一項(xiàng)新研究表明,允許初級(jí)崗位的員工使用ChatGPT等人工智能工具幫助他們完成工作,可以極大地提升工作效率。
在一項(xiàng)新研究中,麻省理工學(xué)院和斯坦福大學(xué)的研究人員分析了ChatGPT等生成式人工智能工具對(duì)某家《財(cái)富》美國(guó)500強(qiáng)軟件公司生產(chǎn)效率的影響。
生成式人工智能模型能夠從人類(lèi)向它們展示的例子中加以學(xué)習(xí),并根據(jù)學(xué)到的信息生成全新的內(nèi)容。
研究團(tuán)隊(duì)使用了5,179名客服代表的數(shù)據(jù),發(fā)現(xiàn)使用人工智能對(duì)話助手的員工的工作效率比未使用的員工高13.8%。
研究人員通過(guò)每名客服每小時(shí)處理多少個(gè)問(wèn)題來(lái)衡量生產(chǎn)力的高低。
“生產(chǎn)力的提升主要反應(yīng)在三個(gè)維度:客服處理單個(gè)聊天所需要的時(shí)間減少了、客服每小時(shí)處理的聊天數(shù)量增加了(客服人員或許需要同時(shí)處理多個(gè)對(duì)話),問(wèn)題成功解決的比例出現(xiàn)小幅提升?!痹撗芯康淖髡咴谟擅绹?guó)國(guó)家經(jīng)濟(jì)研究局(National Bureau of Economic Research)發(fā)表的論文中寫(xiě)道。
研究報(bào)告稱(chēng),新手和低技能工人生產(chǎn)效率提升最高,而生成式人工智能對(duì)經(jīng)驗(yàn)豐富和高技能工人的“影響最小”。
在經(jīng)驗(yàn)不足的員工中,在人工智能的幫助下,他們的工作速度比沒(méi)有技術(shù)幫助的情況下快35%。
“只任職了兩個(gè)月的員工在人工智能的幫助下,工作表現(xiàn)和任職超過(guò)六個(gè)月的員工一樣好。”研究團(tuán)隊(duì)表示,有證據(jù)表明,生成式人工智能模型“將更有能力的員工掌握的潛在的隱性知識(shí)加以傳播,幫助新員工沿著經(jīng)驗(yàn)曲線向下移動(dòng)?!?/p>
他們還認(rèn)為,他們的研究表明,人工智能有助于“改善客戶(hù)情緒,減少了對(duì)管理層干預(yù)的需求,提升了員工的留任率?!?/p>
研究人員寫(xiě)道:“我們的總體研究結(jié)果表明,與人類(lèi)一起工作的生成式人工智能可以對(duì)員工個(gè)人的生產(chǎn)力和留任率產(chǎn)生重大的積極影響?!?/p>
他們說(shuō),“據(jù)我們所知”,他們的研究是首個(gè)針對(duì)現(xiàn)實(shí)環(huán)境中生成式人工智能對(duì)生產(chǎn)力的影響進(jìn)行的研究。
自從最近幾個(gè)月,OpenAI的聊天機(jī)器人ChatGPT成為現(xiàn)象級(jí)流行文化以來(lái),人工智能在工作中的作用已經(jīng)成為熱門(mén)話題。
雖然一些員工擔(dān)心這項(xiàng)技術(shù)可能會(huì)取代自己,但包括IBM的老板阿爾溫德·克里希納在內(nèi)的許多著名首席執(zhí)行官則認(rèn)為,員工應(yīng)該關(guān)注如何“與人工智能攜手共事”。
沃爾瑪(Walmart)的首席人事官唐娜·莫里斯最近在《財(cái)富》雜志的一篇專(zhuān)欄文章中寫(xiě)道,像人工智能這樣的技術(shù)實(shí)際上是在“為員工賦能”,而比爾·蓋茨則預(yù)言,幾年后,每個(gè)人都將擁有自己的“白領(lǐng)”虛擬助手。
“1,000%的游戲規(guī)則改變者”
位于迪拜的人工智能房地產(chǎn)平臺(tái)Realiste的創(chuàng)始人亞歷克斯·加爾采夫向《財(cái)富》雜志表示,麻省理工學(xué)院/斯坦福大學(xué)的研究發(fā)現(xiàn)生產(chǎn)率的提高“確實(shí)意義重大,不應(yīng)該被低估”。
