Josh Bersin谈How To Create Talent Density 如何打造人才密度
Josh Bersin发表文章谈到：在过去几年里，我注意到大公司的表现开始不如小公司。我们现在看到苹果和谷歌都出现了这种情况，而微软应对这一挑战也有相当长的一段时间了。 随着公司的发展，帮助我们推动组织绩效的一个重要理念就是人才密度。这篇文章讨论了人才密度的概念，即公司中技能、能力和表现的质量和密度。强调传统的员工绩效评估模型已导致平庸。建议采用人才密度方法，包括招聘增加或乘数效应的人才，基于帕累托分布管理绩效，以及专注于赋权、反馈和领导力。文章强调，为了创新和市场竞争力，尤其在AI和技术进步的背景下，维持高人才密度的重要性。
In this (long) article, I want to talk about a new concept called Talent density. And as I pondered the concept I think it represents one of the more important topics in management. So I hope you find it as interesting as I do.
First of all, the concept of talent density, pioneered by Netflix by the way, is simple.
Talent Density is the quality and density of skills, capabilities and performance you have in your company.
So, if you have a company that is 100% high performers, you’re very dense. If you have a company that’s 20% high performers, you’re not very dense. It’s easy to understand, but hard to implement, because it gets to the point of how we define performance, how we select people to hire, how we decide who’s going to get promoted, how we decide who’s going to work on what project and how we’re going to distribute pay.
So before I explain talent density, let’s talk about the basic beliefs most companies have. Most organizations believe that they’re operating with a normal distribution or bell curve of performance. I don’t know why that statistical model has been applied to organizations, but it has become almost a standard policy. (Academics have proven it false, as I explain below.)
Using the bell curve, we identify the “mean” or average performance, and then categorize performance into five levels. Number ones are two standard deviations to the right and number fives are two standard deviations to the left.
The people operating at level one get a big raise, the people operating at level two get medium raise, the people operating at level three get an average raise, the people operating at level four get a below average raise and the people operating at level five probably need to leave. Lots of politics in the process, but that’s typically how it works.
As I describe in The Myth of The Bell Curve, these performance and pay strategies have been used for decades. And at scale they create a mediocrity-centered organization, because the statistics limit the quantity and value of 1’s. If you’re operating at 1 level and you get a 2, you’ll quit. If you’re operating at 3 level, you’re probably going to coast. You get my drift. And since the bulk of the company is rated 2 or 3, most of the managers are in the middle.
As the saying goes, A managers hire A people, B managers hire C people. So over time, if not constantly tuned, we end up with an organization that is almost destined to be medium in performance.
Now I’m not saying every company goes through this process, but if you look at the productivity per employee in large organizations it’s almost always below that of smaller organizations. Why? Because as organizations grow, the talent density declines. (Netflix, as an example, example, generates almost $3M of revenue per employee, twice that of Google and 10X that of Disney. And they are the only profitable streaming company, with fewer than 20,000 employees and a $240 billion market cap.)
The traditional model was fine in the industrial age when we had a surplus of talent, jobs were clearly defined, and most employees were measure by the “number of widgets they produced.” In those days we could swap out a “low performer” for a “high performer” because there were lots of people in the job market.
We don’t live in that world anymore. The world we now live in has sub 4% unemployment, a constant shortage of key skills, and a growing shortage of labor. And thanks to automation and AI, the revenue or value per person has skyrocketed, almost an order of magnitude higher than it was 30 years ago.
So we need a better way to think about performance in a world where companies with fewer people can outperform those who get too big. Look at how Salesforce, Google, Apple, who are essentially creative companies, have slowed their ability to innovate as they get bigger. Look at how OpenAI, who is a tiny company, is outperforming Google and Microsoft.
Today most businesses outperform through innovation, time to market, customer intimacy, or IP – not through scale or “harder work.”
How do we maintain a high level of talent density when we’re growing the company and hiring lots of people? Netflix wrote the book on this, so let me give you the story.
First, the hiring process should focus on talent density, not butts in seats. Rather than hire someone to “fill a role” we look for someone who is additive or multiplicative to the entire team. Hire someone that challenges the status quo and brings new ideas, skills, and ideas beyond the “job” as defined. Netflix values courage, innovation, selflessness, inclusion, and teamwork, for example. These are not statements about “doing your job as defined.”
Netflix’s idea is that each incremental hire should make everybody else in the company and everybody else in the team produce at a higher level. Now this is a threatening thing for an insecure manager because most managers don’t want to hire somebody that could take their job away. But that’s why we have this problem.
Second, we need to manage or create some type of performance management process that is built around the Pareto distribution (also called the Power Law) and not the normal distribution. In the Pareto distribution or the power law, we have a small number of people who generate an outsized level of performance, you can call it the 80/20 rule or the 90/10 rule. (20% of the people do 80% of the work)
Studies have shown that companies and many populations work this way, and it makes sense. Think about athletes, where a small number of super athletes are 2-3 better than their peers. The same thing is true in music, science, and entertainment. It’s also true in sales and many business disciplines.
Research conducted in 2011 and 2012 by Ernest O’Boyle Jr. and Herman Aguinis (633,263 researchers, entertainers, politicians, and athletes in a total of 198 samples). found that performance in 94 percent of these groups did not follow a normal distribution. Rather these groups fall into what is called a “Power Law” distribution.
In every population of human beings there are a few people who just have God-given gifts to outperform in the job, and they just naturally seem to be far better than everyone else.
Bill Gates once told the company that there were of the three engineers that he felt made the company of Microsoft. And I’ve heard this in many other companies, where one software engineer and the right role can do the work of 10 other people.
Now, this is not to say that everybody will fall into one level of the Pareto distribution. At a given point in time in your career, you may be in the 80% and over time, as you learn and grow and find the things that you’re naturally good at, you’ll end up in the 20%. But in a given company this is a dynamic that’s constantly taking place. And that’s what Netflix is doing – constantly working on talent density.
What does this mean for performance management? It means that in order to care for a population like this, we have to hire differently, avoid the bell curve, and pay high performers well. Not just a little more than everybody else, a lot more. And that’s what happens in sports and entertainment, so why not in business.
If you look at companies like Google, Microsoft, and others, there are individuals in those companies that make two to three times more than their peers. And as long as these decisions are made based on performance, people are fine with it.
What obviously does not work is when person making all the money is the person who’s the best politician, best looking, or most popular.
And that leads me to item three: In the Netflix culture there’s a massive amount of empowerment, 360 feedback, candor and honesty. You’ve probably read the Netflix culture manifesto: it’s all about the need for people to be honest, to speak truth, to give each other feedback, and to focus on judgement, courage, and accountability. Netflix only recently added job levels: they didn’t have job levels for many years.
