的核心價(jià)值。

可以想象，知識(shí)獲取將更加個(gè)性化，不一定在教室完成。而課堂討論將聚焦如何培養(yǎng)這些維持競(jìng)爭(zhēng)力的核心能力。課程內(nèi)容將從“知識(shí)傳授”轉(zhuǎn)向“知識(shí)應(yīng)用”，重點(diǎn)理解現(xiàn)象背后的原理，培養(yǎng)將知識(shí)融會(huì)貫通的能力。現(xiàn)在的入門課程大多側(cè)重知識(shí)本身，未來(lái)將更注重應(yīng)用場(chǎng)景、局限性及其在更大系統(tǒng)中的作用。

第一財(cái)經(jīng)We talk a lot about how AI is changing business schools. In the specific process you mentioned, how do you think AI itself is transforming business education?

您認(rèn)為AI將如何改變商業(yè)教育模式？

Markus BaerWell, it hasn't yet. Universities have been around for a thousand years and are slow to adapt—which is sometimes good. But I imagine that in the short term, most curricula will incorporate AI portions.As models become more capable, we can automate certain types of learning for individuals, freeing up classroom time for higher-value activities than before. I think that will happen pretty quickly.

So, number one:students will need to understand what AI is, how models operate their basic premises, and develop the intelligence to work with it in a meaningful way—which isn't always intuitive.People often tend to take the easier route, which isn't always helpful for learning and problem-solving. So, I imagine in the short term, we'll see AI literacy, what I'd call "hybrid intelligence"—learning how to get the best out of technology, using the best of human capabilities complemented by AI.Over time, you'll see more individualized learning for knowledge acquisition, and the classroom experience itself will focus much more on application, understanding why things happen—the theoretical understanding—and how things fit together in the larger picture, what I call systems thinking.

Because if you think about the general business curriculum, it's often quite compartmentalized. You take courses in finance, accounting, etc. But what really matters in organizations is how these pieces come together. Most people learn that when they start their business; entrepreneurs have to apply all these different elements. I think more of that integration can and should happen in the classroom.

大學(xué)有著千年傳統(tǒng)，緩慢的變革有時(shí)反而是好的。但我預(yù)計(jì)短期內(nèi)，大多數(shù)課程都會(huì)加入AI內(nèi)容。隨著模型能力提升，知識(shí)性學(xué)習(xí)將更多由AI輔助完成，從而解放課堂時(shí)間用于更高價(jià)值的活動(dòng)。

首先，學(xué)生需要理解AI的基本原理、運(yùn)作方式，學(xué)會(huì)與之有效協(xié)作。人們?nèi)菀走x擇捷徑，但這不利于深度學(xué)習(xí)和問(wèn)題解決。所以短期內(nèi)，“AI素養(yǎng)”和“人機(jī)協(xié)同”能力將成重點(diǎn)，即最大化利用技術(shù)增強(qiáng)人類智能。

長(zhǎng)期來(lái)看，專業(yè)知識(shí)獲取將更加個(gè)性化，課堂則將聚焦知識(shí)應(yīng)用、理解系統(tǒng)運(yùn)作機(jī)制，培養(yǎng)我所說(shuō)的“系統(tǒng)思維”能力。傳統(tǒng)商學(xué)院課程往往分科過(guò)細(xì)，但企業(yè)運(yùn)營(yíng)真正需要的是各領(lǐng)域知識(shí)的整合能力。創(chuàng)業(yè)者通常是在實(shí)踐中學(xué)會(huì)這點(diǎn)。未來(lái)，更多的跨學(xué)科整合應(yīng)該發(fā)生在課堂上。

第一財(cái)經(jīng)If you had to name just one classic management principle that hasn't been impacted by AI or remains critically important today, what would it be?

如果要您舉一個(gè)不受AI影響且始終重要的管理原則，會(huì)是什么？

Markus BaerFor instance, even if you replace certain team members with AI,the basic premise of designing a team—a collection of individuals who coordinate activities and move in the same direction—remains crucial.I believe these principles of team design and coordination will remain relevant as long as people work together. The problems we're trying to solve are becoming more complex, requiring at least some individuals (augmented by AI or not) to solve them. This means knowing how to inspire people so they care about what they're doing, find meaning in it, invest themselves in it, and that you coordinate activities effectively to tackle challenges. This will remain relatively timeless, perhaps until we have fully agentic AI that can solve issues automatically without human input. But we're not there yet.So, principles of team design, leading teams—whether fully human or AI-augmented—will remain valid.

