如何为人力分析专业人士创造职业道路-How to create career paths for people analytics professionals（续）文/David Green
文章导读
往期回顾：
Geetanjali在2017年9月在费城举行的人力分析与未来工作会议上发言要点回顾：
MERCK&CO.的人力分析团队
这个团队由三支柱组成：咨询、高级分析、报告和数据可视化
创建一个数据驱动的文化：高层领导的支持对于人员分析功能的成功至关重要
在人力分析中创造职业道路：一个能够提供发展和职业发展的组织和领导者，可以成为吸引和留住人才的关键因素。
三“C”模式：Capability-Capacity-Connectivity
今日导读：
领导人员分析团队
问7、在谈到你作为一个人分析领导者的角色时，你会对这个角色的新手或者将来想成为一个人分析负责人的人提出什么建议呢?
分享五个我认为普遍适用的特性，并且对于成为这个领域的有效领导者很重要。
优先考虑：对于人员分析领导者来说，学习如何无情地优先考虑团队将花费时间和精力的项目是至关重要的。
位置: 一个好的领导者知道如何找到合适的机会去重新定位、结合和展示这项工作。这不仅对获得声望和对人员分析的认可很重要，而且对提升团队的士气也很重要。
连接: 当你建立起新的职业联系时，你也开始建立友谊，这是一个支持网络，可以帮助你在这个相当模糊的、新的人力分析空间中导航。
与时俱进：作为一个优秀的人员分析领导者，重要的一点是要跟上外部变化的步伐，并将这种学习带回您的业务中
发展:一个有效的领导者需要投入时间和精力来建立自己的内部和外部网络，并与他们的团队分享他们的进
问8、我观察到的一个挑战是，作为一个人分析的领导者，你必须平衡在内部构建能力的重大挑战，同时关注在外部快速发展的领域。作为一名分析人士的领导者，你如何平衡这两个优先事项，以及你如何了解公司外部发生的事情?
尽可能多地阅读各种不同的出版物(博客、文章、白皮书、书籍)，这些内容让我与人力分析的各个方面：从社会科学到人工智能都保持联系。
此外，与来自不同行业的其他从业者建立联系很有帮助，我通过非正式的和正式的对等网络进行联系。
最后，我试着每年参加一些活动来学习新的东西和认识新的人。
人力分析的未来
问9、你认为人力分析的主要趋势是什么?
我认为人力分析中的一些“热点领域”将在未来继续变得“更热”。
我还认为，随着研究的增长和越来越多的组织对这一领域的投资，网络的力量将得到充分的挖掘和释放。
最后，要实现所有这些类型的分析，最重要的领域之一将是关于数据使用、隐私和人员分析领域的安全性的伦理研究。
问10、我们如何平衡我们能做什么以及我们应该做什么？ 谈谈你对道德和隐私等方面的关注。
过度反应或倾向于采用过于保守的方法，这可能会妨碍人员分析领域的一些重要工作。
话虽如此，与适当的实践专家密切合作，就业法律、隐私法律、伦理、通信、业务合作，和工人委员会合作是一个很好的方式,以确保除了工作的合法性。
另一种从道德角度是预先与内部客户分享你分析的可能结果，并向他们清楚地说明在每个场景中他们将采取什么行动。
在人力分析领域工作类型需要把伦理放在最重要的日程上
英文原文：
LEADING THE PEOPLE ANALYTICS TEAM
7. Turning towards your role as a People Analytics Leader, what would your advice be to someone who is new to this role or who aspires to be a Head of People Analytics in the future?
I think everyone has different strengths and experiences, which means their approach will vary with regards to them proving successful as a people analytics leader. But based on my personal experiences and observations of others, I can share five attributes that I think apply universally and are important to being an effective leader in this space.
Prioritise: Whether you have a small or large people analytics team, it will never be big enough to meet all the demands of your clients, particularly as awareness of the team’s capabilities grow. So, it is critical for the people analytics leader to learn (and teach!) how to relentlessly prioritise the projects on which the team will spend its time and effort. A good rule of thumb is to think about the magnitude of business impact that an analysis has the potential to deliver, or a key relationship that it can help build in the business for future collaborations and sponsorship. Many teams even use formal prioritisation grids to help the process, but ultimately the leader needs to ensure that the criteria used to allocate resources to projects aligns with the vision and mission of the people analytics team (which in turn, should align with the objectives of the enterprise).
It is critical for the people analytics leader to learn (and teach!) how to relentlessly prioritise the projects on which the team will spend its time and effort.
Position: A critical skill for a people analytics leader is the ability to effectively position analyses before the right decision-makers at the right time to maximise positive outcomes and build a strong people analytics brand. This is probably one of, if not the most, important part of being a people analytics leader. On many occasions, brilliant workforce analyses have been underutilised in their original scope, but a good leader knows how to find the right opportunities to repurpose, combine and present this work. This is not only important in gaining prestige and recognition for people analytics, but also for boosting the morale of the team.
Connect: There is a small, but growing, community of people analytics leaders globally who collectively have a spectacular amount of experience and knowledge. Fortunately, this community is inclusive and generous, in terms of sharing their knowledge and connections with others in the field. The group is a great resource to learn about new technologies, techniques, vendors, and also receive tips and tricks that can help a new leader to avoid mistakes and grab the right opportunities. Most importantly, as you build new professional connections you also begin building friendships that are a support network to help you navigate this fairly ambiguous, new(ish) space of people analytics.
