在新聞和某些學(xué)術(shù)文獻(xiàn)中，自動(dòng)化往往在被描繪成人機(jī)之間的絕世之戰(zhàn)，而在這場(chǎng)戰(zhàn)爭(zhēng)中，機(jī)器似乎注定將取得勝利，唯一的問(wèn)題只在于它們什么時(shí)候舉行頒獎(jiǎng)儀式。隨著職場(chǎng)中的自動(dòng)化技術(shù)迅速?gòu)目苹眯≌f(shuō)走入商業(yè)現(xiàn)實(shí)，我們相信，它帶來(lái)的遠(yuǎn)非人或機(jī)器人的簡(jiǎn)單二選一，而是更加微妙的抉擇。針對(duì)美國(guó)經(jīng)濟(jì)領(lǐng)域各個(gè)行業(yè)的超過(guò)2,000種工作，我們進(jìn)行了研究。以下是研究得出的八項(xiàng)發(fā)現(xiàn)，其中凸顯了不同行業(yè)的自動(dòng)化潛力——我們提供了一些技術(shù)指標(biāo)，讓大家看看哪些崗位最可能接受自動(dòng)化，哪些崗位最不可能被自動(dòng)化。

一些工作可以被自動(dòng)化，不代表它就會(huì)被自動(dòng)化

盡管技術(shù)可行性是自動(dòng)化的必要前提，但是打造令人驚嘆的商業(yè)應(yīng)用還需要考慮其他因素。這其中包括開發(fā)和部署自動(dòng)化軟硬件所需的成本，以及勞動(dòng)力供需的變動(dòng)。用昂貴的機(jī)器人取代每小時(shí)掙10美元的人類廚師，在技術(shù)上或許可以實(shí)現(xiàn)，但在商業(yè)上可能沒(méi)有太大意義，因?yàn)槌杀究赡芴撸顿Y回報(bào)率很差。管理和社會(huì)問(wèn)題，可能也是許多醫(yī)院的病人在手術(shù)后醒來(lái)想要人類護(hù)士而不是機(jī)器人照料的原因。

某些體力工作被自動(dòng)化的概率最大

美國(guó)職場(chǎng)人士幾乎有五分之一的時(shí)間都要干體力活，或是在可預(yù)見的環(huán)境中操作器械——也就是說(shuō)，這些環(huán)境的設(shè)定十分常見，改變也相對(duì)容易預(yù)測(cè)。這類工作中，有超過(guò)四分之三都能用我們當(dāng)下的技術(shù)自動(dòng)化，尤其是制造業(yè)和食品服務(wù)業(yè)。從技術(shù)角度來(lái)看，這些行業(yè)最有可能接受自動(dòng)化。在廠房中，機(jī)器人已經(jīng)開始進(jìn)行產(chǎn)品裝配和打包的重復(fù)工作，而在食品服務(wù)業(yè)，一些餐廳正在測(cè)試自助下單，甚至采用機(jī)器人侍者。

數(shù)據(jù)收集和處理領(lǐng)域的自動(dòng)化時(shí)機(jī)也已經(jīng)成熟。在美國(guó)經(jīng)濟(jì)領(lǐng)域的所有崗位上，員工要花費(fèi)三分之一的時(shí)間來(lái)收集和處理數(shù)據(jù)。這兩項(xiàng)工作都很適合自動(dòng)化，這將給零售、金融服務(wù)和保險(xiǎn)等行業(yè)帶來(lái)影響。我們沒(méi)必要讓員工離職，但他們扮演的角色可能會(huì)發(fā)生很大變化。例如，抵押經(jīng)紀(jì)人會(huì)花費(fèi)多達(dá)90%的時(shí)間來(lái)處理申請(qǐng)，自動(dòng)化以后，他們可以用更多時(shí)間來(lái)給客戶提供建議。

甚至連高薪崗位也會(huì)受到影響。并不只有入門員工或低收入員工才需要收集和處理數(shù)據(jù)，那些年收入超過(guò)20萬(wàn)美元的人也需要花費(fèi)30%的時(shí)間來(lái)做這項(xiàng)工作。因此，各公司會(huì)很有興趣將這些工作自動(dòng)化?？傮w來(lái)看，工資和工作能否被自動(dòng)化的關(guān)系還有很大變數(shù)。