即使團(tuán)隊(duì)中部分工作由AI完成，但只要人類還需要協(xié)作，團(tuán)隊(duì)設(shè)計(jì)的基本原則（讓成員協(xié)調(diào)一致、朝著共同目標(biāo)前進(jìn)）就依然有效。

我們面對(duì)的問(wèn)題日益復(fù)雜，至少不同人的共同協(xié)作才能解決。這意味著要懂得激勵(lì)團(tuán)隊(duì)成員，讓他們?nèi)耐度?、找到其中的意義，并有效協(xié)作。這些原則在完全自主的AI出現(xiàn)前將始終重要。所以，團(tuán)隊(duì)設(shè)計(jì)和領(lǐng)導(dǎo)力原則——無(wú)論是純?nèi)祟悎F(tuán)隊(duì)還是人機(jī)協(xié)同團(tuán)隊(duì)——都將持續(xù)有效。

第一財(cái)經(jīng)I'm asking this because we discuss AI ethics, digital transformation, and analytics these days. How can educators strike the right balance between tradition and innovation?

在教育領(lǐng)域，我們?cè)撊绾纹胶鈧鹘y(tǒng)與創(chuàng)新？

Markus BaerI think we are entering a former university prospective. We have been pretty traditional in how we make curricular changes; the structure and delivery have remained pretty consistent since I joined my university, for instance.So, I believe we're entering a phase of innovation. I think of it as a pendulum swinging from one side to the other. The pendulum has been much more on the traditional side—which isn't always bad, depending on the change.But now, I think it's swinging towards innovation. Universities that close themselves off from this will be at a disadvantage. I don't believe we will all become obsolete in the short term because most people use AI as a tool for certain tasks, not for entire jobs. Universities must reflect that reality. We will use AI for certain types of tasks, like knowledge acquisition done more independently, while other tasks will retain a more traditional classroom experience.But from a university perspective, the pendulum must swing towards innovation; we have to become more open and ready to change.That is my belief.

大學(xué)正進(jìn)入創(chuàng)新階段。我將其比作鐘擺：過(guò)去更偏向傳統(tǒng)，這未必是壞事。但現(xiàn)在鐘擺正擺向創(chuàng)新?？咕苓@一趨勢(shì)的大學(xué)將處于劣勢(shì)。

不過(guò)，我不認(rèn)為大學(xué)的教育會(huì)過(guò)時(shí)，因?yàn)槟壳癆I主要輔助特定任務(wù)而非取代全部工作。大學(xué)應(yīng)該反映這一現(xiàn)實(shí)：某些任務(wù)可借助AI，而其他任務(wù)仍需傳統(tǒng)課堂體驗(yàn)。但大學(xué)整體必須更加開放，積極擁抱變革。

第一財(cái)經(jīng)That's quite interesting. These days, everyone talks about innovation and new technologies, but you mentioned that schools and universities remain quite traditional. But is it a tradition to be traditional?

這很有趣。大家都在談創(chuàng)新，但大學(xué)卻保持傳統(tǒng)。這是否本身也成了一種傳統(tǒng)？

Markus BaerIt is. I think it's a good question. Partly, it's the tradition of universities.But the reason I say we must change is that we've never had a technology like AI before. It's unlike anything in the past because it doesn't just touch one particular area.

Past inventions like the calculator or the computer initially affected certain areas.AI is impacting all business functions and has a global impact.So, while tradition may have served us well through the innovations of the twentieth century, the advent of AI is, in my humble opinion, profound enough that we must abandon that old model and re-envision what a university does. I think this is inevitable because students will want to know if they can be employed after spending four years with us in the classroom. We need a good answer for that. I'm not saying everything we do will become obsolete, but I do think we must adapt to this new reality.

確實(shí)如此。這一部分源于大學(xué)傳統(tǒng)。但我強(qiáng)調(diào)大學(xué)必須變革，因?yàn)锳I技術(shù)與過(guò)去任何技術(shù)都不同，它不只影響某個(gè)領(lǐng)域，而是波及所有領(lǐng)域并產(chǎn)生全球影響。

在20世紀(jì)的歷次突破性創(chuàng)新中，傳統(tǒng)模式尚能應(yīng)對(duì)。但AI卻不一樣，它的影響如此深遠(yuǎn)，我們必須重新構(gòu)想大學(xué)在這個(gè)時(shí)代的功能。因?yàn)檫@事關(guān)學(xué)生經(jīng)過(guò)四年學(xué)習(xí)后能否就業(yè)，是否還具有競(jìng)爭(zhēng)力。但我不是說(shuō)，大學(xué)現(xiàn)有的一切都會(huì)過(guò)時(shí)，但我們必須適應(yīng)新現(xiàn)實(shí)。

第一財(cái)經(jīng)From a university's perspective, could you elaborate a bit more on the risks and benefits of staying traditional, especially in these days of great uncertainties going around?