Evolve: Since a people analytics leader needs to have some depth in analytical methods, it is always a good idea to read, listen and learn. Thanks to social media there are amazing resources available, many of them free, that any analytics leader can and should leverage to keep oneself updated and evolving. There are some extremely prolific writers (like David Green!) who share both original and curated content on various forums including LinkedIn. Whether you are looking for detailed tutorials on advanced data science methods or want to learn about the latest technological breakthrough and its application to people data, there is a publication, podcast, or video out there on it. Another reason why this mind set of curiosity and awareness is important is because the people analytics space is sensitive primarily due to ethics and privacy reasons; and keeping a handle on that also demands a leader who keeps their eyes and ears open. An important part of being a strong people analytics leader is to keep up with the pace of change externally and bring that learning back to your business.
An important part of being a strong people analytics leader is to keep up with the pace of change externally and bring that learning back to your business
Develop: Last, but certainly not the least, a critical part of being a good people analytics leader is simply being a good leader. This implies being someone who invests in the development of their team. It is of particular importance because it is a space that has attracted a lot of exceptional talent, but still has somewhat limited opportunities for advancement. Therefore, an effective leader needs to invest time and effort in building their own internal and external network; and share it with their teams for their advancement. They should also be committed to actively finding or creating opportunities for their team members to learn new skills and develop themselves as multi-faceted professionals.
An effective leader needs to invest time and effort in building their own internal and external network; and share it with their teams for their advancement
8. One of the challenges I’ve observed in being a people analytics leader is that you have to balance the significant challenge of building capability internally whilst keeping an eye externally on what is a fast-developing field. As a people analytics leader, how do you juggle these two priorities, and how do you keep abreast of what is happening outside the organisation?
I strive to practice the same behaviours that I would advise new people analytics leaders to try. For example, I follow and subscribe to content by certain thought leaders in people analytics and read as many varied publications as possible (blogs, articles, whitepapers, books) which keep me connected to the different aspects of people analytics; from social science to artificial intelligence.
In addition, it really helps to connect with other practitioners in the field from different industries, which I do via both informal and formal peer networks. This helps to broaden one’s worldview, spark new ideas, and offers a forum to ask questions of your peers. Most likely, if you are facing a people analytics quandary, there is a leader out there who has faced it too and would be willing to share their experience.
Finally, there are a plethora of great conference events out there, and the quality and number of these keeps rising every year. I try to participate in at least a few such events every year to learn new things and meet new people.
THE FUTURE OF PEOPLE ANALYTICS
9. What do you believe will be the main trends moving forward in people analytics?
I think that a number of “hot areas” in people analytics will continue to get “hotter” in the future. The idea of employee experience will grow even wider with focus on the end-to-end experience all the way from being a prospective candidate stage to becoming an alumni of the company. This is likely to grow simultaneously with the focus on managing and optimising a new, fluid workforce that may at any one time be full-time and freelance, human and robotic.
I also think that the power of networks will be fully explored and unleashed as research grows and more organisations invest in this space. The applications of network analysis are so varied and relevant, that it should continue to gather steam in the future.
Finally, from my perspective to enable all these types of analyses, one of the most critical areas that will grow in importance will be the study of ethics relating to data use, privacy and security in the space of people analytics.
10. Finally, how do we balance what we can do with what we should do? How concerned are you about areas such as ethics and privacy?
This is a great question, and a difficult one to answer. The frontiers of what is possible are being pushed at a break-neck speed thanks to ever larger datasets being at our disposal faster, and at cheaper cost. And that pace makes it tough to process the implications in real time. In fact, this often leads to an overreaction or the inclination to adopt an overly conservative approach that can hamper some great work in the people analytics space.
That being said, I believe that an extremely important fact to understand about the space we work in is that we should not do something just because it is possible. Besides being legally compliant, the type of work being undertaken in this field needs to put ethics at the very top of the agenda even before beginning work on an analysis. Working closely with the appropriate experts in the practices of employment law, privacy law, ethics, communications, business partners and workers councils is a good way to ensure that besides the legality of the work, its potential impact on people is also being considered through the lens of ethics, privacy, and empathy. Most established organisations have extensive reviews involving these types of stakeholders already in place.
Another way to pressure test the approach from an ethics lens is to share possible outcomes of an analysis with the internal clients beforehand and ask them to articulate what actions they would take in each scenario. Obviously, this method is not possible in every situation, but when applicable it can be a useful “stop and reflect” moment.
The type of work being undertaken in the people analytics field needs to put ethics at the very top of the agenda
People Analytics
2018年07月31日
People Analytics
如何为人力分析专业人士创造职业道路-How to create career paths for people analytics professionals文/David Green
文章导读
根据德勤于2017年11月发布的“高影响力人力分析研究”(High-Impact People Analytics study), 69%的大型机构(10,000多名员工)现在拥有一个“人力分析团队”。
Geetanjali Gamel在旧金山举行的“人民分析与未来工作会议”(People Analytics & Future of Work Conference)上的演讲这个话题。Geetanjali是默克公司劳动力分析的全球领导者。在2017年9月在费城举行的人民分析与未来工作会议上发言。
为什么要人力分析?
