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最新研究:AI助手提升员工幸福度

最新研究:AI助手提升员工幸福度

Chloe Taylor 2023-05-02
一项新研究发现,在ChatGPT等人工智能的帮助下,员工的生产率提高了14%。

图片来源:MASKOT/GETTY IMAGES

一项新研究表明,允许初级岗位的员工使用ChatGPT等人工智能工具帮助他们完成工作,可以极大地提升工作效率。

在一项新研究中,麻省理工学院和斯坦福大学的研究人员分析了ChatGPT等生成式人工智能工具对某家《财富》美国500强软件公司生产效率的影响。

生成式人工智能模型能够从人类向它们展示的例子中加以学习,并根据学到的信息生成全新的内容。

研究团队使用了5,179名客服代表的数据,发现使用人工智能对话助手的员工的工作效率比未使用的员工高13.8%。

研究人员通过每名客服每小时处理多少个问题来衡量生产力的高低。

“生产力的提升主要反应在三个维度:客服处理单个聊天所需要的时间减少了、客服每小时处理的聊天数量增加了(客服人员或许需要同时处理多个对话),问题成功解决的比例出现小幅提升。”该研究的作者在由美国国家经济研究局(National Bureau of Economic Research)发表的论文中写道。

研究报告称,新手和低技能工人生产效率提升最高,而生成式人工智能对经验丰富和高技能工人的“影响最小”。

在经验不足的员工中,在人工智能的帮助下,他们的工作速度比没有技术帮助的情况下快35%。

“只任职了两个月的员工在人工智能的帮助下,工作表现和任职超过六个月的员工一样好。”研究团队表示,有证据表明,生成式人工智能模型“将更有能力的员工掌握的潜在的隐性知识加以传播,帮助新员工沿着经验曲线向下移动。”

他们还认为,他们的研究表明,人工智能有助于“改善客户情绪,减少了对管理层干预的需求,提升了员工的留任率。”

研究人员写道:“我们的总体研究结果表明,与人类一起工作的生成式人工智能可以对员工个人的生产力和留任率产生重大的积极影响。”

他们说,“据我们所知”,他们的研究是首个针对现实环境中生成式人工智能对生产力的影响进行的研究。

自从最近几个月,OpenAI的聊天机器人ChatGPT成为现象级流行文化以来,人工智能在工作中的作用已经成为热门话题。

虽然一些员工担心这项技术可能会取代自己,但包括IBM的老板阿尔温德·克里希纳在内的许多著名首席执行官则认为,员工应该关注如何“与人工智能携手共事”。

沃尔玛(Walmart)的首席人事官唐娜·莫里斯最近在《财富》杂志的一篇专栏文章中写道,像人工智能这样的技术实际上是在“为员工赋能”,而比尔·盖茨则预言,几年后,每个人都将拥有自己的“白领”虚拟助手。

“1,000%的游戏规则改变者”

位于迪拜的人工智能房地产平台Realiste的创始人亚历克斯·加尔采夫向《财富》杂志表示,麻省理工学院/斯坦福大学的研究发现生产率的提高“确实意义重大,不应该被低估”。

“我们要认识到,即便是生产率的微弱提升也会对企业运营产生重大影响,从而提高效率和盈利能力。”他在一封电子邮件中写道:“由于将人工智能引入了本公司的工作流程,我们看到公司生产力大幅提升。通过利用人工智能为客户识别市场上最好的物业,我们的投资经理的生产力提高了200%。我坚信,只要使用正确的方法和工具,许多其他行业也能够实现这种水平的进步。”

加尔采夫说,他相信未来两年到三年内,对人工智能的使用和创新会增加,他认为这项技术将变得更加用户友好,更容易融入到商业运营中。

他说:“通过实现重复型任务的自动化、简化决策过程、提供独特的见解,这些工具对生产力的提升作用将是传统手段难以或不可能实现的。”

与此同时,英国人工智能咨询公司Deeper Insights的首席执行官及创始人杰克·汉普森表示,他的公司及客户见证了比研究中记录的14%更大的生产率提升。

他在4月24日的电话采访中告诉《财富》杂志,像ChatGPT这样的人工智能工具“1,000%”将改变职场的游戏规则。

汉普森表示,Deeper Insights合作的一家人力资源公司开始使用类似ChatGPT的大型语言模型协助起草策略、检索策略信息、搜索最新的职场案件判例后,生产率提高了20%至40%。

他补充说,Deeper Insights本身也在使用ChatGPT等工具来完成部分工作,比人工的速度要快得多。

“我们显然需要大量数据,而很多时候我们没有足够的客户数据来训练人工智能。”汉普森解释说:“所以我们现在使用ChatGPT和其他大型语言模型来创建替代训练数据集,这为我们节省了大量时间和金钱。在一个为期三周或三个月的项目中,我们之前可能会花大约两周到三周的时间来创建训练数据集,而现在只需要一两天。”

生成式人工智能研究的作者在论文中强调,他们的研究“不是为了阐释生成式人工智能工具对总就业或工资的影响”。

他们说:“我们的研究结果并不涉及人工智能可能对技能需求、工作设计、工资或客户需求产生的长期影响。”但他们指出,他们的研究确实提出了一个问题,即员工向人工智能系统提供的数据是否应该得到补偿,以及如何得到补偿。他们补充道,生产率的提高可能是“由于人工智能系统得以将公司高水平员工的实践经验收录到系统中。”(财富中文网)