“我們要認(rèn)識(shí)到,即便是生產(chǎn)率的微弱提升也會(huì)對(duì)企業(yè)運(yùn)營(yíng)產(chǎn)生重大影響,從而提高效率和盈利能力?!彼谝环怆娮余]件中寫(xiě)道:“由于將人工智能引入了本公司的工作流程,我們看到公司生產(chǎn)力大幅提升。通過(guò)利用人工智能為客戶(hù)識(shí)別市場(chǎng)上最好的物業(yè),我們的投資經(jīng)理的生產(chǎn)力提高了200%。我堅(jiān)信,只要使用正確的方法和工具,許多其他行業(yè)也能夠?qū)崿F(xiàn)這種水平的進(jìn)步。”
加爾采夫說(shuō),他相信未來(lái)兩年到三年內(nèi),對(duì)人工智能的使用和創(chuàng)新會(huì)增加,他認(rèn)為這項(xiàng)技術(shù)將變得更加用戶(hù)友好,更容易融入到商業(yè)運(yùn)營(yíng)中。
他說(shuō):“通過(guò)實(shí)現(xiàn)重復(fù)型任務(wù)的自動(dòng)化、簡(jiǎn)化決策過(guò)程、提供獨(dú)特的見(jiàn)解,這些工具對(duì)生產(chǎn)力的提升作用將是傳統(tǒng)手段難以或不可能實(shí)現(xiàn)的?!?/p>
與此同時(shí),英國(guó)人工智能咨詢(xún)公司Deeper Insights的首席執(zhí)行官及創(chuàng)始人杰克·漢普森表示,他的公司及客戶(hù)見(jiàn)證了比研究中記錄的14%更大的生產(chǎn)率提升。
他在4月24日的電話采訪中告訴《財(cái)富》雜志,像ChatGPT這樣的人工智能工具“1,000%”將改變職場(chǎng)的游戲規(guī)則。
漢普森表示,Deeper Insights合作的一家人力資源公司開(kāi)始使用類(lèi)似ChatGPT的大型語(yǔ)言模型協(xié)助起草策略、檢索策略信息、搜索最新的職場(chǎng)案件判例后,生產(chǎn)率提高了20%至40%。
他補(bǔ)充說(shuō),Deeper Insights本身也在使用ChatGPT等工具來(lái)完成部分工作,比人工的速度要快得多。
“我們顯然需要大量數(shù)據(jù),而很多時(shí)候我們沒(méi)有足夠的客戶(hù)數(shù)據(jù)來(lái)訓(xùn)練人工智能。”漢普森解釋說(shuō):“所以我們現(xiàn)在使用ChatGPT和其他大型語(yǔ)言模型來(lái)創(chuàng)建替代訓(xùn)練數(shù)據(jù)集,這為我們節(jié)省了大量時(shí)間和金錢(qián)。在一個(gè)為期三周或三個(gè)月的項(xiàng)目中,我們之前可能會(huì)花大約兩周到三周的時(shí)間來(lái)創(chuàng)建訓(xùn)練數(shù)據(jù)集,而現(xiàn)在只需要一兩天?!?/p>
生成式人工智能研究的作者在論文中強(qiáng)調(diào),他們的研究“不是為了闡釋生成式人工智能工具對(duì)總就業(yè)或工資的影響”。
他們說(shuō):“我們的研究結(jié)果并不涉及人工智能可能對(duì)技能需求、工作設(shè)計(jì)、工資或客戶(hù)需求產(chǎn)生的長(zhǎng)期影響?!钡麄冎赋?,他們的研究確實(shí)提出了一個(gè)問(wèn)題,即員工向人工智能系統(tǒng)提供的數(shù)據(jù)是否應(yīng)該得到補(bǔ)償,以及如何得到補(bǔ)償。他們補(bǔ)充道,生產(chǎn)率的提高可能是“由于人工智能系統(tǒng)得以將公司高水平員工的實(shí)踐經(jīng)驗(yàn)收錄到系統(tǒng)中?!保ㄘ?cái)富中文網(wǎng))
譯者:Agatha
一項(xiàng)新研究表明,允許初級(jí)崗位的員工使用ChatGPT等人工智能工具幫助他們完成工作,可以極大地提升工作效率。
在一項(xiàng)新研究中,麻省理工學(xué)院和斯坦福大學(xué)的研究人員分析了ChatGPT等生成式人工智能工具對(duì)某家《財(cái)富》美國(guó)500強(qiáng)軟件公司生產(chǎn)效率的影響。
生成式人工智能模型能夠從人類(lèi)向它們展示的例子中加以學(xué)習(xí),并根據(jù)學(xué)到的信息生成全新的內(nèi)容。
研究團(tuán)隊(duì)使用了5,179名客服代表的數(shù)據(jù),發(fā)現(xiàn)使用人工智能對(duì)話助手的員工的工作效率比未使用的員工高13.8%。