问1、你好，Geetanjali，请解释一下吸引你到人力分析领域的原因。
我工作中最有趣的部分是理解、测量和预测人类行为及其对销售和收入等业务结果的影响。因此，我很自然地被这个机会所吸引，这个机会将科学的方法引入到人们的数据中，并帮助塑造一个组织如何为其投资者带来价值，同时为其员工带来更丰富的经验。
MERCK & CO.的人力分析团队
问2、请您描述一下默克公司的劳动力分析团队的规模和结构，以及它是如何与业务联系起来的。
默克的劳动力分析团队(WFA)拥有15名成员，在全球80多个市场，69000名员工。
这个团队由三个主要支柱组成：咨询、高级分析、报告和数据可视化。
咨询——每个咨询师都与我们的业务部门(如制造、研究、销售等)保持一致。他们与领导者紧密合作，以理解和预见棘手的业务问题，并运用正确的方法解决问题，将分析转化为可操作的观点。
高级分析——高级分析团队是一群灵活的数据科学家和专业人士，他们主要专注于需要高级技术技能或很有意义的项目。它们围绕业务问题进行组织。
报告和数据可视化——他们直接与来自业务各个部门的内部客户合作，以确保合适的人在合适的时间拥有合适的数据。驱动了内部客户满意度。
三个WFA团队紧密合作，以确保识别和利用业务活动之间的协同作用。
创建一个数据驱动的文化
问3、德勤(Deloitte)的“高影响力人物分析”(High-Impact People Analytics)研究发现，在创造高级能力方面，最重要的因素是需要创建数据驱动的文化。你在默克公司是如何做到这一点的?
我们首先在人力资源社区中推广数据，推出了一个基于云的劳动力分析平台。我们还开发和部署了一个能力构建程序，其中的模块主要集中在度量选择、假设测试、数据可视化、推荐开发等方面。
此外，我们一直在利用的另一个渠道，加速人力资源数据驱动文化，是让我们更广泛的人力资源社区的成员成为分析“冠军”。
最后，我们还建立了一个人力资源领导团队，在人力资源中传达建筑数据和分析能力的信息。
高层领导的支持对于人员分析功能的成功至关重要
在人力分析中创造职业道路
问4、您对为人力分析专业人员创建职业发展道路充满热情。 为什么你认为这是如此重要？
我热衷于为那些使人力分析成为可能的人们建立更好的工作体验！ 我发现这个团队能够为职业道路，继任计划和大型员工的人才流动等领域做出决策，但经常陷入无处可扩展的境地。
此外，大多数人分析团队都是人力资源部门的一员，而且往往被贴上高度专业化的“人力资源精英”卓越中心(CoE)的标签，这限制了横向或向上进入CoEs或业务部门的其他人力资源角色的机会。
最后，一个能够提供发展和职业发展的组织和领导者，可以成为吸引和留住优秀人才的关键因素。
如果我们能让更多人力分析人才流动起来，就会为人力资源和企业的其他部门增加技能、方法和拓宽视角，为企业创造额外的价值。
一个能够提供发展和职业发展的组织和领导者，可以成为吸引和留住优秀人才的关键因素
问5、关于人才分析团队的职业发展，你在默克制定了什么计划?关于人才分析团队的职业发展，你在默克制定了什么计划?
从我在默克公司工作的第一天起，我的首要任务之一就是了解我的团队的力量和抱负，并将他们的发展与他们的职业目标结合起来。我得出了一个Capability-Capacity-Connectivity模型，为我们的人员分析团队提供一个可持续发展项目。这种模式成功的一个关键驱动力是你的领导的支持和与其他团队的合作。
问6、职业发展计划的主要好处和收获是什么?
“3C”方法是围绕解决障碍和为人学分析团队创建促进职业发展的桥梁而构建的。
第一个“C”：能力，能力必须在两个级别上处理。
能力级别1：构建数据、技术和分析精明的客户
能力级别2：提升人员分析团队
第二个“C”：Capacity容纳度
如果没有时间远离日常的活动，就不可能专注于一个人职业生涯的下一步
第三个“C”：连接
将人员分析团队与其他人力资源，数据科学，技术和业务专业人员联系起来，建立对双方不同类型工作的认识和相互欣赏。
英文原文：
According to Bersin by Deloitte’s High-Impact People Analytics study, which was published in November 2017, 69% of large organisations (10,000+ employees) now have a people analytics team.
It is a surprise then that many organisations overlook the need to develop the careers of their people analytics team. Given the pace of evolution of the field and the high-demand for talent in the space, this is an oversight that needs correction.
As such, it was refreshing that the main focus of Geetanjali Gamel’s presentation earlier this year at the People Analytics & Future of Work Conference in San Francisco (see key learnings here) was on this very topic.
Geetanjali is the global leader of workforce analytics at Merck & Co., Inc. (NYSE: MRK, known as MSD outside the United States and Canada). I caught up with Geetanjali recently to ask how she has created career development paths for her team as well as discuss other related topics in the people analytics field.
Geetanjali Gamel speaking at the People Analytics & Future of Work Conference in Philadelphia in September 2017
WHY PEOPLE ANALYTICS?
1. Hi Geetanjali, please can you introduce yourself, describe your background and explain what attracted you to the people analytics space.