Giving people feedback is a challenge because it’s uncomfortable. So this has to to start at the top and it has to be done in a developmental, honest way. This does not mean people should threaten or disparage each other, but we all need to know that at the end of a project or the end of the meeting it’s okay for somebody to tell us “here’s what was great about that and here’s what wasn’t great about it.”
One of the most important institutions in the world, the US military lives, eats and dies by this process. If you’re in the military and you mess something up, you can guarantee that somebody’s going to tell you about it, and you’re going to get some help making sure you don’t do it again. We don’t have life or death situations in companies, but we can certainly use this kind of discipline.
The fourth thing that matters in talent density is leadership and goal setting. One of the things that really gets in the way of a high performing company is too many individual goals, too many siloed projects and responsibilities and people not seeing the big picture.
If your goal setting and performance management process is 100% based on individual performance you are sub-optimizing your company. Not only does this work against teamwork, but there really isn’t a single thing in a company that anybody can do alone. So our performance management research continuously shows that people should be rewarded for both their achievements as well as that of the team. (Here’s the research to explain.)
Why is talent density important right now? Let me mention a few reasons.
First, we’re entering a period of low unemployment so every hire is going to be challenging. And thanks to AI, companies are going to be able to operate with smaller teams. What better time to think about how to “trim down” your company so it’s performing at its best?
Second, the transformations from AI are going to require a lot of flexibility and learning agility in your company. You want a highly focused, well aligned team to help make that happen. And while AI will help every company improve, your ability to leverage AI quickly will turn into a competitive advantage (think back about how web and digital and e-commerce did the same).
(I firmly believe the companies with the most ingenious applications of AI will disrupt their competitors. I’m still amazed at Whole Food’s hand recognition checkout process: I can see self-service coffee, groceries, and other retail and hospitality coming.)
Third, the post-industrial business world is going to start to devalue huge, lumbering organizations. Many big companies just need a lot of people, but as Southwest Airlines taught us long ago, it’s the small team that performs well. So if you can’t break your company into small high-performing teams, your talent density will suffer.
When the book is written on Apple’s $10 Billion car, I bet one problem was the size and scale of the team. We’ll see soon enough. By the way, I still recommend everyone read “The Mythical Man-Month,” which to me is the bible of organizing around small teams.
What if you’re a healthcare provider, retailer, manufacturer, hospitality company? Does talent density apply to you? Absolutely! Go into a Costco and see how happy and engaged the employees are. Then go into a poorly run retailer and you’ll feel the difference.
In my book Irresistible I give examples of companies who embrace what I call “the unquenchable power of the human spirit.” Nobody wants to feel like they’re underperforming. With the right focus on accountability and growth we can help everyone out-perform their expectations.
Now is a time rethink how our organizations work. Not only should we promote and reward the hyper-performers, the Pareto rule and Talent Density thinking encourage us to help mid-level performers learn, grow, and transform themselves into superstars.
Let’s throw away the old ideas of bell curve, forced distribution, and simplistic performance management. Companies that push for everlasting high performance are energizing places to work, they deliver outstanding products and services, and they’re great investments for stakeholders.
AI中文翻译：
在这篇篇幅较长的文章中，我想探讨一个被称为“人才密度”的新概念。思考此概念时，我认为它是管理领域中极其重要的议题之一。希望您能像我一样发现其趣味性。
首先，Netflix首创的“人才密度”概念其实很简单。
人才密度指的是公司内部技能、能力和表现的质量与密集程度。
换句话说，如果你的公司全是高绩效人才，那么你的“人才密度”就很高。如果只有20%是高绩效人才，那么你的“人才密度”就不高。这个概念虽然容易理解，但实际执行起来却颇具挑战，因为它涉及到我们如何定义绩效、招聘员工的标准、晋升决策、项目分配以及薪酬分配。
