Recently, the leadership team of an American supermarket chain decided that their business needed to get a lot more efficient. So they embraced their digital transformation with zeal. Out went the teams supervising meat, veg, bakery, and in came an algorithmic task allocator. Now, instead of people working together, each employee went, clocked in, got assigned a task, did it, came back for more. This was scientific management on steroids, standardizing and allocating work. It was super efficient.
最近,美國某連鎖超市的領導團隊 決定要讓他們的企業更有效率。 所以他們火熱擁抱數位化轉型。 團隊出去監看肉品、 蔬果及麵包部門, 回來的時候想出了一套 任務分配演算法。 現在,員工不再一同工作, 而是每個員工自己去打卡, 拿分配的任務,做完, 然後再去領更多的任務。 這是科學管理大補丸, 標準化及分配每個工作。 超有效率。
Well, not quite, because the task allocator didn't know when a customer was going to drop a box of eggs, couldn't predict when some crazy kid was going to knock over a display, or when the local high school decided that everybody needed to bring in coconuts the next day.
才怪。 因為任務分配法不知道 什麼時候顧客會打破一盒蛋, 不能預測什麼時候哪來的小鬼 會打翻陳列的商品, 或什麼時候當地某高中會突然決定 學生隔天通通要帶椰子去學校。
(Laughter)
(笑聲)
Efficiency works really well when you can predict exactly what you're going to need. But when the anomalous or unexpected comes along -- kids, customers, coconuts -- well, then efficiency is no longer your friend.
效率要在你能準確預估 你到底需要什麼的時候才會有用。 如果有任何異常 或無預警的情況發生── 小孩、顧客、椰子── 那效率就不再是你的朋友。
This has become a really crucial issue, this ability to deal with the unexpected, because the unexpected is becoming the norm. It's why experts and forecasters are reluctant to predict anything more than 400 days out. Why? Because over the last 20 or 30 years, much of the world has gone from being complicated to being complex -- which means that yes, there are patterns, but they don't repeat themselves regularly. It means that very small changes can make a disproportionate impact. And it means that expertise won't always suffice, because the system just keeps changing too fast.
這件事已經變成非常重要的問題, 就是處理意外的能力, 因為意外變得愈來愈正常。 這就是為什麼專家及預測人員 很不願意預測四百天後的情況。 為什麼? 因為在過去二三十年, 世界上的大部分地區從複雜 轉變到複合式的複雜。 也就是說是的,的確有模式存在, 但是這些模式並不經常重複。 也就是說非常小的變化 就會造成不成比例的衝擊, 這也代表著專業知識 不見得永遠夠用, 因為系統總是變化太快。
So what that means is that there's a huge amount in the world that kind of defies forecasting now. It's why the Bank of England will say yes, there will be another crash, but we don't know why or when. We know that climate change is real, but we can't predict where forest fires will break out, and we don't know which factories are going to flood. It's why companies are blindsided when plastic straws and bags and bottled water go from staples to rejects overnight, and baffled when a change in social mores turns stars into pariahs and colleagues into outcasts: ineradicable uncertainty. In an environment that defies so much forecasting, efficiency won't just not help us, it specifically undermines and erodes our capacity to adapt and respond.
這就意味著 這個世界有很大一片地方, 現在可以說不符合預測。 這就是為什麼英國央行會說, 是,的確會有下一波的崩盤, 但是我們不知道為什麼 或什麼時候發生。 我們知道氣候變遷是真的, 但是我們無法預測 森林大火會在哪裡發生, 我們也不知道哪個工廠會淹水。 這也就是為什麼 製造公司會完全看不到 塑膠吸管、塑膠袋和瓶裝水 會在一夕之間從生活必需品 變成人人喊打, 也不懂什麼時候社會民德改變, 讓巨星隕落成賤民,同僚被放逐; 根深蒂固的不確定性。 在一個經常無可預測的環境內, 效率不但不能幫助我們, 還特別會破壞並磨掉 我們適應及反應的能力。
So if efficiency is no longer our guiding principle, how should we address the future? What kind of thinking is really going to help us? What sort of talents must we be sure to defend? I think that, where in the past we used to think a lot about just in time management, now we have to start thinking about just in case, preparing for events that are generally certain but specifically remain ambiguous.