Like many of my colleagues in people analytics, I’ve had a non-linear path to my current role. I am a trained economist and began my career in research at the Federal Reserve Bank of St. Louis studying topics like macroeconomic forecasting, unemployment and inflation. With this foundation in social science methodology and research, I soon transitioned to business forecasting, predictive analysis and scenario-planning to drive customer growth and revenue projections in corporate planning and finance departments in the energy sector. The most intriguing part of my work was in understanding, measuring and predicting human behaviour and its impact on business outcomes such as sales and revenue. So, I was naturally attracted by the opportunity to bring scientific methodology to people data and help shape how an organisation can drive value for its investors along with enhanced experience for its employees. I began by building a predictive analytics function from scratch in HR in my previous role at Mastercard and since 2016 I have led the advanced workforce analytics, consulting and reporting organisation in Merck HR.
THE PEOPLE ANALYTICS TEAM AT MERCK & CO.
2. Please can you describe the size and structure of the workforce analytics team at Merck and how it aligns to the business
Merck’s workforce analytics team (WFA) has 15 members who support 69,000 employees in over 80 markets worldwide through a rich portfolio of people analytics products.
The team consists of three primary pillars; Consulting, Advanced Analytics, and Reporting & Data Visualisation (see Figure 1 below).
Figure 1: The Workforce Analytics team at Merck & Co (Source: Geetanjali Gamel)
Consulting - Each consultant is aligned to one of our business divisions like manufacturing, research, sales, etc. They work closely with leaders to understand and anticipate burning business questions, utilise the right methodology to find the answers; and convert the analyses into actionable insights.
Advanced Analytics - The advanced analytics team is a nimble group of data scientists and specialised professionals who focus mainly on ad hoc projects requiring advanced technical skills and/or initiatives of enterprise level significance. They are organised around business questions and may support several divisions at a time, in contrast to the end-to-end approach that the consultants take with each initiative.
Reporting & Data Visualisation – This team forms the backbone of all the amazing work we are able to do, as well as the internal customer satisfaction we drive. They work directly with internal clients from all parts of the business to ensure that the right people have the right data at the right time.
The three WFA teams work closely with each other to ensure that any synergies between business initiatives are identified and leveraged.
CREATING A DATA-DRIVEN CULTURE
3. The recent Bersin by Deloitte High-Impact People Analytics study found that the single biggest predictor in creating advanced capability is the need to create a data-driven culture. How have you achieved this at Merck particularly with regards to HR Business Partners and the wider HR function?
I agree that culture can be the strongest catalyst or impediment for people analytics. It is also ridiculously difficult to identify and alter, particularly because organisations at any given time tend to be collections of sub-cultures. But there are some patterns of behaviours, decision-making, and incentive-rewards, which distinguish data driven cultures from others. These behaviours can be purposefully incubated through a combination of upskilling, training and mind-set building.
At Merck, we believe that a leading HR function is one where analytics capability is not only for the analytics team, but the whole HR team. This does not imply that every role requires equal depth in analytics, but a new baseline of data interpretation and communication skills is critical to being effective partners to the business. To this end, we started out by democratising data within our HR community by rolling out a cloud based workforce analytics platform. This is helping us drive greater familiarity and reliance on data among our HR users. We have also developed and deployed a capability-building program with modules focused on metric selection, hypothesis testing, data visualisation, recommendation development, and more.
Another channel that we have been leveraging to accelerate a data driven culture in HR has been to engage members of our wider HR community as analytics “Champions”. These superheroes are critical to spreading the adoption of data informed insights, since they live and breathe the daily challenges of their colleagues; and can share relatable examples with their counterparts on how data can unlock value.
Finally, we also have an HR leadership team that is aligned and strong advocates in relaying the message of building data and analytics capability in HR. Needless to say, sponsorship of senior leaders is imperative to the success of a people analytics function.
Sponsorship of senior leaders is imperative to the success of a people analytics function
CREATING CAREER PATHS IN PEOPLE ANALYTICS
4. You are passionate on the need to create career paths for people analytics professionals. Why do you believe this is so important?
I firmly believe that the goal of people analytics is to drive value for the business as well as provide a better experience of work for employees. So naturally, I am equally passionate about building a better work experience for the people who make people analytics possible! I find a sad irony in the fact that the team which enables decision-making on areas like career pathing, succession planning, and talent movement for the larger workforce, is often stuck in a position of having nowhere to grow. From my discussions with many colleagues in this field, I have learned that the typical people analytics team usually tends to have a group of individual contributors (analysts, data scientists, consultants) and a director or senior director level leader. This leaves only one spot for the entire team to aspire to, at least for upward movement.
In addition, most people analytics teams sit within HR and tend to be branded as a highly-specialised “HR-lite” centre of excellence (CoE), which limits the opportunities to move laterally or upward into other HR roles in CoEs or business units. And this reality of being “boxed-in” can be very frustrating for bright, highly-employable individuals.
If you are a leader in people analytics, and if you have had to recently recruit new talent for your team, I would guess you are acutely aware of the gaping chasm between talent demand and supply in this field. In my opinion, an organisation and a leader who can offer development and career growth can be a key differentiator in attracting and retaining the best people analytics talent.
Broadening that vision, if we enabled more fluid movement of people analytics talent, it would add to the diversity of skills, approaches and perspectives to other parts of HR and the business, and would create additional value for the enterprise.
