包阅导读总结
1. 关键词：Gen AI、Workloads、Productivity、Upwork、Survey
2. 总结：Upwork 研究发现，虽多数高管预期生成式 AI 能提高生产力，但多数员工称其实际降低了生产力。调查了美、英等国 2500 名工作者，指出员工生产力下降的因素。该调查引发争议，有人质疑其可信度和方法，多数学术研究倾向于 AI 与生产力正相关。
3.
– 主要内容：
– Upwork 研究发现预期与实际生产力情况相悖
– 96%高管预期生成式 AI 工具能提高生产力，77%员工称实际降低了生产力
– 调查情况
– 调查了美、英、澳、加 2500 名工作者，包括高管、全职员工和自由职业者
– 不同群体观点
– 部署 AI 工具的公司，81%领导认为公司生产力提高，仅 42%未采用工具的公司领导这么认为
– 约一半使用 AI 的高管认为公司落后于对手，50%认为员工技能和采用度导致生产力停滞
– 员工生产力下降因素
– 花更多时间审查、学习使用工具，被要求做更多工作
– 相关建议和争议
– Upwork 提出改善 AI 采用的措施
– 调查引发网络批评，包括对调查来源和方法的质疑，也有人强调 AI 工具的局限性和管理中对其理解不足，多数学术研究倾向于 AI 与生产力正相关
思维导图：
文章来源：infoq.com
作者：Sergio De Simone
发布时间：2024/7/29 0:00
语言：英文
总字数：630字
预计阅读时间：3分钟
评分：77分
标签：生成式 AI,生产力,员工培训,AI 采用,Upwork
以下为原文内容
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A controversialsurvey by Upwork Research Institute found that while 96% of C-suite leaders expect the use of generative AI tools to increase overall productivity levels, 77% of surveyed employees say they have actually decreased their productivity. In fact, the survey contradicts previous research showing a positive correlation.
Upwork Research surveyed 2,500 workers across the US, UK, Australia, and Canada, including C-suite executives (50%), full-time employees (25%), and freelancers (25%).
The picture provided by executives contradicts strikingly what employees report. Indeed, 81% of leaders at companies that deployed AI-based tools believe the overall company productivity increased, compared with only 42% of leaders at companies that did not adopt AI-based tools. Yet, many leaders expect more:
One in two executives at companies using AI believe their company is falling behind their competitors (51%) and that their workforce’s overall productivity levels are stalled due to lack of employee skills and adoption (50%).
Despite their expectations about the benefits of using AI tools, approximately three-quarters of surveyed executives admit they have no training plan in place for their workforce, and only 13% maintain they developed a well-implemented strategy. Instead, AI adoption seems to be emerging bottom-up, thanks to early adopters and innovators in the workforce.
This is somewhat coherent, with 47% of surveyed workers saying “they have no idea how to achieve the productivity gains their employers expect”. Interestingly, executives believe the opposite, with 37% of them saying their workforce is highly skilled and comfortable with these tools. The reality is only 17% of employees feel they are.
Surveyed workers list several factors that contribute to their productivity loss:
For example, survey respondents reported that they’re spending more time reviewing or moderating AI-generated content (39%), invest more time learning to use these tools (23%), and are now being asked to do more work (21%).
This leads Upwork researchers to explain these data through the well-known “productivity paradox”, referring to the slowdown in US productivity during the 70s and the 80s while IT tech adoption was rapidly climbing.
By deploying new technology—no matter how exciting and full of potential—without updating our organizational systems and models, we risk creating productivity strain […]. We risk another productivity paradox with generative AI if we don’t fundamentally rethink the way we work.
Among the measures that Upwork suggests to improve AI adoption are leveraging non-traditional talent, co-creating productivity metrics, and moving toward skill-based approaches rather than job roles.
Echoed by Forbes’ contributor Bryan Robinson, who writes, “scientific findings released today contradict those expectations and re-ignite apprehensions about AI’s impact on employee overload and burnout”, the survey has raised some criticism on the Internet.
The first factor of criticism is the source of the survey itself. Being Upwork, a marketplace for freelancers, several commentators see a credibility problem with a survey that makes freelancers appear more attractive to large organizations. Another reason for concern is the lack of a full explanation of the survey methodology.
Others are more open to accepting the survey as trustworthy, but tend to highlight the limits of AI tools themselves, saying,”AI models (generative or not) are useful in specific cases, not all cases. Failing to acknowledge that and failing to strategise accordingly only leads to short term success and long term pain”, or even the lack of understanding of AI tools in management: “The only people excited are people who simply do not know enough to know they are wrong”. It goes without saying that many commenters report an increase in their productivity and an actual workload reduction by automating small, repetitive tasks.
As a final note, it seems the majority of academic research on the topic tends to posit a positive correlation between the use of AI tools and productivity, but their analysis falls out of the scope of the present article.