如果效率不再是我們的 最高指導原則, 那我們未來要怎麼做呢? 什麼樣的思維才能真正幫助我們? 什麼樣的人才是我們一定要保住的? 我想,過去我們 只著重「及時」管理, 現在我們必須開始思考「萬一」, 準備應付還算有把握, 但某些項目仍不能完全掌控的情況。
One example of this is the Coalition for Epidemic Preparedness, CEPI. We know there will be more epidemics in future, but we don't know where or when or what. So we can't plan. But we can prepare. So CEPI's developing multiple vaccines for multiple diseases, knowing that they can't predict which vaccines are going to work or which diseases will break out. So some of those vaccines will never be used. That's inefficient. But it's robust, because it provides more options, and it means that we don't depend on a single technological solution. Epidemic responsiveness also depends hugely on people who know and trust each other. But those relationships take time to develop, time that is always in short supply when an epidemic breaks out. So CEPI is developing relationships, friendships, alliances now knowing that some of those may never be used. That's inefficient, a waste of time, perhaps, but it's robust.
流行病預防聯盟 CEPI 就是這方面的例子。 我們知道未來會有更多流行病, 但是我們不知道何時、 何地或何種會發生。 所以我們不能事先計畫。 但是我們可以事先準備。 所以流行病預防聯盟 為多種疾病開發出多種疫苗, 因為他們知道他們無法預測 哪種疫苗會有效, 或哪種疾病會爆發, 所以某些開發出的疫苗 就會永遠用不到。 這很沒效率。 卻很萬全, 因為這提供了更多選擇, 也意味著我們不需要依賴 單一技術解決方案。 流行病應變很大程度也取決於 相互了解和信任的人。 但是這種關係需要時間來培養, 流行病爆發時,時間永遠不夠用。 所以流行病預防聯盟 現在就培養關係、友誼、結盟, 即使知道這些關係 可能永遠都用不上。 這很沒效率,也可以說是浪費時間, 但這很萬全。
You can see robust thinking in financial services, too. In the past, banks used to hold much less capital than they're required to today, because holding so little capital, being too efficient with it, is what made the banks so fragile in the first place. Now, holding more capital looks and is inefficient. But it's robust, because it protects the financial system against surprises.
萬全的想法在金融服務業也看的到。 過去,銀行的資本要求 比現在少很多, 因為持有那麼少的資金 雖然效率超高, 卻讓銀行一開始就非常脆弱。 現在,資本要求變高乍看 ──也的確──很沒效率, 但是很萬全,
Countries that are really serious about climate change
因為這樣能保護金融系統免受驚嚇。
know that they have to adopt multiple solutions, multiple forms of renewable energy, not just one. The countries that are most advanced have been working for years now, changing their water and food supply and healthcare systems, because they recognize that by the time they have certain prediction, that information may very well come too late.
極為看重氣候變遷的國家 知道他們必須多管齊下, 採用多種可再生能源, 而非只有一種。 進展好的國家已經實行了很多年, 改變他們的水源 及食物來源及健保系統, 因為他們意識到就算他們預測到了, 到那個時候資訊 也可能已經來得太晚。
You can take the same approach to trade wars, and many countries do. Instead of depending on a single huge trading partner, they try to be everybody's friends, because they know they can't predict which markets might suddenly become unstable. It's time-consuming and expensive, negotiating all these deals, but it's robust because it makes their whole economy better defended against shocks. It's particularly a strategy adopted by small countries that know they'll never have the market muscle to call the shots, so it's just better to have too many friends. But if you're stuck in one of these organizations that's still kind of captured by the efficiency myth, how do you start to change it? Try some experiments.
同樣的方法也可以用在貿易戰上, 許多國家都這樣做。 與其倚靠一個超強貿易夥伴, 還不如試著跟大家都交朋友, 因為他們知道他們不能預測 哪個市場可能會突然崩盤。 這很花時間也很花金錢, 要協商各式交易, 但這很萬全, 因為這讓他們的整個經濟 更能防禦衝擊。 這種策略特別受小國青睞, 因為他們知道他們的市場 永遠不能做主, 所以最好擁有非常多朋友。 但是如果你待的組織, 還卡在效率這樣的迷思裡, 你要怎麼開始做出改變? 嘗試一些實驗。
In the Netherlands, home care nursing used to be run pretty much like the supermarket: standardized and prescribed work to the minute: nine minutes on Monday, seven minutes on Wednesday, eight minutes on Friday. The nurses hated it. So one of them, Jos de Blok, proposed an experiment. Since every patient is different, and we don't quite know exactly what they'll need, why don't we just leave it to the nurses to decide?
在荷蘭, 以前居家護理師的調配 還用很像超市的做法: 標準化及指定化的工作—— 以分鐘計: 星期一做九分鐘,星期三做七分鐘, 星期五做八分鐘。 護理師恨死了! 所以護理師勃洛克 就提出一個實驗。 既然每個病人的情況都不一樣, 我們根本無從得知他們到底要什麼, 那我們為什麼不讓護士自己判斷呢?
Sound reckless?
聽起來有欠考慮喔?