An organisation and a leader who can offer development and career growth can be a key differentiator in attracting and retaining the best people analytics talent
5. What program have you put into place at Merck regarding the career development of the people analytics team?
From the first day of my role at Merck, one of my top priorities was to understand the strengths and aspirations of my team and align their development to meet their career goals. After multiple discussions and numerous iterations on ideas, I arrived at a Capability-Capacity-Connectivity model to power a sustainable development program for our people analytics team. The underlying idea is that if we can build the right capability within the analytics team and its clients; reallocate capacity that is being consumed by suboptimal tasks; and drive connectivity between people analytics teams and other parts of the business; then we can potentially discover and create new career paths and opportunities. But please bear in mind that a key driver of success for such a model is sponsorship from your leaders and partnership with other teams. In our case, we were fortunate to have both. This has empowered us to be inventive and co-create development opportunities for our team.
6. Please can you provide more detail on what comprises each of the Capability, Capacity and Connectivity elements of this approach. What have been the key benefits and learnings from the career development program?
The “3C” approach is built around tackling barriers and creating bridges that promote career development for people analytics teams. At the outset we knew that the team was faced with a high volume of requests needing significant manual effort. (see Figure 2 below):
Figure 2: Challenges in accelerating maturity in people analytics (Source: Geetanjali Gamel)
Since the day-to-day work was time and effort intensive, there was not much room to hone more sophisticated skills or build knowledge sharing relationships with others, leaving the people analytics team stuck in a loop. So, we put careful thought and purpose into adopting the following model.
Capability
The first “C”, or capability, had to be addressed at two levels. The first was to empower our broader HR team with the right tools and training to have greater autonomy to perform analyses. We moved to an intuitive analytics platform and organised workshops, office hours, and learning sessions to improve data literacy among our internal HR clients. This type of effort is important to free-up time for the people analytics team to build their own skillset (and path to growth), while also creating a greater awareness in other parts of HR about analytics.
Figure 3: Capability - Level 1: building data, technology and analytics savvy clients (Source: Geetanjali Gamel)
The second area of capability building had a more direct impact on the team. We held a team strategy session where we identified areas that needed focus for internal functional, technical and strategic competency building. These focus areas were carefully selected to create dual impact – provide us with a skill or knowledge we could use immediately in our work; and more importantly, help us practice a new behaviour that would develop us as well-rounded professionals. For example, on the technical side, we organised an in-house R-training curriculum, created and delivered by some of our own colleagues to the rest of the team. This helped us build a technical skill we could immediately put to use to do better work, and also built coaching and confidence skills for those who led the program. Another great example was of an external guest speaker series that we launched, which brought recognition to the team for bringing new insights to the company, and also helped the team gain experience in organising an event successfully end-to-end.
Figure 4: Capability - Level 2: Upskilling the people analytics team (Source: Geetanjali Gamel)
Capacity
At first, capacity building measures may not sound like a natural fit with developing career paths. But it is impossible to focus on the next steps in one’s career if there is no time to step away from the daily barrage of activity to have a conversation; listen to a webinar; learn about a new project; or simply, chat with colleagues over lunch. As such creating capacity for the team is critical to allow them to develop their skillset to be more widely applicable, as well as to build the networks they need to find new opportunities.
As mentioned before, our journey began with democratising data and providing a range of workforce metrics and even results of our enterprise voice survey in accessible cloud platforms to our HR community. We continue to supplement our efforts to empower our internal clients, and in the process unlock capacity for our team, by forming global communities of practice for analytics. Another effort to scale our analytics delivery and save precious time has been by finding opportunities to utilise process automation on repeatable tasks.
It is impossible to focus on the next steps in one’s career if there is no time to step away from the daily barrage of activity
Connectivity
Despite efforts in building capability and reallocating capacity, there can’t be much career development if there is nowhere to go! This is when the third “C” of connectivity comes into play. In fact, it could just as easily be C for creativity, because we need a great deal of innovative thinking and risk taking to create opportunities where they don’t always exist.
We started with small yet effective steps rather than trying to construct huge, formal programs. Connecting the people analytics team with other HR, data science, technology, and business professionals builds an awareness and appreciation for different types of work on both sides. We leveraged opportunities to co-create part-time assignments with other teams, participate in cross functional events, invite guest speakers to team meetings, and collaborate on projects to expose the team to other areas of analytical work.
Connecting the people analytics team with other HR, data science, technology, and business professionals builds an awareness and appreciation for different types of work on both sides
To create development assignments for the people analytics team we were creative and went with “quasi-experiments”. The first was an opportunity for a team member to take on the role of an HR business partner on a part-time basis for a few, smaller client groups. This gave the individual an opportunity to apply their analytical skillset to the role and get much greater exposure than before to business clients and business issues. Such an experiment has a multiplier effect. Where typically a business partner track is not easily available to a people analytics professional, creating such an opportunity internally can open up a new career path. Moreover, even if the individual does not end up pursuing this new career direction at the end of the experiment, it is still a valuable learning experience for them to be in the shoes of their internal client, i.e., the HR business partner. Finally, it may help to lay the foundation for what I like to call the HRBP 3.0 model.
Where the original HRBP role had a heavy component of operational (and even transactional) work, the HRBP 2.0 model that many companies follow today aims at strategic business partners who enable key business decisions. The HRBP 3.0 model takes it a step further by envisioning an analytical HR business partner, who relies on both data driven insight and business acumen to support their client.
Another “experiment” in creating new career opportunities was a mini-assignment we created for one of our people analytics team members to lead a large, remote team in the service delivery space. This was a completely different line of work from people analytics, and was heavily focused on operational and organisational skills like identifying and escalating issues on short deadlines, supplier relationship management, building relationships with a variety of HR and non HR stakeholders, and leading a service centre team to drive customer satisfaction. Clearly, this would not be a typical career path for a people analytics professional, but that is exactly why we need to be bold and creative with such experiments. This assignment not only exposed the individual to a different type and pace of work, but also gave them an opportunity to bring their analytical skills to the table to significantly elevate the usage and interpretation of transactional data.