在详细解释“人才密度”之前，让我们先看看大多数公司的基本信念。许多组织相信，他们的员工表现遵循一个正态分布或钟形曲线。这个统计模型为何被广泛应用于组织之中，我并不清楚，但它几乎已成为标准做法。（实际上，如我下文将解释的，学术研究已证明这一模型是错误的。）
采用钟形曲线，我们确定平均表现（即“平均线”），然后将员工的表现划分为五个等级。表现最好的被归为一级，标准为右偏两个标准差；表现最差的被归为五级，左偏两个标准差。
一级表现者获得大幅度加薪，二级表现者获得中等加薪，三级表现者获得平均水平的加薪，四级表现者加薪低于平均，五级表现者可能就需要离开公司了。虽然这个过程充满了政治操作，但这就是它通常的运作方式。
正如我在《钟形曲线的神话》中所述，这些关于绩效和薪酬的策略已经使用了数十年。而且，当这些策略在大规模下实施时，它们会造成以平庸为中心的组织文化，因为这种统计方法限制了顶尖人才的数量和价值。如果你是一级表现者却被评为二级，你很可能就会选择离职。如果你是三级表现者，你可能就会选择安于现状。你应该明白我的意思了。而且，由于大部分员工的评级为二级或三级，大多数管理者也就处于中等水平。
常言道，A级的管理者招聘A级人才，B级的管理者则招聘C级人才。因此，如果不持续进行优化调整，组织最终几乎注定会变得中庸。
我并不是说每家公司都会经历这一过程，但如果你查看大型组织的员工生产率，通常都低于小型组织的生产率。为什么呢？因为随着组织规模的扩大，“人才密度”往往会下降。（以Netflix为例，其每名员工创造的收入几乎为300万美元，是Google的两倍，是迪士尼的十倍。他们是唯一盈利的流媒体公司，员工不足20,000人，市值2400亿美元。）
在工业时代，人才供过于求，工作职责明确，大多数员工的表现以“生产的产品数量”来衡量。那个时候，低绩效者可以轻松地被高绩效者替换，因为劳动市场上有大量的人才可供选择。
但我们不再生活在那个时代了。在我们现在的世界里，失业率低于4%，关键技能持续短缺，劳动力整体也日益减少。而且，得益于自动化和AI技术，每位员工创造的收入或价值比30年前高出了几个数量级。
因此，在一个人员更少的公司可以超越体量更大的公司的世界中，我们需要一种更好的绩效思考方式。看看Salesforce、Google、Apple这些本质上依靠创新的公司，随着规模扩大，它们的创新能力如何变缓。再看看OpenAI，尽管是一个小公司，却在超越Google和Microsoft。
如今，大多数企业通过创新、市场响应速度、客户亲密度或知识产权而非规模或“更加努力的工作”来实现超越。
在我们不断发展公司并招聘大量人员的同时，我们如何保持高水平的“人才密度”？Netflix在此领域有着开创性的工作，让我来分享一下他们的故事。
首先，招聘过程应专注于提高“人才密度”，而不是仅仅为了填补空缺。我们寻找的不是简单地“填补一个角色”的人，而是能够为整个团队带来正面或倍增效果的人才。我们寻找的是那些能够挑战现状、带来新观点和技能，并超出传统“工作定义”的人。例如，Netflix重视勇气、创新、无私、包容和团队合作等价值观，并不仅仅是“完成既定工作”。
Netflix的理念是，每一次新增的招聘都应该使公司内每个人和团队的每个成员的生产力得到提升。这对于那些缺乏安全感的管理者来说可能是个挑战，因为大多数管理者并不希望招聘可能会取代他们的人。但正是这种思维方式导致了我们当前的问题。
其次，我们需要建立或改进一种围绕帕累托分布（也称作幂律分布）而非正态分布的绩效管理流程。在帕累托分布或幂律分布中，少数人贡献了超出常规的绩效水平，这可以称作80/20规则或90/10规则。（即20%的人完成了80%的工作）
研究显示，许多公司和人群实际上都是以这种方式运作的，这是合理的。想想那些在体育、音乐、科学和娱乐领域表现出色的人，其中少数顶尖人才的表现是同龄人的两到三倍。销售和许多商业领域也是如此。
2011年和2012年由Ernest O’Boyle Jr.和Herman Aguinis进行的研究（涵盖了633,263名研究人员、艺术家、政治家和运动员，共198个样本）发现，这94%的群体的表现并不遵循正态分布，而是呈现所谓的“幂律分布”。
在每个人群中，总有少数人因为天赋异禀，在工作中表现出色，自然而然地比其他人优秀得多。
比尔·盖茨曾经对微软说过，他认为公司中的三名工程师是公司的基石。我也在许多其他公司听到过类似的故事，其中一位软件工程师在合适的位置上可以完成其他十人的工作量。
这并不意味着每个人都将被归入帕累托分布的某一层级。在你职业生涯的某个阶段，你可能处于80%的群体中，但随着你不断学习、成长并找到自己真正擅长的领域，你最终可能进入20%的群体。但在任何一个公司，这种动态都在不断发生。这就是Netflix一直在努力提升“人才密度”的原因。
这对绩效管理意味着什么？这意味着，为了照顾这样一个群体，我们必须采取不同的招聘方式，避免使用钟形曲线，并且为高绩效者提供丰厚的薪酬。这不仅仅是支付比其他人稍微多一点的薪水，而是要多得多。这在体育和娱乐领域已经是常态，那么为什么不可以应用到商业领域呢？
如果你观察Google、Microsoft等公司，你会发现，这些公司中的个别人物赚取的收入是他们同事的两到三倍。只要这些决策基于绩效，大家通常都能接受它。
当然，不起作用的情况是，赚取高薪的是那些最擅长政治、外表最出众或最受欢迎的人。
这就引出了第三点：在Netflix的文化中，存在着大量的授权、360度反馈、直率和诚实。您可能已经读过Netflix的文化宣言，它强调人们需要诚实、坦诚、互相提供反馈，并专注于判断力、勇气和责任感。直到最近，Netflix才引入了职级制度——在很多年里，他们根本没有职级制度。
提供反馈是挑战性的，因为这会使人感到不适。因此，这个过程必须从高层开始，并以一种促进发展、诚实的方式进行。这并不意味着人们应互相威胁或贬低，但我们都需要明白，在项目结束或会议结束时，对方告诉我们“这是成功之处，这是失败之处”是完全可以接受的。
美国军队是世界上最重要的机构之一，它依靠这种过程生存、发展和克服困难。如果你在军队犯错，你可以确信会有人告诉你，并且你会得到帮助以确保你不会再犯同样的错误。虽然公司里没有生死攸关的情况，但我们完全可以借鉴这种纪律性。
在“人才密度”中很重要的第四点是领导力和目标设定。阻碍高绩效公司发展的一个常见问题是过多的个人目标、孤立的项目和职责，以及员工无法看到整体大局。
如果你的目标设定和绩效管理过程完全基于个人表现，那么你就在削弱你的公司。这不仅阻碍了团队合作，而且实际上没有什么是公司内任何人能够独立完成的。因此，我们的绩效管理研究不断表明，人们应该同时因其个人成就和团队成就而获得奖励。（这是相关的研究。）
为什么“人才密度”在当前尤为重要？我来列举几个原因。
首先，我们正处于一个失业率低的时期，因此每次招聘都将是一个挑战。而且，随着AI技术的帮助，公司将能够以更小的团队运作。在这样一个时刻，有什么比考虑如何“精简”你的公司、使其发挥最佳表现更合适的时机呢？
其次，随着AI的变革，你的公司将需要极大的灵活性和学习适应能力。你需要一个高度专注、良好协调的团队来实现这一目标。而且，尽管AI将帮助每个公司提高效率，但你快速应用AI的能力将变成一个竞争优势（回想一下网站、数字化和电子商务如何实现了同样的事情）。
（我坚信，那些能够巧妙应用AI的公司将会颠覆它们的竞争对手。我对Whole Foods的手掌识别结账过程仍感到惊讶：我预见到自助服务咖啡、杂货及其他零售和酒店业务的出现。）
第三，后工业时代的商业世界将开始贬低庞大、笨重的组织。许多大公司只是需要大量员工，但正如西南航空所示，小团队的表现通常更好。因此，如果你无法将你的公司划分为小型高效团队，你的“人才密度”将受到影响。
当有关Apple的100亿美元汽车项目的书籍编写时，我敢打赌问题之一将是团队的规模和规模。我们很快就会发现。顺便说一下，我还是推荐每个人阅读《神话般的人月》，对我而言，这本书是关于围绕小团队进行组织的经典之作。
如果你是医疗服务提供者、零售商、制造商或酒店业者，“人才密度”是否适用于你？当然适用！走进一家Costco，看看员工是多么的开心和投入。然后走进一个管理混乱的零售商，你就能感受到区别。
在我的书《不可抗拒》中，我列出了那些拥抱我所称之为“人类精神不可磨灭力量”的公司的例子。没有人愿意觉得自己表现不佳。通过适当的关注责任和成长，我们可以帮助每个人超越他们自己的期望。
现在是时候重新考虑我们的组织如何运作了。我们不仅应该提升和奖励顶尖表现者，帕累托法则和“人才密度”思维还鼓励我们帮助中等表现者学习、成长，并将自己转变为明星。
让我们抛弃旧的钟形曲线、强制分配和简单化绩效管理的想法。不断追求高绩效的公司是充满活力的工作场所，他们提供卓越的产品和服务，并且对投资者而言是极好的投资。
AI
2024年03月10日
AI
根据美世 2024 年全球人才趋势研究，高管认为人工智能是提高生产力的关键，但大多数员工尚未做好转型的准备Mercer's 2024 Global Talent Trends Study unveils critical insights from over 12,000 global leaders and employees, highlighting the increasing importance of AI in productivity, discrepancies between executive and HR perceptions, the necessity of human-centric work design, and the growing challenges in trust, diversity, and resilience within the workforce. The study emphasizes the urgency of adapting talent strategies to foster greater agility and employee well-being amidst technological advances and shifting workforce dynamics.