機(jī)器人目前還不善于鋪床疊被

目前來(lái)看，在不可預(yù)見的環(huán)境中進(jìn)行體力工作或操作器械的工作，相對(duì)而言難以被自動(dòng)化。例如在建筑工地操作吊車，或是在公共場(chǎng)所清掃垃圾，或是在賓館整理床鋪。最后一項(xiàng)屬于不可預(yù)見的環(huán)境，是因?yàn)榭腿丝赡軙?huì)把枕頭扔到不同的地方，或者把衣服留在床上，讓機(jī)器人很難自動(dòng)進(jìn)行客房清潔。不過(guò)這種情況可能很快就會(huì)改變，人們已經(jīng)開始進(jìn)行重點(diǎn)研究，來(lái)改善機(jī)器人在不可預(yù)見的物理環(huán)境中的表現(xiàn)。

是時(shí)候去當(dāng)老師或潔牙師了？

最難利用當(dāng)今的技術(shù)自動(dòng)化的是那些管理和培養(yǎng)人才的工作（只有9%有自動(dòng)化的潛力），決策、規(guī)劃、創(chuàng)造性的工作（18%）或是與客戶、供應(yīng)商和其他股東互動(dòng)的工作（20%）。這些工作涉及編寫軟件、創(chuàng)建菜單、撰寫宣傳材料——或是建議顧客哪種顏色的鞋子最合適，經(jīng)驗(yàn)和閱歷往往很重要。

在醫(yī)療行業(yè)中，注冊(cè)護(hù)士的工作只有不到30%可以被自動(dòng)化，而在潔牙師的工作中，這一比例降到了13%。在我們研究的所有行業(yè)中，最不受自動(dòng)化影響的是教育。教育需要很強(qiáng)的專業(yè)能力，以及與他人交流的復(fù)雜技巧，迄今為止，機(jī)器在這方面的表現(xiàn)很不完備，只有極少數(shù)情況例外。

機(jī)器會(huì)改變工作，但它們無(wú)法完全取代人類。分析自動(dòng)化的技術(shù)可行性時(shí)，最好不要看整個(gè)崗位，而要看各種工作內(nèi)容所消耗的時(shí)間，以及利用現(xiàn)有技術(shù)可以自動(dòng)化且能應(yīng)用于職場(chǎng)的工作內(nèi)容占比。整體來(lái)看，我們發(fā)現(xiàn)只有5%的崗位可以用現(xiàn)有技術(shù)完全自動(dòng)化。然而，現(xiàn)有技術(shù)卻可以讓付酬工作中45%的內(nèi)容自動(dòng)化。此外還有超過(guò)30%的崗位，它們的工作內(nèi)容有約60%可以被自動(dòng)化。

請(qǐng)繼續(xù)留意這一領(lǐng)域

科技會(huì)隨著時(shí)間不斷發(fā)展，技術(shù)可行性也在不斷進(jìn)步。本文中的分析目前關(guān)注的只是現(xiàn)有技術(shù)，但是我們會(huì)進(jìn)行持續(xù)的研究，考慮科技發(fā)展的不同情況。隨著科技的發(fā)展，例如機(jī)器可能會(huì)掌握自然語(yǔ)言，擁有與普通人類似的能力，屆時(shí)在技術(shù)上可以自動(dòng)化的工作類型就會(huì)增加。

自動(dòng)化也會(huì)從根本上改變機(jī)構(gòu)的性質(zhì)。未來(lái)管理者面臨的挑戰(zhàn)，在于他們需要考慮用機(jī)器取代人力的成本，以及在改變后的辦公環(huán)境中調(diào)整商業(yè)流程的復(fù)雜程度，從而確定如何用自動(dòng)化改變公司，怎樣發(fā)揮自動(dòng)化的價(jià)值。自動(dòng)化帶來(lái)的主要效益，可能并非來(lái)自人力成本的降低，而是源于減少錯(cuò)誤率、提高產(chǎn)出、改善質(zhì)量、安全性和速度導(dǎo)致的生產(chǎn)效率的提高。 （財(cái)富中文網(wǎng)）

譯者：嚴(yán)匡正

Automation is often depicted in news articles and some academic literature as a titanic struggle between man and machine, in which the machine seems destined to win and the only question is how soon to schedule the medal ceremony. As automation in the workplace moves rapidly from science fiction to business fact, we believe the changes it will bring are more nuanced than a simple choice between human and robot. We have examined 2,000-plus work activities in every industry sector across the US economy. Here are eight findings from our research, highlighting the automation potential of each sector—providing some technical indicators as to which occupations are most and least likely to go to a machine.