While many mature organisations have good-sized people analytics teams, there are still many where the teams are pretty lean. This model may work well for most purposes, but it usually limits the opportunities for team-members to have people management experience. This is not always necessary for upward mobility, but it many cases it is difficult to move upward without some kind of experience of leading a team. Keeping this in mind, we built more depth in our people analytics team, creating enterprise advanced people analytics and data visualisation and reporting sub-teams within the larger group, which are led by two of our team members. Taking a chance on subject matter experts and giving them the opportunity to lead and delegate not only helps to open up doors for them, it also gives them a chance to coach others on their team to be future experts and leaders.
Lastly, we also created a new learning analytics role on our people analytics team which is a step toward building greater synergies between people analytics and learning practices, but also our small contribution in creating a new capability (and career path!) that is still evolving in many organisations.
10 Trends in Workforce Analytics （英文）
Workforce analytics is developing and maturing. These are the 10 major trends for the near future.
1. From one time to real-time
Many workforce analytics efforts start as a consultancy project. A question is formulated (“How do our employees experience their journey?”), many people are interviewed, data is gathered, and with the help of the external consultants a nice report is written and many follow up projects to redesign the employee journey are defined.
A one-time effort is nice, but it might be more beneficial to develop ways to gather more regularly and maybe even real-time feedback from candidates, employees and other relevant groups.
The survey practice is changing. We see organizations using several approaches:
The classic annual or bi-annual employee survey, for a deep dive.
Weekly, monthly or quarterly pulse surveys to gather more frequent feedback. A few questions, often varying the questions per cycle. Some more advanced pulse survey solutions are adaptive: they ask more questions to people when they sense there are issues (“How was your week?”. If the answer is “Very Good”, the survey is finished, if you answer, “Not so good”, there are some follow-up questions). Pulse surveys can also be easily connected to the important “moments that matter” for the employee experience.
Continuous real-time mood measurement. Innovative solutions in this area are still scarce, especially if you want to measure in a passive non-obtrusive way. Keencorp is an example, they analyze aggregated e-mails and can report on the mood (and risks) in different parts of an organization.
In my article Employee mood measurement trends, you can find an extensive overview of mood measurement providers.
2. From people analytics to workforce analytics
Currently, the general opinion seems to be that people analytics is a better label than HR analytics.
Increasingly the workforce is consisting of more than just people. Robots and chatbots are entering the workforce. The first legal discussions have started: who is responsible for the acts of the robots?
If we’re also analyzing robots, we’re moving from people analytics towards workforce analytics. Robot wellbeing and robot productivity is a nice domain for HR to claim.
3. More transparency
This overview of workforce analytics trends cannot be complete without a reference to GDPR. GDPR is fueling a lot of positive developments, one of them being a lot more transparency. About what kind of data is collected, how it is used, and how algorithms are used to make decisions about people.
The issue of data ownership is related. It is expected that employees will no longer accept that they cannot own their own personal data. Employees need to have the possibility to show their data to their potential next employer as evidence for their productivity and engagement.
4. More focus on productivity
In the last years, there has not been a lot of focus on productivity. We see a slow change at the horizon.
Traditionally, capacity problems have been solved by recruiting new people. This has led to several problems. I have seen this several times in fast growing scale-ups.
As the growth is limited by the ability the find new people, the selection criteria are (often unconsciously) lowered, as many people are needed fast. These new people are not as productive as the existing crew. Because you have more people, you need more managers. Lower quality people and more managers lowers productivity.
Another approach is, to focus more on increasing the productivity of the existing employees, instead of hiring additional staff, and on improving the selection criteria.
Using workforce analytics, you can try to find the characteristics of top performing people and teams, and the conditions that facilitate top performance.
These findings can be used to increase productivity and to select candidates that have the characteristics of top performers. When productivity increases, you need less people to deliver the same results.
A related read on this topic are the 3 reasons to stop counting heads.
5. What is in it for me?
A lack of trust can influence many workforce analytics efforts. If the focus is primarily on efficiency and control, employees will doubt if there are any benefits for them.
Overall there is a shift to more employee-centric organizations, although sometimes you can doubt how genuine the efforts are to improve the employee experience.
Asking the question: “How will the employees benefit from this effort?” is a good starting point for most workforce analytics projects. It also helps to create buy-in, which becomes increasingly important with the introduction of the GPDR.
6. From individuals to teams to networks
Many workforce analytics projects today are still focused on individuals. What are the characteristics of our top performers? How can we measure the individual employee experience? How can we decrease absenteeism?
Earlier, I gave an overview to what extend current HR practices are focused on teams.
As you can see in the table, most of the practices are still very focused on the individual. Workforce analytics can help to improve the way teams and networks function in and across organizations. The rise of Organizational Network Analysis is one of the promising signs.
7. Cracks in the top-down approach
The tendency to implement changes top-down, is still common.
We like uniformity and standardization. In our central control room, we look at our dashboard, and we know we need to act when the lights are turning from green to orange.
HR finds it difficult to approach issues in a different way. Performance management is a good example. Changing the performance management process is often tackled as an organization-wide issue, and HR needs to find the new uniform solution.