(Laughter)
(笑聲)
(Applause)
(掌聲)
In his experiment, Jos found the patients got better in half the time, and costs fell by 30 percent. When I asked Jos what had surprised him about his experiment, he just kind of laughed and he said, "Well, I had no idea it could be so easy to find such a huge improvement, because this isn't the kind of thing you can know or predict sitting at a desk or staring at a computer screen." So now this form of nursing has proliferated across the Netherlands and around the world. But in every new country it still starts with experiments, because each place is slightly and unpredictably different.
勃洛克在他的實驗中發現 病人只要原本一半的時間就好轉, 花費下降了 30% 。 我問勃洛克他的實驗裡 哪個部分最讓他驚訝, 他只笑了一下說: 「嗯,我真的不知道這麼簡單 就能有這麼大的改進, 因為這不是你坐在書桌前 或盯著電腦螢幕 就能知道或預測的。」 所以現在這樣的照護系統 已經通行荷蘭及全世界。 但是在每個新國家 都還是要以實驗開始, 因為每個地方都不太一樣, 這種不同也很難預測。
Of course, not all experiments work. Jos tried a similar approach to the fire service and found it didn't work because the service is just too centralized. Failed experiments look inefficient, but they're often the only way you can figure out how the real world works. So now he's trying teachers. Experiments like that require creativity and not a little bravery.
當然,不是每個實驗都會成功。 勃洛克把類似的方法用在消防隊上, 發現沒有用, 因為消防隊實在非常集中派工。 失敗的實驗看起來很沒效率, 但這經常是你能找出實際情況 如何運作的唯一的方法。 所以現在他去試試看老師這行。 像這樣的實驗需要創造力 和不只一點點的勇氣。
In England -- I was about to say in the UK, but in England --
在英格蘭, 我本來要說英國,但是在英格蘭,
(Laughter)
(笑聲)
(Applause)
(掌聲)
In England, the leading rugby team, or one of the leading rugby teams, is Saracens. The manager and the coach there realized that all the physical training they do and the data-driven conditioning that they do has become generic; really, all the teams do exactly the same thing. So they risked an experiment. They took the whole team away, even in match season, on ski trips and to look at social projects in Chicago. This was expensive, it was time-consuming, and it could be a little risky putting a whole bunch of rugby players on a ski slope, right?
在英格蘭,稱霸的橄欖球隊, 或說稱霸的橄欖球隊之一, 就是薩拉森人隊。 球隊經理及教練意識到 他們所做的體格訓練 及數據導向的體能訓練 都變成通用的, 真的,所有的隊伍 都在做一模一樣的事。 所以他們放手一搏做了個實驗。 他們把整個隊伍調開, 即使在賽季中, 也跑去滑雪, 去芝加哥當義工。 這很貴, 這很花時間, 而且還有點小風險, 你想,把整隊的橄欖球員 拉去滑雪欸?
(Laughter)
(笑聲)
But what they found was that the players came back with renewed bonds of loyalty and solidarity. And now when they're on the pitch under incredible pressure, they manifest what the manager calls "poise" -- an unflinching, unwavering dedication to each other. Their opponents are in awe of this, but still too in thrall to efficiency to try it.
但是他們發現球員回去時, 他們彼此之間的忠誠度 及團結度都更新了。 現在他們在球場上 面對極大的壓力時, 他們表現出經理說的「沉穩」—— 一種堅定不移的彼此奉獻。 他們的敵隊都對此敬畏無比, 但還是被效率束縛而不敢嘗試。
At a London tech company, Verve, the CEO measures just about everything that moves, but she couldn't find anything that made any difference to the company's productivity. So she devised an experiment that she calls "Love Week": a whole week where each employee has to look for really clever, helpful, imaginative things that a counterpart does, call it out and celebrate it. It takes a huge amount of time and effort; lots of people would call it distracting. But it really energizes the business and makes the whole company more productive.
在倫敦有家科技公司叫 Verve, 只要是會動的東西, 他們的執行長都要算一算, 但是她找不出任何東西 對公司的生產力有影響。 所以她設計了一個實驗, 她稱為「愛情週」: 整整一週的時間, 每個員工都要尋找真正高明、 有幫助、有想像力的東西, 是他們的對手做的, 找出來並慶祝一下。 這要花很多時間和努力, 很多人會說這是分心。 但是這確實為企業注入了活力, 使整個公司更有生產力。
Preparedness, coalition-building, imagination, experiments, bravery -- in an unpredictable age, these are tremendous sources of resilience and strength. They aren't efficient, but they give us limitless capacity for adaptation, variation and invention. And the less we know about the future, the more we're going to need these tremendous sources of human, messy, unpredictable skills.