美世今天发布了2024年全球人才趋势研究。该研究借鉴了全球 12,000 多名高管、人力资源主管、员工和投资者的见解，揭示了雇主为在这个新时代蓬勃发展而采取的行动。
“今年的调查结果突显了工作中的惊人转变，”美世总裁帕特·汤姆林森 (Pat Tomlinson) 表示。“他们指出，高管层和人力资源部门对于 2024 年业务发展的看法存在显着分歧，而且员工对于技术影响的看法也存在滞后。随着我们迎来人机团队的时代，组织需要将人置于转型的核心。”
生成式人工智能 (AI) 被视为提高生产力的关键
生成式人工智能能力的快速增长引发了人们对劳动力生产力提升的希望，40% 的高管预测人工智能将带来超过 30% 的收益。然而，五分之三 (58%) 的人认为科技进步的速度超过了公司对员工进行再培训的速度，不到一半 (47%) 的人认为他们可以通过当前的人才模式满足今年的需求。
“通过人工智能提高生产力是高管们最关心的问题，但答案不仅仅在于技术。提高员工生产力需要有意识的、以人为本的工作设计。”美世全球人才咨询主管兼该研究的作者 Kate Bravery 说道。“领先的公司认识到人工智能只是其中的一部分。他们正在从整体的角度来解决生产力下降的问题，并通过新的人机协作模式提供更大的敏捷性。”
寻找通向未来工作的可持续道路面临着挑战。四分之三 (74%) 的高管担心他们的人才的转变能力，不到三分之一 (28%) 的人力资源领导者非常有信心他们能够使人机团队取得成功。提高敏捷性的关键是采用技能驱动的人才模型，这是高增长公司已经掌握的。
员工信任度全面下降
2023 年，对雇主的信任度从 2022 年的历史最高水平下降，这是一个危险信号，因为研究表明信任对员工的精力、蓬勃发展感和留下来的意愿产生重大影响。那些相信雇主会为他们和社会做正确事情的人，表示自己正在蓬勃发展、具有强烈的使命感、归属感和被重视感的可能性是其他人的两倍。
近一半的员工表示，他们希望为一个令他们感到自豪的组织工作，一些公司的回应是优先考虑可持续发展工作和“良好工作”原则。鉴于公平薪酬（34%）和发展机会（28%）是员工今年留下来的主要驱动力，雇主有动力在未来一年在薪酬公平、透明度和公平获得职业机会方面取得更快进展。
在全球范围内，员工都清楚，归属感有助于他们成长，但只有 39% 的人力资源领导者表示，女性和少数族裔在其组织的领导团队中拥有良好的代表，只有 18% 的人表示，最近的多元化、公平性和包容性努力提高了员工保留率关键多元化群体。四分之三的员工 (76%) 目睹过年龄歧视。由于这些挑战加上持续的技能短缺，更多地关注包容性和满足员工的需求将有助于所有员工蓬勃发展。
未来几年，韧性将至关重要
最近在风险缓解方面的投资已获得回报，64% 的高管表示他们的业务能够承受不可预见的挑战，而两年前这一比例为 40%。通货膨胀等近期担忧严重影响高管的三年计划，但网络和气候等长期风险可能没有得到应有的必要关注。
建立个人韧性与企业韧性同样重要，五分之四 (82%) 的员工担心自己今年会精疲力竭。为员工福祉重新设计工作对于缓解这一风险至关重要，51% 的高增长公司（2023 年收入增长 10% 或以上）已经这样做了，而低增长同行中只有 39% 这样做了。
员工体验是重中之重
超过一半的高管 (58%) 担心他们的公司在激励员工采用新技术方面做得不够，三分之二 (67%) 的人力资源领导者也担心他们在没有改变工作方式的情况下实施了新技术解决方案。员工体验是今年HR的首要任务；这是一个值得关注的问题，因为蓬勃发展的员工表示雇主设计的工作体验能够发挥他们的最佳水平的可能性是普通员工的 2.6 倍。
人力资源部门在改善所有人的工作方面发挥着关键作用，但人力资源部门越来越有必要与风险和数字化领导者合作，以按要求的速度引入必要的变革。为了满足组织和员工的期望，96% 的公司计划今年对人力资源职能进行一些重新设计，重点是跨部门交付和领先的数字化工作方式。
投资者重视敬业的员工队伍
今年，美世首次收集资产管理公司关于组织的人才战略如何影响其投资决策的意见。近十分之九 (89%) 的人将员工敬业度视为公司绩效的关键驱动力，84% 的人认为“流失和燃烧”方法会损害商业价值。投资者还表示，营造信任和公平的氛围是未来五年建立真正、可持续价值的最重要因素。
单击此处了解更多信息并下载今年的研究。
关于美世 2024 年全球人才趋势研究
美世全球人才趋势目前已进入第九个年头，汇集了来自 17 个地区和 16 个行业的 12,200 多名高管、人力资源领导者、员工和投资者的见解，该研究重点介绍了当今领先组织为确保人员长期可持续发展所采取的措施。在此过程中走得更远的组织在四个领域取得了长足的进步。(1) 他们认识到，以人为本的生产力需要关注工作的演变以及工作人员的技能和动机。(2) 他们认识到信任是真正的工作对话，通过透明度和公平的工作实践得到加强。(3) 随着风险变得更加关联且难以预测，他们认识到，提高风险意识和缓解水平对于建立一支准备就绪、有复原力的员工队伍至关重要。(4) 他们承认，随着工作变得越来越复杂，简化、吸引和激励员工走向数字化的未来至关重要。
关于美世
美世坚信，可以通过重新定义工作世界、重塑退休和投资成果以及释放真正的健康和福祉来建设更光明的未来。美世在 43 个国家/地区拥有约 25,000 名员工，公司业务遍及 130 多个国家/地区。美世是Marsh McLennan （纽约证券交易所股票代码：MMC）旗下的企业，Marsh McLennan 是风险、战略和人才领域全球领先的专业服务公司，拥有超过 85,000 名同事，年收入达 230 亿美元。通过其市场领先的业务（包括达信、Guy Carpenter和奥纬咨询），达信帮助客户应对日益动态和复杂的环境。
AI
2024年03月07日
AI
滴滴出行选用NICE，以提供基于实时 AI 的个性化服务NICE has partnered with DiDi Global to enhance customer and employee experiences through its cloud-based Workforce Management (WFM) and Employee Engagement Manager (EEM) solutions. This collaboration aims to streamline DiDi's global contact center operations, improving operational efficiency and customer satisfaction with AI-driven forecasting and scheduling. The implementation of NICE's solutions facilitates real-time management and self-scheduling for agents, boosting employee engagement and operational efficiency. DiDi's choice of NICE highlights the importance of advanced, flexible technology in supporting the dynamic needs of modern, app-based transportation services.