In line with the trend called “the consumerization of HR”, employees are expected to take more initiative. Employees are increasingly tired of waiting for the organization and HR, and want to be more independent of organizational initiatives.
If you want feedback, you can easily organize it yourself, for example with the Slack plug-in Captain Feedback. A simple survey to measure the mood in your team is quickly built with Polly (view: “How to measure the mood in your team with Slack and Polly“). Many employees are already tracking their own fitness with trackers like Fitbit and the Apple Watch.
Many teams primarily use communication tools as WhatsApp and Slack, avoiding the officially approved communication channels. HR might go with the flow, and tap on to the channels used, instead of trying to promote standardized and approved channels.
How can workforce analytics benefit from the data gathered by on their employee’s own devices? If it is clear, what the benefits are for employees to share their data, they might be able to help to enrich the data sets and improve the quality of workforce analytics.
8. Ignoring the learning curve
In their book “Making HR measurement strategic”, Wayne Cascio and John Boudreau presented an often-quoted picture, with the title “Hitting the “Wall” in HR measurement”. The wall was the wall between descriptive and predictive analytics.
There are many more overviews with the people analytics maturity levels. Generally, the highest level is predictive analytics.
Patrick Coolen of ABN AMRO Bank recently mentioned a next level: continuous analytics, and he introduced a second wall, the wall between predictive analytics and continuous analytics.
As predictive analytics seems to be the holy grail, many HR teams want to jump immediately to this level. Let’s skip operational reporting, advanced reporting and strategic analytics. We can leapfrog, ignore the learning curve, and jump to the highest level in one step.
For many teams, ignoring the learning curve does not seem to be a sensible strategy. Maybe it is better to learn walking before you start running.
9. Give us back our time!
Recently I spoke to HR professionals from big multinationals who were involved in a “Give us back our time” projects.
In their organizations, the assignment to all staff groups was: stop using (meant was: wasting) more and more time of the employees and managers, please give us some time back!
An example that was mentioned concerned performance management. In this organization, they calculated that all the work around the performance management process for one employee costed manager and employee around 10 hours (preparation, two formal meetings per year, completing the online forms, meeting with HR to review the results etc.).
By simplifying the process (no mandatory meetings, no forms, no review meetings, just one annual rating to be submitted per employee by the manager), HR could give back many hours to the organization – to the relief of both managers and employees.
Big HR systems generally promise a lot. But before the system can live up to the high expectations, a lot of work needs to be done. Data fields must be defined. Global processes must be standardized. Heritage systems must be dismantled.
This results in a lot of work (and agony), for employees, for managers, for HR and for the implementation partners (who do not mind).
Workforce analytics can help a lot in the “give-us-time-back” projects, for example by some simple time-measurement. Measure the time a sample of managers, employees, and HR professionals spend on different activities, and estimate the value these activities optimizes the core activities of the organization (e.g. serving clients and bringing in new clients).
10. Too high expectations
The expectations of workforce analytics are often too high. Two elements must be considered.
In the first place, human behavior is not so easy to predict, even if you have access to loads of people data.
Even in domains where good performance is very well defined and where a lot of data is gathered inside and outside the field, as for example in football, it is very difficult to predict the future success of young players.
Secondly, the question is to what extend managers, employees and HR professionals behave in a rational way. All humans are prone to cognitive biases, that influence the way they interpret the outcomes of workforce analytics projects. Some interesting articles on this subject are why psychological knowledge is essential to success with people analytics, by Morten Kamp Andersen, and The psychology of people analytics, written by myself.
A more general thought: what if you replaced ‘Workforce analytics’ with ‘Science’? What is the role of science in HR? The puzzle is, that there are many scientific findings that have been available for a long time but that are hardly used in organizations.
Example: it has been proven repeatedly, that the (unstructured) interview is a very poor selection instrument.
But still, most organizations still rely heavily on this instrument (as people tend to overestimate their own capabilities). Why would organizations rely on the outcomes of workforce analytics, when they hardly use scientific findings in the people domain?
An interesting presentation on this topic that I recommend is by Rob Briner, titled evidence-based HR, what is it and is it really happening?
There’s a lot that’s changing in the world of work. These are the 10 trends in workforce analytics that I’m seeing today and that will likely impact the way we work in the near future.
This article is based on a keynote I gave at the Workforce Analytics Forum in Frankfurt, Germany, on April 18, 2018.