準備、結盟、 想像、實驗、 勇氣—— 在無法預測的時代, 這些都是韌性與力量的重大來源。 這些都沒有效率, 但是會給我們無限的能力 來適應、變化及創新。 我們對未來所知愈少, 就愈需要這些巨大的來源, 給我們人性化、亂七八糟、 不可預測的能力。
But in our growing dependence on technology, we're asset-stripping those skills. Every time we use technology to nudge us through a decision or a choice or to interpret how somebody's feeling or to guide us through a conversation, we outsource to a machine what we could, can do ourselves, and it's an expensive trade-off. The more we let machines think for us, the less we can think for ourselves. The more --
但是在我們對科技 愈來愈依賴的過程中, 我們正以資產剝離的手法 剝奪這些能力。 每次我們用科技 推動自己做決定或做選擇, 或詮釋別人的情感, 或引導我們對話, 我們就是把本來可以 自己做的東西外包給機器, 而這是非常昂貴的交易。 我們愈讓機器幫我們思考, 我們自己就愈不會思考。 醫師——
(Applause)
(掌聲)
The more time doctors spend staring at digital medical records, the less time they spend looking at their patients. The more we use parenting apps, the less we know our kids. The more time we spend with people that we're predicted and programmed to like, the less we can connect with people who are different from ourselves. And the less compassion we need, the less compassion we have.
醫師盯著數位病例的時間愈多, 他們花在看病人的時間就愈少, 我們愈常使用育兒應用程式, 就愈不了解自己的兒女。 我們花愈多時間 與自己預測並設計好 會喜歡的人相處, 就愈不會與異己之人建立關係。 我們需要的同情心愈來愈少, 我們的同情心就會愈來愈少。
What all of these technologies attempt to do is to force-fit a standardized model of a predictable reality onto a world that is infinitely surprising. What gets left out? Anything that can't be measured -- which is just about everything that counts.
這些科技企圖要做的, 是把可預測現實的標準化模式 硬塞到一個瞬息萬變的世界來用。 這遺漏了什麼? 無法測量的事物, 這幾乎包含了所有事物。
(Applause)
(掌聲)
Our growing dependence on technology risks us becoming less skilled, more vulnerable to the deep and growing complexity of the real world.
我們愈來愈依賴科技, 風險就是讓我們愈來愈沒有能力, 更無法應對 深不可測、日益複雜的 真實世界。
Now, as I was thinking about the extremes of stress and turbulence that we know we will have to confront, I went and I talked to a number of chief executives whose own businesses had gone through existential crises, when they teetered on the brink of collapse. These were frank, gut-wrenching conversations. Many men wept just remembering. So I asked them: "What kept you going through this?"
在我思索著我們知道 一定會面對的極端壓力與混亂時, 我去找了幾位執行長談談, 他們的公司都經歷過生存危機, 瀕臨倒閉的窘境。 這些都是坦率、令人心碎的對談。 許多男士想起來就掉淚。 我問他們: 「什麼讓你熬過去?」
And they all had exactly the same answer. "It wasn't data or technology," they said. "It was my friends and my colleagues who kept me going."
他們的回答都一模一樣。 「不是數據或科技,」他們說。 「是我的朋友及同事讓我繼續走下去。」
One added, "It was pretty much the opposite of the gig economy."
有一位還補充說: 「這跟零工經濟大相逕庭。」
But then I went and I talked to a group of young, rising executives, and I asked them, "Who are your friends at work?" And they just looked blank.
然後我去跟一群 年輕的後起新秀對談, 我問他們: 「誰是你在職場上的朋友?」 他們都兩眼空空的瞪著我。
"There's no time."
「沒有時間。」
"They're too busy."
「他們太忙了。」
"It's not efficient."
「這沒有效率。」
Who, I wondered, is going to give them imagination and stamina and bravery when the storms come?
我心想,誰會給他們 想像力、耐力和勇敢, 在風暴來臨之時?
Anyone who tries to tell you that they know the future is just trying to own it, a spurious kind of manifest destiny. The harder, deeper truth is that the future is uncharted, that we can't map it till we get there.
任何試圖告訴你他們知道未來的人, 只是想佔有它, 一種虛假的昭昭天命。 更艱難更深沉的事實是, 未來是未知的, 我們無法把它標到地圖上, 直到我們到達為止。
But that's OK, because we have so much imagination -- if we use it. We have deep talents of inventiveness and exploration -- if we apply them. We are brave enough to invent things we've never seen before. Lose those skills, and we are adrift. But hone and develop them, we can make any future we choose.
但這沒關係, 因為我們有這麼多的想像力—— 如果我們運用想像力。 我們有深不可測的創新及探索才能—— 如果我們應用這些才能。 我們有足夠的勇氣 去發明我們從未見過的事物。 失去這些才能, 我們只能隨波逐流。 但是磨練及開發這些才能, 我們可以創造我們選擇的未來。
Thank you.
謝謝各位。
(Applause)
(掌聲)