研究人員通過(guò)每名客服每小時(shí)處理多少個(gè)問(wèn)題來(lái)衡量生產(chǎn)力的高低。
“生產(chǎn)力的提升主要反應(yīng)在三個(gè)維度:客服處理單個(gè)聊天所需要的時(shí)間減少了、客服每小時(shí)處理的聊天數(shù)量增加了(客服人員或許需要同時(shí)處理多個(gè)對(duì)話),問(wèn)題成功解決的比例出現(xiàn)小幅提升?!痹撗芯康淖髡咴谟擅绹?guó)國(guó)家經(jīng)濟(jì)研究局(National Bureau of Economic Research)發(fā)表的論文中寫(xiě)道。
研究報(bào)告稱(chēng),新手和低技能工人生產(chǎn)效率提升最高,而生成式人工智能對(duì)經(jīng)驗(yàn)豐富和高技能工人的“影響最小”。
在經(jīng)驗(yàn)不足的員工中,在人工智能的幫助下,他們的工作速度比沒(méi)有技術(shù)幫助的情況下快35%。
“只任職了兩個(gè)月的員工在人工智能的幫助下,工作表現(xiàn)和任職超過(guò)六個(gè)月的員工一樣好。”研究團(tuán)隊(duì)表示,有證據(jù)表明,生成式人工智能模型“將更有能力的員工掌握的潛在的隱性知識(shí)加以傳播,幫助新員工沿著經(jīng)驗(yàn)曲線向下移動(dòng)?!?/p>
他們還認(rèn)為,他們的研究表明,人工智能有助于“改善客戶(hù)情緒,減少了對(duì)管理層干預(yù)的需求,提升了員工的留任率。”
研究人員寫(xiě)道:“我們的總體研究結(jié)果表明,與人類(lèi)一起工作的生成式人工智能可以對(duì)員工個(gè)人的生產(chǎn)力和留任率產(chǎn)生重大的積極影響?!?/p>
他們說(shuō),“據(jù)我們所知”,他們的研究是首個(gè)針對(duì)現(xiàn)實(shí)環(huán)境中生成式人工智能對(duì)生產(chǎn)力的影響進(jìn)行的研究。
自從最近幾個(gè)月,OpenAI的聊天機(jī)器人ChatGPT成為現(xiàn)象級(jí)流行文化以來(lái),人工智能在工作中的作用已經(jīng)成為熱門(mén)話題。
雖然一些員工擔(dān)心這項(xiàng)技術(shù)可能會(huì)取代自己,但包括IBM的老板阿爾溫德·克里希納在內(nèi)的許多著名首席執(zhí)行官則認(rèn)為,員工應(yīng)該關(guān)注如何“與人工智能攜手共事”。
沃爾瑪(Walmart)的首席人事官唐娜·莫里斯最近在《財(cái)富》雜志的一篇專(zhuān)欄文章中寫(xiě)道,像人工智能這樣的技術(shù)實(shí)際上是在“為員工賦能”,而比爾·蓋茨則預(yù)言,幾年后,每個(gè)人都將擁有自己的“白領(lǐng)”虛擬助手。
“1,000%的游戲規(guī)則改變者”
位于迪拜的人工智能房地產(chǎn)平臺(tái)Realiste的創(chuàng)始人亞歷克斯·加爾采夫向《財(cái)富》雜志表示,麻省理工學(xué)院/斯坦福大學(xué)的研究發(fā)現(xiàn)生產(chǎn)率的提高“確實(shí)意義重大,不應(yīng)該被低估”。
“我們要認(rèn)識(shí)到,即便是生產(chǎn)率的微弱提升也會(huì)對(duì)企業(yè)運(yùn)營(yíng)產(chǎn)生重大影響,從而提高效率和盈利能力?!彼谝环怆娮余]件中寫(xiě)道:“由于將人工智能引入了本公司的工作流程,我們看到公司生產(chǎn)力大幅提升。通過(guò)利用人工智能為客戶(hù)識(shí)別市場(chǎng)上最好的物業(yè),我們的投資經(jīng)理的生產(chǎn)力提高了200%。我堅(jiān)信,只要使用正確的方法和工具,許多其他行業(yè)也能夠?qū)崿F(xiàn)這種水平的進(jìn)步?!?/p>
加爾采夫說(shuō),他相信未來(lái)兩年到三年內(nèi),對(duì)人工智能的使用和創(chuàng)新會(huì)增加,他認(rèn)為這項(xiàng)技術(shù)將變得更加用戶(hù)友好,更容易融入到商業(yè)運(yùn)營(yíng)中。
他說(shuō):“通過(guò)實(shí)現(xiàn)重復(fù)型任務(wù)的自動(dòng)化、簡(jiǎn)化決策過(guò)程、提供獨(dú)特的見(jiàn)解,這些工具對(duì)生產(chǎn)力的提升作用將是傳統(tǒng)手段難以或不可能實(shí)現(xiàn)的。”