领先的移动出行平台通过利用 NICE 的客户体验 AI 技术，使其员工能够提供轻松且高效的客户服务体验
新泽西州霍博肯-NICE (纳斯达克: NICE) 今日宣布，滴滴出行已经选用了 NICE 劳动力管理 (WFM) 和员工参与管理 (EEM) 作为其云端创新技术的一部分。滴滴现在可以全面预测、规划和管理其全球客户联系中心的运作；同时提升运营效率和员工的参与度，并确保客服代表能够在首次通话中解决问题。Betta作为全球最大的 WFM 客户群之一的支持者，在实施过程中与 NICE 价值实现服务携手合作，负责执行集成，并在多国提供咨询、培训和支持服务。
滴滴出行寻求一种能够满足其核心业务、功能及技术需求，并能够随公司成长而扩展的劳动力管理解决方案。NICE WFM 结合了 AI 技术与灵活性，能够满足跨多个大洲、具有特定区域特色的运营需求，这不仅成本效益高，而且精确度高，确保维持最佳的服务水平。通过精准预测，确保在合适的时间有合适技能的代理人，从而大幅提升客户满意度。
通过引入 NICE EEM，可以实时解决人员配置需求，使得客服代理能够自我调节工作时间表，从而增强员工参与度和工作满意度。此外，利用智能日内自动调整功能，能够主动地进行调整，预防问题的发生。
滴滴出行国际客户体验执行总监 Caio Poli 表示：“基于多个考量因素，NICE 显然是我们的首选。我们寻找的是一个顶尖的云端劳动力管理解决方案，能够使我们的全球运营在保证运营效率和员工参与度的同时，提供卓越的客户体验。NICE 的智能日内自动化功能给我们留下了深刻印象，我们的选择是基于 AI 驱动的策略以及云技术的速度和灵活性。”
NICE 美洲总裁 Yaron Hertz 表示：“随着滴滴持续全球扩张，NICE 很高兴有机会为这家数字时代最具创新和活力的应用型运输公司之一提供服务。我们相信，通过采用 NICE 的 AI 驱动预测和机器学习来进行最适合的调度安排，对于联系中心和员工而言，这将有助于推动滴滴的未来发展。”
关于滴滴出行公司
滴滴出行公司是一个领先的移动技术平台，它在亚太地区、拉丁美洲及其他全球市场提供一系列基于应用的服务，包括网约车、叫车服务、代驾以及其他共享出行方式，还涵盖某些能源和车辆服务、食品配送和城市内部货运服务。滴滴为车主、司机和配送伙伴提供灵活的工作和收入机会，致力于与政策制定者、出租车行业、汽车行业及社区合作，利用 AI 技术和本地化智能交通创新解决全球的交通、环境和就业挑战。滴滴力图为未来城市构建一个安全、包容和可持续的交通与本地服务生态系统，以创造更好的生活体验和更大的社会价值。更多信息，请访问：www.didiglobal.com
关于 NICE
借助 NICE (纳斯达克: NICE)，全球各地不同规模的组织现在可以更容易地创造卓越的客户体验，同时满足关键的业务指标。作为世界领先的云原生客户体验平台 CXone 的提供者，NICE 是 AI 驱动自助服务和代理辅助客户体验软件领域的全球领导者，服务范围超出了传统的联系中心。超过 25,000 个组织在超过 150 个国家，包括 85 家以上的财富 100 强公司，都选择与 NICE 合作，以改造并提升每一次客户互动。www.nice.com
商标说明：NICE 和 NICE 标志是 NICE Ltd. 的商标或注册商标。所有其他标志属于它们各自的所有者。NICE 商标的完整列表，请访问：www.nice.com/nice-trademarks。
2024年人力资源预测：全球追求生产力In this fast-evolving era, all companies and individuals are seeking for change and efficiency. We can see the core of productivity in the new year: AI. Let's have a look at details on the Josh Bersin Predictions for 2024.