译者:Agatha

一项新研究表明,允许初级岗位的员工使用ChatGPT等人工智能工具帮助他们完成工作,可以极大地提升工作效率。

在一项新研究中,麻省理工学院和斯坦福大学的研究人员分析了ChatGPT等生成式人工智能工具对某家《财富》美国500强软件公司生产效率的影响。

生成式人工智能模型能够从人类向它们展示的例子中加以学习,并根据学到的信息生成全新的内容。

研究团队使用了5,179名客服代表的数据,发现使用人工智能对话助手的员工的工作效率比未使用的员工高13.8%。

研究人员通过每名客服每小时处理多少个问题来衡量生产力的高低。

“生产力的提升主要反应在三个维度:客服处理单个聊天所需要的时间减少了、客服每小时处理的聊天数量增加了(客服人员或许需要同时处理多个对话),问题成功解决的比例出现小幅提升。”该研究的作者在由美国国家经济研究局(National Bureau of Economic Research)发表的论文中写道。

研究报告称,新手和低技能工人生产效率提升最高,而生成式人工智能对经验丰富和高技能工人的“影响最小”。

在经验不足的员工中,在人工智能的帮助下,他们的工作速度比没有技术帮助的情况下快35%。

“只任职了两个月的员工在人工智能的帮助下,工作表现和任职超过六个月的员工一样好。”研究团队表示,有证据表明,生成式人工智能模型“将更有能力的员工掌握的潜在的隐性知识加以传播,帮助新员工沿着经验曲线向下移动。”

他们还认为,他们的研究表明,人工智能有助于“改善客户情绪,减少了对管理层干预的需求,提升了员工的留任率。”

研究人员写道:“我们的总体研究结果表明,与人类一起工作的生成式人工智能可以对员工个人的生产力和留任率产生重大的积极影响。”

他们说,“据我们所知”,他们的研究是首个针对现实环境中生成式人工智能对生产力的影响进行的研究。

自从最近几个月,OpenAI的聊天机器人ChatGPT成为现象级流行文化以来,人工智能在工作中的作用已经成为热门话题。

虽然一些员工担心这项技术可能会取代自己,但包括IBM的老板阿尔温德·克里希纳在内的许多著名首席执行官则认为,员工应该关注如何“与人工智能携手共事”。

沃尔玛(Walmart)的首席人事官唐娜·莫里斯最近在《财富》杂志的一篇专栏文章中写道,像人工智能这样的技术实际上是在“为员工赋能”,而比尔·盖茨则预言,几年后,每个人都将拥有自己的“白领”虚拟助手。

“1,000%的游戏规则改变者”

位于迪拜的人工智能房地产平台Realiste的创始人亚历克斯·加尔采夫向《财富》杂志表示,麻省理工学院/斯坦福大学的研究发现生产率的提高“确实意义重大,不应该被低估”。

“我们要认识到,即便是生产率的微弱提升也会对企业运营产生重大影响,从而提高效率和盈利能力。”他在一封电子邮件中写道:“由于将人工智能引入了本公司的工作流程,我们看到公司生产力大幅提升。通过利用人工智能为客户识别市场上最好的物业,我们的投资经理的生产力提高了200%。我坚信,只要使用正确的方法和工具,许多其他行业也能够实现这种水平的进步。”

加尔采夫说,他相信未来两年到三年内,对人工智能的使用和创新会增加,他认为这项技术将变得更加用户友好,更容易融入到商业运营中。

他说:“通过实现重复型任务的自动化、简化决策过程、提供独特的见解,这些工具对生产力的提升作用将是传统手段难以或不可能实现的。”

与此同时,英国人工智能咨询公司Deeper Insights的首席执行官及创始人杰克·汉普森表示,他的公司及客户见证了比研究中记录的14%更大的生产率提升。

他在4月24日的电话采访中告诉《财富》杂志,像ChatGPT这样的人工智能工具“1,000%”将改变职场的游戏规则。

汉普森表示,Deeper Insights合作的一家人力资源公司开始使用类似ChatGPT的大型语言模型协助起草策略、检索策略信息、搜索最新的职场案件判例后,生产率提高了20%至40%。

他补充说,Deeper Insights本身也在使用ChatGPT等工具来完成部分工作,比人工的速度要快得多。

“我们显然需要大量数据,而很多时候我们没有足够的客户数据来训练人工智能。”汉普森解释说:“所以我们现在使用ChatGPT和其他大型语言模型来创建替代训练数据集,这为我们节省了大量时间和金钱。在一个为期三周或三个月的项目中,我们之前可能会花大约两周到三周的时间来创建训练数据集,而现在只需要一两天。”

生成式人工智能研究的作者在论文中强调,他们的研究“不是为了阐释生成式人工智能工具对总就业或工资的影响”。

他们说:“我们的研究结果并不涉及人工智能可能对技能需求、工作设计、工资或客户需求产生的长期影响。”但他们指出,他们的研究确实提出了一个问题,即员工向人工智能系统提供的数据是否应该得到补偿,以及如何得到补偿。他们补充道,生产率的提高可能是“由于人工智能系统得以将公司高水平员工的实践经验收录到系统中。”(财富中文网)

译者: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.”

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