與此同時(shí),英國(guó)人工智能咨詢(xún)公司Deeper Insights的首席執(zhí)行官及創(chuàng)始人杰克·漢普森表示,他的公司及客戶(hù)見(jiàn)證了比研究中記錄的14%更大的生產(chǎn)率提升。
他在4月24日的電話采訪中告訴《財(cái)富》雜志,像ChatGPT這樣的人工智能工具“1,000%”將改變職場(chǎng)的游戲規(guī)則。
漢普森表示,Deeper Insights合作的一家人力資源公司開(kāi)始使用類(lèi)似ChatGPT的大型語(yǔ)言模型協(xié)助起草策略、檢索策略信息、搜索最新的職場(chǎng)案件判例后,生產(chǎn)率提高了20%至40%。
他補(bǔ)充說(shuō),Deeper Insights本身也在使用ChatGPT等工具來(lái)完成部分工作,比人工的速度要快得多。
“我們顯然需要大量數(shù)據(jù),而很多時(shí)候我們沒(méi)有足夠的客戶(hù)數(shù)據(jù)來(lái)訓(xùn)練人工智能。”漢普森解釋說(shuō):“所以我們現(xiàn)在使用ChatGPT和其他大型語(yǔ)言模型來(lái)創(chuàng)建替代訓(xùn)練數(shù)據(jù)集,這為我們節(jié)省了大量時(shí)間和金錢(qián)。在一個(gè)為期三周或三個(gè)月的項(xiàng)目中,我們之前可能會(huì)花大約兩周到三周的時(shí)間來(lái)創(chuàng)建訓(xùn)練數(shù)據(jù)集,而現(xiàn)在只需要一兩天?!?/p>
生成式人工智能研究的作者在論文中強(qiáng)調(diào),他們的研究“不是為了闡釋生成式人工智能工具對(duì)總就業(yè)或工資的影響”。
他們說(shuō):“我們的研究結(jié)果并不涉及人工智能可能對(duì)技能需求、工作設(shè)計(jì)、工資或客戶(hù)需求產(chǎn)生的長(zhǎng)期影響?!钡麄冎赋?,他們的研究確實(shí)提出了一個(gè)問(wèn)題,即員工向人工智能系統(tǒng)提供的數(shù)據(jù)是否應(yīng)該得到補(bǔ)償,以及如何得到補(bǔ)償。他們補(bǔ)充道,生產(chǎn)率的提高可能是“由于人工智能系統(tǒng)得以將公司高水平員工的實(shí)踐經(jīng)驗(yàn)收錄到系統(tǒng)中。”(財(cái)富中文網(wǎng))
譯者:Agatha
Allowing staff in entry-level roles to use A.I. tools like ChatGPT to help them with their work brings about big productivity boosts, according to new research.
In a new study, researchers from Massachusetts Institute of Technology and Stanford University analyzed the impact generative A.I. tools like ChatGPT had on productivity at an unnamed Fortune 500 software firm.
Generative A.I. models are programmed to use what they have learned from examples they have been shown in the past and generate something completely new based on that information.
Using data from 5,179 customer support agents, the research team found that workers who had access to an A.I.-based conversational assistant were 13.8% more productive than those who did not.