在當(dāng)前不確定性環(huán)境中，保持傳統(tǒng)具體有哪些風(fēng)險(xiǎn)和收益？

Markus BaerI think we must figure out what skills we need to focus on. It would be counterproductive to change just for the sake of change.

You have to figure out what skills will be relevant for the next five to twenty years and adapt the curriculum to that.However, certain elements of any curriculum, like how to make decisions or the ethical issues you raised earlier, will remain relevant.Leadership—inspiring people—will become more important as some jobs are lost and people face pressure from AI advancements. I believe management, and especially leadership, will remain or become even more important, and business schools already teach that.The risk lies in being reactive or making changes without thorough consideration—changing because you feel you must but not knowing where to invest your resources. If universities develop a clear agenda focusing on the skills that will remain relevant, I believe, that is going to be the winning recipe.

關(guān)鍵是要明確未來(lái)五到二十年哪些技能仍然重要。為改變而改變只會(huì)適得其反。需要識(shí)別持續(xù)有價(jià)值的技能并相應(yīng)調(diào)整課程。但課程中某些要素不會(huì)過(guò)時(shí)，比如決策能力、倫理問(wèn)題等等。領(lǐng)導(dǎo)力、激勵(lì)人心的能力，隨著AI帶來(lái)的就業(yè)壓力，將變得更加重要。改變的風(fēng)險(xiǎn)在于未經(jīng)深思就倉(cāng)促變革。如果大學(xué)能明確哪些技能持續(xù)重要并專注于此，這將是成功之道。

第一財(cái)經(jīng)My last question: although schools try to stay as traditional as possible, we see more and more company decisions being made with algorithms and AI. So, how should leaders use advanced technologies like generative AI not to be replaced, but to amplify and refine their existing business activities?

最后一個(gè)問(wèn)題：企業(yè)決策越來(lái)越依賴算法和AI，領(lǐng)導(dǎo)者該如何運(yùn)用生成式AI等技術(shù)來(lái)增強(qiáng)而非取代自身業(yè)務(wù)？

Markus BaerThat's a great question, and it's precisely what universities can teach. Early research shows that if you outsource decision-making to AI without interrogating it, without understanding what decision is being made and how, and without first forming your own independent assessment—the results aren't productive. For instance, a notable study showed that physicians who received AI advice early on and typically didn't interrogate it—either blindly following or ignoring it—didn't perform better; in fact, they did worse than physicians who made up their minds first. Having a hunch or an initial decision, then using AI to confirm, interrogate, and examine it, proves much more productive. So,designing these processes—figuring out how AI can augment human intelligence—is something we can teach.

這正是大學(xué)可以教學(xué)的領(lǐng)域。早期研究顯示，如果將決策完全外包給AI而不加審視、不理解其決策邏輯，也不先形成獨(dú)立判斷，效果往往不佳。

比如有研究顯示，過(guò)早獲得AI建議卻不去深入思考的醫(yī)生，其表現(xiàn)反而不如先獨(dú)立判斷再借助AI確認(rèn)的同行。先有初步判斷，再用AI驗(yàn)證、質(zhì)疑和完善，這種方式效率更高。所以，設(shè)計(jì)人機(jī)協(xié)同的工作流程，正是我們?cè)谡n程中應(yīng)該教學(xué)的內(nèi)容。

第一財(cái)經(jīng)Which means we need to take an active part, even with AI assistance?

也就是說(shuō)，即使有AI輔助，我們?nèi)孕璞３种鲃?dòng)參與。

Markus BaerYes, because the best way to use it isn't always obvious.Automating tasks is great, as you said, but it might reduce our understanding of what we're doing and why. It may also erode our decision-making abilities and deprive us of our agency and independent judgment, which is highly valuable for domain experts.I know AI can be trained on larger amounts of data and might be more accurate, but the combination seems to be the sweet spot, at least at this point. The key question is, how do you create that combination? How do you integrate both human and artificial intelligence in the most productive way? That is something universities will be well-positioned to teach.

沒(méi)錯(cuò)。自動(dòng)化固然好，但可能讓我們失去對(duì)工作本質(zhì)的理解，削弱決策能力，剝奪專業(yè)判斷力——而這正是專家的價(jià)值所在。AI或許能處理更多數(shù)據(jù)、更精確，但目前階段，人機(jī)協(xié)同才是最佳模式。如何實(shí)現(xiàn)最優(yōu)組合，正是大學(xué)擅長(zhǎng)的領(lǐng)域。

第一財(cái)經(jīng)Thank you, Professor, for answering these challenging questions about the future. I really appreciate it.

感謝您！為我們解讀這些關(guān)乎未來(lái)的重要問(wèn)題。

Markus BaerThank you!

多謝！