在过去的二十年里，我一直在写关于人力资源预测的文章，但今年不同。我看到这一年打破了范式，改变了商业中的每一个角色。不仅人工智能会改变每家公司和每一项工作，而且公司将开始不懈地寻求生产力。
想想我们的过去。2008年金融危机后，世界开始了加速增长的零利率时期。公司增加了收入，雇用了员工，并看着他们的股价上涨。招聘继续以狂热的速度进行，导致2019年底失业率创下3.5%的历史新低。
随之而来的是大流行，在六个月内，一切都停滞不前。2020年4月，失业率飙升至15%，公司将人们送回家，我们重新设计了我们的产品、服务和经济，以应对远程工作、混合工作制和对心理健康的关注。
一旦经济再次启动（由于美国的财政刺激），公司又回到了旧的招聘周期。但随着利率上升和需求不足，我们看到裁员一再发生，在过去的18个月里，我们看到了招聘、裁员，然后再次招聘以复苏经济。
为什么会出现跷跷板效应？
首席执行官和首席财务官正处于我们所说的“工业时代”——招聘以增长经济，然后在事情好转时裁员。
今天，当我们进入2024年时，一切都不同了。我们必须“囤积人才”，投资于生产力，并重新开发和重新部署人员以实现增长。
我们生活在一个失业率为 3.8% 的世界，几乎每个职位都存在劳动力短缺，劳动力权力日益增强，员工需求不断涌现：对加薪、灵活性、自主权和福利的要求。每年有超过20%的美国员工换工作（每月2.3%），其中近一半的变化是进入新行业。
为什么这是“新常态”？
有几个原因。首先，正如我们在全球劳动力情报研究中所讨论的那样，行业是重叠的。每家公司都是数字化公司；每家公司都希望建立经常性收入来源；很快，每家公司都将使用人工智能。过去停留在行业内的职业正在转变为“基于技能的职业”，让人们比以往任何时候都更容易跳槽。
其次，员工（尤其是年轻员工）感到有权按照自己的意愿行事。他们可能会悄悄地辞职，“做兼职”，或者抽出时间转行。他们看到自己的生活很长（人们的寿命比 1970 年代和 1980 年代长得多），所以他们不介意离开你的公司去其他地方。
第三，生育率持续下降，劳动力短缺加剧。日本、中国、德国和英国的劳动力人口都在萎缩。在未来十年左右的时间里，大多数其他发达经济体也将如此。
第四，工会正在崛起。由于华盛顿的新理念，我们看到了谷歌、亚马逊、星巴克、GM、福特、Stellantis、凯撒、迪士尼、Netflix等公司的劳工活动。虽然工会参与率不到美国劳动力的11%，但在欧洲要高得多，而且这一趋势正在上升。
这一切意味着什么？
这有很多影响。
首先，公司将更加专注于建立高保留率的工作模式（有人称之为“劳动力囤积”）。这意味着改善薪酬公平，继续混合工作模式，投资于以人为本的领导力，并为员工提供在公司内部从事新职业的机会。这就是为什么人才市场、基于技能的发展和工作流程中的学习如此重要的原因。
其次，CEO必须了解员工的需求、愿望和要求。正如爱德曼的最新研究表明的那样，职业发展现在位居榜首，同时对授权、影响力和信任的渴望也排在首位。我们称之为“员工激活”的新主题：倾听员工的意见，并将有关他们工作的决定委托给他们的经理、团队和领导者。
第三，传统的“雇佣成长”模式并不总是奏效。在这个后工业时代，我们必须系统地运作，将内部发展、工作再设计、经验和招聘放在一起。这汇集了招聘、奖励和薪酬、学习与发展以及组织设计等独立领域。（阅读我们的系统性人力资源研究了解更多信息。）
“业务绩效”的真正含义是什么？
如果你是首席执行官，你希望增长收入、增加市场份额、提高盈利能力和可持续性。如果你不能通过招聘来成长（而员工不断以奇怪的方式“激活”），你还有什么选择？这很简单：您可以自动化生产并专注于生产力。
虽然这张图表令人印象深刻，但它给每个CEO都引出了一个问题：我们在这张图表上的位置是什么？我们的运营速度是否与同行一样快、一样高效？
我认为这导致了一种我称之为“生产力优势”的策略。如果你能帮助你的公司更快地发展（生产力意味着速度，而不仅仅是利润），你就可以比你的竞争对手更快地进行重塑。这才是真正让CEO们夜不能寐的原因。
考虑一下普华永道最新的CEO调查数据。今年，我们必须比以往任何时候都更快地重塑我们的公司。到2024年，45%的CEO（去年为39%）认为他们的业务在十年内将无法生存。
生产力优势
为什么生产力如此重要？有四个原因。
首先，CEO们关心它。
2024 年普华永道 CEO 调查发现，CEO 认为公司 40%的工作浪费了生产力。
尽管这听起来令人震惊，但对我来说却是真实的：太多的电子邮件、太多的会议、混乱的招聘流程、官僚主义的绩效管理等等。（HR 就有其中一些问题。）
其次，AI让人生产力优势成为可能。
人工智能的应用旨在提高白领的生产力。（过去大多数自动化都有助于蓝领或灰领工人。）生成式 AI 让我们能够更快地查找信息，了解趋势和异常值，训练自己和学习，并清理我们随身携带的文档、工作流程、门户以及后台合规和管理混乱的系统。
第三，公司的发展需要AI。
当很难找招聘到人时，你将如何成长？去年，招聘时间增加了近20%，就业市场变得更加艰难。你能在技术技能上与谷歌或OpenAI竞争吗？
内部开发、重组和自动化项目就是答案。有了生成式人工智能，机会无处不在。
第四，生产力推动重塑。
如果你考虑重塑你公司（新产品、利用人工智能、进入新市场等）的需求，最大的障碍是惯性。为什么诺基亚和黑莓的手机业务输给了苹果？因为这些公司“又胖又快乐”。在这个人才和技能短缺的时代，这是灾难的根源。
普华永道（PwC）估计，“效率低下”产生了对GDP10万亿美元的税收，相当于全球GDP的7%。这种税收阻碍了您的公司转型。每当我们简化、减少会议并更好地定义决策权时，我们都会加快并实现变革。
这一切对人力资源意味着什么？
正如我在《人力资源预测》中所描述的那样，我们有很多问题需要解决。
我们必须加快向动态工作和组织结构的转变。我们必须专注于和务实地对待技能。我们必须重新思考“员工体验”，并处理我们所说的“员工激活”。我们将不得不对我们的人力资源技术、招聘和L&D系统进行现代化改造，以利用人工智能并使这些系统更加有用。
我们的人力资源团队也将由人工智能驱动。正如我们的Galileo™客户告诉我们的那样，一个架构良好的“专家助理”可以彻底改变人力资源人员的工作方式。我们可以成为“全栈”人力资源专业人员，在几秒钟而不是几周内找到有关我们团队的数据，几秒钟与一线领导分享人力资源、领导力和管理实践。（Galileo被一些世界上最大的公司用作管理教练。）
还有一些其他变化。随着公司专注于“通过生产力实现增长”，我们必须考虑每周 4 天的工作制，我们如何将混合工作制度化，以及如何以更有效的方式连接和支持远程工作者。我们必须重新关注领导力发展，在一线经理身上花费更多的时间和金钱，并继续投资于文化和包容性。我们必须简化和重新思考绩效管理，我们必须解决令人头疼的薪酬公平问题。
还有更多。
DEI 计划必须嵌入到业务中（人力资源 DEI 警察的时代已经结束）。我们必须清理我们的员工数据，以便我们的人工智能和人才情报系统准确且值得信赖。正如我们的系统性人力资源研究所指出的那样，我们必须将思维从“支持业务”转变为“成为有价值的顾问”，并将我们的人力资源服务产品化。
所有这些都在我们本周发布的40页新报告“2024 年人力资源预测”中进行了详细说明，其中包括一系列行动计划，以帮助您思考所有这些问题。
让我提醒你一个大观念。生产力是人力资源部门存在的原因。
我们所做的一切，从招聘到辅导，从开发到组织设计，只有在帮助公司成长的情况下才能成功。作为人员流动、敬业度、技能和领导力方面的专家，我们人力资源部门每天都在提高员工和组织的生产力。2024年是专注于这一更高使命的一年。
最后一件事：照顾好自己。
该报告有15个详细的预测，每个预测都有一系列需要考虑的行动步骤。最后一个真正适合你：专注于人力资源的技能和领导力。作为流程的管理者，我们必须专注于我们自己的能力。2024年将是成长、学习和团队合作的一年。如果我们处理好这15个问题，我们将帮助我们的公司在未来一年蓬勃发展。
Josh Bersin预测的详细信息
预测研究是我们每年阅读量最大的报告。它包括我们所有研究的详细摘要，并讨论了首席执行官、首席人力资源官和人力资源专业人士的15个基本问题。它将以以下形式提供：
包含详细信息的信息图。（点击这里)
Source JOSH BERSIN
AI
2024年02月01日
AI
HR Predictions for 2024: The Global Search For Productivity2024年的HR预测强调了生产力和AI在商业和雇佣实践中的关键作用。这篇文章讨论了公司在动态的经济条件和不断变化的劳动力市场背景下，如何适应他们的人才管理和招聘策略。强调了员工赋权的增加，劳动力市场的变化，以及技能发展的重要性。文章还探讨了劳动力囤积、混合工作模式和员工激活等关键概念。此外，还涉及领导力挑战、薪酬公平、DEI计划，以及可能的四天工作周。
一起来看Josh Bersin 带来新得见解
For the last two decades I’ve written about HR predictions, but this year is different. I see a year of shattering paradigms, changing every role in business. Not only will AI change every company and every job, but companies will embark on a relentless search for productivity.