Just because something can be automated doesn’t mean it will be.

While technical feasibility is a necessary precondition for automation, other factors are required to build a compelling business case. These include the cost of developing and deploying the hardware and the software for automation, and the supply-and-demand dynamics of labor. Replacing human cooks earning $10 per hour with expensive robots may be possible technically, but might not make business sense because it may cost too much and not provide a good return on investment. Regulatory and social issues could also be factors many hospital patients will want a human nurse rather than a robot to care for them when they wake up after surgery.

Certain physical jobs have the highest potential to be automated.

Almost one-fifth of the time spent in US workplaces involves performing physical activities or operating machinery in a predictable environment—that is, specific actions in familiar settings where changes are relatively easy to anticipate. More than three-quarters of such activities could be automated already with today’s technology, and figure prominently in manufacturing and food service, making these sectors the most technically susceptible to automation. Robots on factory floors already do repetitive rote tasks such as product assembly and packaging, while in food service, some restaurants are testing self-service ordering or even robotic servers.

Data collection and processing are ripe for automation, too. Across all occupations in the US economy, workers spend one-third of their time collecting and processing data. Both activities are highly like to be automated and could affect industries, from retail to financial services and insurance. Workers won’t necessarily be out of the job, but their roles may very well change. For example, mortgage brokers spend as much as 90% of their time processing applications, and could instead spend more time advising clients.

Even high-paying jobs will be affected. It’s not just entry-level workers or low-wage clerks who collect and process data; people whose annual incomes exceed $200,000 spend more than 30% of their time doing so, too. That makes activities in these jobs attractive for companies to automate. Overall, the correlation between wages and automatability shows a great deal of variability.

Robots aren’t great at making beds — yet.

For now, activities that require physical movement or operating machinery in unpredictable settings are relatively challenging to automate. Examples include operating a crane on a construction site, collecting trash in public areas, or making beds in hotel rooms. The latter is unpredictable because guests throw pillows in different places, or may leave clothing on their beds, which makes it hard for a robot to carry out maid service. This might change soon, however, as significant research is being devoted to improving the performance of robots in physically unpredictable environments.

Time to become a teacher or dental hygienist?

The hardest activities to automate with the technologies available today are those that involve managing and developing people (9% automation potential), where expertise is applied to decision-making, planning, or creative work (18%), or interacting with customers, suppliers, and other stakeholders (20%). These activities, where experience and age are often an asset, can be as varied as coding software, creating menus, writing promotional materials — or advising customers which color shoes best suit them.

In health care, less than 30% of a registered nurse’s job could be automated, while for dental hygienists, that proportion drops to 13%. Of all the sectors we have examined, among the least susceptible to automation is education. The essence of teaching includes deep expertise and complex interactions with other people for which machines, so far and with few exceptions, receive an incomplete grade.

Machines will change jobs, but they won’t fully take over from humans. The technical feasibility of automation is best analyzed by looking not at occupations as a whole, but at the amount of time spent on individual activities, and the degree to which these could be automated by using technology that currently exists and adapting it to individual work activities. Overall, we find that only about 5% of occupations could be fully automated by adapting current technology. However, today’s technologies could automate 45% of the activities people are paid to perform across all occupations. What’s more, about 60% of all occupations could see 30% or more of their work activities automated.

Watch this space.

Technology continues to develop and technical feasibility will thus evolve over time. This analysis has focused only on currently available technologies, but our on-going research considers different scenarios for technology development. As technological advances such as machines being able to acquire natural language abilities that match median human capability, the numbers and types of activities that are technically susceptible to automation will increase.

Automation will fundamentally change the nature of organizations. The challenge for managers will be to identify where automation could transform their organizations, and then figure out where to unlock value, given the cost of replacing human labor with machines and the complexity of adapting business processes to a changed workplace. Most benefits may come not from reducing labor costs but from raising productivity through fewer errors, higher output, and improved quality, safety, and speed.