Productivity was measured by how many issues individual agents resolved per hour.
“This increase reflects shifts in three components of productivity: a decline in the time it takes an agent to handle an individual chat, an increase in the number of chats that an agent is able to handle per hour (agents may handle multiple calls at once), and a small increase in the share of chats that are successfully resolved,” the study’s authors wrote in their paper, which was published by the National Bureau of Economic Research.
The greatest productivity boost was seen among novice and low-skilled workers, according to the research paper, while access to generative A.I. had “minimal impact” on experienced and highly skilled workers.
In less experienced staff, A.I. helped them work 35% faster than they had without the tech’s assistance.
“Treated agents with two months of tenure perform just as well as untreated agents with over six months of tenure,” the research team said, arguing that there was evidence generative A.I. models “disseminate the potentially tacit knowledge of more able workers and help newer workers move down the experience curve.”
They also argued that their study showed A.I. assistance “improves customer sentiment, reduces requests for managerial intervention, and improves employee retention.”
“Our overall findings demonstrate that generative A.I. working alongside humans can have a significant positive impact on the productivity and retention of individual workers,” the researchers wrote.
They said that “to the best of our knowledge,” their research was the first time the impact of generative A.I. on workplace productivity had ever been investigated in a real-world setting.
Since OpenAI’s chatbot ChatGPT became a cultural phenomenon in recent months, the role of artificial intelligence in the workplace has become a hot topic of debate.
While some workers are concerned that the technology could displace them from their roles, many big-name CEOs, such as IBM boss Arvind Krishna, have said employees should instead be focused on working “hand in hand with artificial intelligence.”
Walmart’s chief people officer Donna Morris wrote in a recent op-ed for Fortune that tech like A.I. was actually “empowering our people,” while Bill Gates has prophesized that in years to come, everyone will have their own “white collar” virtual assistant.
“1,000% a game changer”
Alex Galtsev, founder of Realiste—a Dubai-based A.I.-powered real estate platform—told Fortune that the productivity boost observed in the MIT/Stanford study “is truly significant and should not be underestimated.”
“It’s crucial to recognize that even small improvements in productivity can have a substantial impact on business operations, leading to increased efficiency and profitability,” he said in an email. “We have witnessed a significant increase in productivity in our organization thanks to the incorporation of A.I. into our work processes. Our investment managers’ productivity has increased by up to 200% by utilizing artificial intelligence to identify the best properties on the market for our clients. I strongly believe that this level of improvement is achievable for many other businesses as well, given the right approach and tools.”
Galtsev said he believed there would be increased adoption and innovation in A.I. over the next two to three years, arguing that the technology would become more user-friendly and thus easier to integrate into business operations.
“By automating repetitive tasks, streamlining decision-making processes, and providing unique insights, these tools can offer productivity improvements that are difficult or impossible to achieve through traditional means,” he said.
Meanwhile, Jack Hampson, CEO and founder of British A.I. consultancy Deeper Insights, said his company and its clients were seeing much bigger productivity gains than the 14% recorded in the study.
He told Fortune in a phone call on April 24 that A.I. tools like ChatGPT will “1,000%” be a game changer in the workplace.
An HR firm Deeper Insights works with has seen productivity boosts of 20% to 40% since it started using a ChatGPT-like large language model to assist with tasks like writing policy, retrieving policy information, and searching for employment case law updates, Hampson told Fortune.
Deeper Insights itself is also using tools like ChatGPT to get work done much faster than it could be completed manually, he added.
“We’re obviously very data hungry, and a lot of the time we don’t have enough data from a client to train an [A.I.] model,” Hampson explained. “So we’re now using things like ChatGPT and other large language models to create alternative training datasets, and that is saving us a lot of time and money. In a three-week or a three-month project, we probably spend about two to three weeks on the creation of training datasets, and that time is now a day or two.”
The authors of the generative A.I. study stressed in their paper that their research was “not designed to shed light on the aggregate employment or wage effects of generative A.I. tools.”
“Our results do not capture potential longer-term impacts on skill demand, job design, wages, or customer demand,” they said, but they noted that their findings did raise questions about whether—and how—workers should be compensated for the data they provide to A.I. systems, adding that the productivity boost was likely “driven by the A.I. system’s ability to embody the best practices of high-skill workers in [the] firm.”