Think about where we have been. Following the 2008 financial crisis the world embarked on a zero-interest rate period of accelerating growth. Companies grew revenues, hired people, and watched their stock prices go up. Hiring continued at a fevered pace, leading to a record-breaking low unemployment rate of 3.5% at the end of 2019.
Along came the pandemic, and within six months everything ground to a halt. Unemployment shot up to 15% in April of 2020, companies sent people home, and we re-engineered our products, services, and economy to deal with remote work, hybrid work arrangements, and a focus on mental health.
Once the economy started up again (thanks to fiscal stimulus in the US), companies went back to the old cycle of hiring. But as interest rates rose and demand fell short we saw layoffs repeat, and over the last 18 months we’ve seen hiring, layoffs, and then hiring again to recover.
Why the seesaw effect?
CEOs and CFOs are operating in what we call the “Industrial Age” – hire to grow, then lay people off when things slow down.
Well today, as we enter 2024, all that is different. We have to “hoard our talent,” invest in productivity, and redevelop and redeploy people for growth.
We live in a world of 3.8% unemployment rate, labor shortages in almost every role, an increasingly empowered workforce, and a steady drumbeat of employee demands: demands for pay raises, flexibility, autonomy, and benefits. More than 20% of all US employees change jobs each year (2.3% per month), and almost half these changes are into new industries.
Why is this the “new normal?”
There are several reasons. First, as we discuss in our Global Workforce Intelligence research, industries are overlapping. Every company is a digital company; every company wants to build recurring revenue streams; and soon every company will run on AI. Careers that used to stay within an industry are morphing into “skills-based careers,” enabling people to jump around more easily than ever before.
Second, employees (particularly young ones) feel empowered to act as they wish. They may quietly quit, “work their wage,” or take time out to change careers. They see a long runway in their lives (people live much longer than they did in the 1970s and 1980s) so they don’t mind leaving your company to go elsewhere.
Third, the fertility rate continues to drop and labor shortages will increase. Japan, China, Germany, and the UK all have shrinking workforce populations. And in the next decade or so, most other developed economies will as well.
Fourth, labor unions are on the rise. Thanks to a new philosophy in Washington, we’ve seen labor activity at Google, Amazon, Starbucks, GM, Ford, Stellantis, Kaiser, Disney, Netflix, and others. While union participation is less than 11% of the US workforce, it’s much higher in Europe and this trend is up.
What does all this mean?
There are many implications.
First, companies will be even more focused on building a high-retention model for work (some call it “labor hoarding.”) This means improving pay equity, continuing hybrid work models, investing in human-centered leadership, and giving people opportunities for new careers inside the company. This is why talent marketplaces, skills-based development, and learning in the flow of work are so important.
Second, CEOs have to understand the needs, desires, and demands of workers. As the latest Edelman study shows, career growth now tops the list, along with the desire for empowerment, impact, and trust. A new theme we call “employee activation” is here: listening to the workforce and delegating decisions about their work to their managers, teams, and leaders.
Third, the traditional “hire to grow” model will not always work. In this post-industrial age we have to operate systemically, looking at internal development, job redesign, experience, and hiring together. This brings together the silo’d domains of recruiting, rewards and pay, learning & development, and org design. (Read our Systemic HR research for more.)
What does “business performance” really mean?
If you’re a CEO you want revenue growth, market share, profitability, and sustainability. If you can’t grow by hiring (and employees keep “activating” in odd ways), what choice do you have? It’s pretty simple: you automate and focus on productivity.
Why do I see this as the big topic in 2024? For three big reasons.
First, CEOs care about it.
The 2024 PwC CEO survey found that CEO’s believe 40% of the work in their company is wasted productivity.
As shocking as that sounds, it rings true to me: too many emails, too many meetings, messy hiring process, bureaucratic performance management, and more. (HR owns some of these problems.)
Second, AI enables it.
AI is designed to improve white-collar productivity. (Most automation in the past helped blue or gray collar workers.) Generative AI lets us find information more quickly, understand trends and outliers, train ourselves and learn, and clean up the mess of documents, workflows, portals, and back office compliance and administration systems we carry around like burdens.
Third, we’re going to need it.
How will you grow when it’s so hard to find people? Time to hire went up by almost 20% last year and the job market is getting even tougher. Can you compete with Google or OpenAI for tech skills?
Internal development, retooling, and automation projects are the answer. And with Generative AI, the opportunities are everywhere.
What does all this mean for HR?
Well as I describe in the HR Predictions, we have a lot of issues to address.
We have to accelerate our shift to a dynamic job and organization structure. We have to get focused and pragmatic about skills. We have to rethink “employee experience” and deal with what we call “employee activation.” And we are going to have to modernize our HR Tech, our recruiting, and our L&D systems to leverage AI and make these systems more useful.
Our HR teams will be AI-powered too. As our Galileo™ customers already tell us, a well-architected “expert assistant” can revolutionize how HR people work. We can become “full-stack” HR professionals, find data about our teams in seconds instead of weeks, and share HR, leadership, and management practices with line leaders in seconds. (Galileo is being used as a management coach in some of the world’s largest companies.)
There are some other changes as well. As the company gets focused on “growth through productivity,” we have to think about the 4-day week, how we institutionalize hybrid work, and how we connect and support remote workers in a far more effective way. We have to refocus on leadership development, spend more time and money on first line managers, and continue to invest in culture and inclusion. We have to simplify and rethink performance management, and we have to solve the vexing problem of pay-equity.
And there’s more.
DEI programs have to get embedded in the business (the days of the HR DEI Police are over). We have to clean up our employee data so our AI and talent intelligence systems are accurate and trustworthy. And we have to shift our thinking from “supporting the business” to “being a valued consultant” and productizing our HR offerings, as our Systemic HR research points out.
All this is detailed in our new 40-page report “HR Predictions for 2024,” launching this week, including a series of Action Plans to help you think through all these issues.
And let me remind you of a big idea. Productivity is why HR departments exist.
Everything we do, from hiring to coaching to development to org design, is only successful if it helps the company grow. As experts in turnover, engagement, skills, and leadership, we in HR have make people and the organization productive every day. 2024 is a year to focus on this higher mission.
One final thing: taking care of yourself.
The report has 15 detailed predictions, each with a series of action steps to consider. The last one is really for you: focus on the skills and leadership of HR. We, as stewards of the people-processes, have to focus on our own capabilities. 2024 will be a year to grow, learn, and work as a team. If we deal with these 15 issues well, we’ll help our companies thrive in the year ahead.
Details on the Josh Bersin Predictions
The predictions study is our most widely-read report each year. It includes a detailed summary of all our research and discusses fifteen essential issues for CEOs, CHROs, and HR professionals. It will be available in the following forms:
Webinar and launch on January 24: Register Here (replays will be available)
Infographic with details: Available on January 24.
Microlearning course on Predictions: Available on January 24.
Detailed Report and Action Guide: Available to Corporate Members and Josh Bersin Academy Members (JBA). (Note you can join the JBA for $495 per year and that includes our entire academy of tools, resources, certificate courses, and SuperClasses in HR.)
AI
2024年01月19日
AI
Workday: It’s Time to Close the AI Trust GapWorkday, a leading provider of enterprise cloud applications for finance and human resources, has pressed a global study recently recognizing the importance of addressing the AI trust gap. They believe that trust is a critical factor when it comes to implementing artificial intelligence (AI) systems, especially in areas such as workforce management and human resources.
Research results are as follows:
At the leadership level, only 62% welcome AI, and only 62% are confident their organization will ensure AI is implemented in a responsible and trustworthy way. At the employee level, these figures drop even lower to 52% and 55%, respectively.
70% of leaders say AI should be developed in a way that easily allows for human review and intervention. Yet 42% of employees believe their company does not have a clear understanding of which systems should be fully automated and which require human intervention.
1 in 4 employees (23%) are not confident that their organization will put employee interests above its own when implementing AI. (compared to 21% of leaders)
1 in 4 employees (23%) are not confident that their organization will prioritize innovating with care for people over innovating with speed. (compared to 17% of leaders)
1 in 4 employees (23%) are not confident that their organization will ensure AI is implemented in a responsible and trustworthy way. (compared to 17% of leaders)
“We know how these technologies can benefit economic opportunities for people—that’s our business. But people won’t use technologies they don’t trust. Skills are the way forward, and not only skills, but skills backed by a thoughtful, ethical, responsible implementation of AI that has regulatory safeguards that help facilitate trust.” said Chandler C. Morse, VP, Public Policy, Workday.
Workday’s study focuses on various key areas:
Section 1: Perspectives align on AI’s potential and responsible use.
“At the outset of our research, we hypothesized that there would be a general alignment between business leaders and employees regarding their overall enthusiasm for AI. Encouragingly, this has proven true: leaders and employees are aligned in several areas, including AI’s potential for business transformation, as well as efforts to reduce risk and ensure trustworthy AI.”
Both leaders and employees believe in and hope for a transformation scenario* with AI.
Both groups agree AI implementation should prioritize human control.
Both groups cite regulation and frameworks as most important for trustworthy AI.
Section 2: When it comes to the development of AI, the trust gap between leaders and employees diverges even more.
“While most leaders and employees agree on the value of AI and the need for its careful implementation, the existing trust gap becomes even more pronounced when it comes to developing AI in a way that facilitates human review and intervention.”
Employees aren’t confident their company takes a people-first approach.
At all levels, there’s the worry that human welfare isn’t a leadership priority.
Section 3: Data on AI governance and use is not readily visible to employees.
“While employees are calling for regulation and ethical frameworks to ensure that AI is trustworthy, there is a lack of awareness across all levels of the workforce when it comes to collaborating on AI regulation and sharing responsible AI guidelines.”
Closing remarks: How Workday is closing the AI trust gap.
Transparency: Workday can prioritize transparency in their AI systems. Providing clear explanations of how AI algorithms make decisions can help build trust among users. By revealing the factors, data, and processes that contribute to AI-driven outcomes, Workday can ensure transparency in their AI applications.
Explainability: Workday can work towards making their AI systems more explainable. This means enabling users to understand the reasoning behind AI-generated recommendations or decisions. Employing techniques like interpretable machine learning can help users comprehend the logic and factors influencing the AI-driven outcomes.
Ethical considerations: Working on ethical frameworks and guidelines for AI use can play a crucial role in closing the trust gap. Workday can ensure that their AI systems align with ethical principles, such as fairness, accountability, and avoiding bias. This might involve rigorous testing, auditing, and ongoing monitoring of AI models to detect and mitigate any potential biases or unintended consequences.
User feedback and collaboration: Engaging with users and seeking their feedback can be key to building trust. Workday can involve their customers and end-users in the AI development process, gathering insights and acting on user concerns. Collaboration and open communication will help Workday enhance their AI systems based on real-world feedback and user needs.
Data privacy and security: Ensuring robust data privacy and security measures is vital for instilling trust in AI systems. Workday can prioritize data protection and encryption, complying with industry standards and regulations. By demonstrating strong data privacy practices, they can alleviate concerns associated with AI-driven data processing.
SOURCE Workday
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