by Tom Haak
Tom Haak is the director of the HR Trend Institute The HR (Human Resources) Trend Institute follows, detects and encourages trends. In the people and organization domain and in related areas. Where possible, the institute is also a trend setter. Tom has an extensive experience in HR Management in multinational companies. He worked in senior HR positions at Fugro, Arcadis, Aon, KPMG and Philips Electronics. He holds a master’s degree in Psychology. Tom has a keen interest in innovative HR, HR tech and how organizations can benefit from trend shifts. Twitter: @tomwhaak
People Analytics
2018年06月27日
People Analytics
人工智能与自动化在HR与未来劳动力中的影响和应用文|Soumyasanto Sen
来源|Digital HR Tech
人工智能（AI）几十年来一直在改变我们的生活，但今天它的存在感比以往都要大得多。有时候，当一个新的人工智能驱动的系统，工具或产品出现并超越我们人类时，我们甚至都没意识到这个事实。事实上，人工智能正在影响着各种各样的人类生活，从以下几个方面来看：
繁琐，耗时的任务的自动化;
人类能力的增强和;
人类功能的放大。
“虽然这种AI技术的大部分使用目前非常简单，但它正在彻底改变我们的日常生活; 无论是职业上还是个人生活中。”
然而，人力资源和劳动力的人工智能和自动化的好处并不是即时产生的。这是一段旅程，人们可以看到自动化过程的短期收益，增强的中期收益以及最终扩大人类活动或任务的长期收益。
让我们更详细地看看人工智能和自动化对人力资源和劳动力的各种影响。首先，我们来看看历史上是怎么说的，以及这种向人工智能和自动化的转变如何持续了很长时间。之后，我们将探讨我们如何采用这项新技术，以及作为一个组织前进的基本策略是什么，同时将潜在威胁转化为机遇。
人工智能与人力资源自动化：影响与现状
如今人工智能无处不在，关于它如何影响工作的未来，则需要考虑很多方面。
“现在它几乎可以渗透到每一个软件中，”德勤的Bersin负责人兼创始人Josh Bersin说。根据德勤Bersin的研究，近40％的公司仅在人力资源部门就会使用某种形式的AI。
据Personnel Today介绍，38％的企业已经在他们的工作场所使用人工智能，62％的人希望早在今年就开始使用。根据德勤的Bersin，33％的员工预计在不久的将来他们的工作将会增加与AI的协作。
人工智能存在于几乎所有主要行业，从医疗保健到广告，交通，金融，法律，教育以及现在也在我们的工作场所。
我们已经越来越多地在个人生活中使用聊天机器人和虚拟助手，现在我们也可以期望在工作场所中使用它们。例如，AI协助我们找到新工作，回答常见问题，或接受辅导和指导。在组织中使用AI可以帮助我们创建更加无缝，更灵活，更偏向用户驱动的员工体验。
让我们看看劳动力日常生活中的典型工作日，以便我们清楚地看到AI的一些十分常见的实际用途。
清晨在家
许多智能家居设备具有了解您的行为模式并且帮助您节省资金的能力。像Nest恒温器有助于增加日常便利性并节省能源。
Amazon Alexa，Siri，Google Now和Cortana都是各种平台上的智能数字私人助理。 “今天的交通情况如何？”，“我的日程安排是什么？”，“提醒我在十点钟给X先生打电话”，这些助理的反应非常迅速。
在去办公室的路上
我们可能已经看到有人在驾车上班时阅读报纸（尽管目前风险很大）。但是自动驾驶汽车正在变得越来越有效率; Google的“WA YMO“和特斯拉的“Autopilot”就是两个很好的例子。
在赶着进入办公室时，没有时间找到你选择的新音乐？ Spotify使用深度学习来创建最终的个性化播放列表，并根据用户的预先聆听行为提供新的音乐。
下午在办公室
海明威（Hemingway）应用程序使用简单的人工智能，通过自然语言处理来识别书写问题，并打磨您的书写结构。它有助于节省时间并提高可读性。
现在我们不需要因为那些有语言障碍的会议感到困扰了。目前，Skype的翻译器使用8种语言，文本翻译人员可以使用超过50种语言进行即时消息传递。
在电话会议上记笔记有时很困难。Clarke.ai 是一个人工智能机器人，可拨入您的电话会议并完成整个笔记为您工作。然后，当通话结束时，它会直接将电子邮件发送到您的收件箱。
我们通常会在我们的收件箱中堆放一堆电子邮件，即使不包括垃圾邮件。Google的智能回复功能使用机器学习功能来分析您的电子邮件，并给出您可能想要发送的快速，简短的回复的建议。
离开办公室的时间
为你的团队找到合适的人选并非易事。Paradox使用Olivia作为AI助理，让你专注于整个候选人管理。 VCV是一个负责招聘的人工智能机器人 ，它可以搜索候选人，给他们打电话并利用语音识别功能询问问题，然后邀请他们录制视频面试。Glider是另一个类似的例子，它会将你的招聘放在AUTO-PILOT上。
需要为你的直接报告人推荐课程，但无法腾出时间？ SAP SuccessFactors，Comertone和许多其他公司已经提供类似的功能，以推荐基于个人职业生涯跟踪和绩效的课程。
在回家的路上
忘了安排明天的会议？ AI公司x.ai推出了“Amy”，这是一个虚拟个人助理，可以自动执行安排会议的过程。
想在到达你家之前买东西但不记得了吗？ Capitan是您在使用时自动学习的智能购物清单，为您节省时间并避免错过的物品。
晚上在家
一旦你到家，需要放松。Netflix根据您表达的兴趣和您过去做出的判断推荐电视剧和电影。
不需要花费时间来搜寻为周末或假期购买的东西，毕竟你已经非常疲劳了。亚马逊的预期航运项目希望在您需要某些物品之前就向您送来它们。
The North Face是IBM Watson平台，以更具吸引力，个性化和相关购物体验的方式为你寻找一件最完美的夹克。
这些只是几个例子。无论您是否意识到，AI已经对我们的日常（工作）生活产生巨大影响。对于我们大多数人来说，人工智能技术正在帮助我们更有效地完成工作，并且通常使我们的生活和工作更加轻松。
因此，AI在改变人力资源和劳动力方面发挥着重要作用;减少人为偏见，提高候选人评估效率，改善与员工的关系，改进可塑性，提高度量标准的采用率以及改善工作场所学习都是组织今天正在经历的一些好处。
珍妮·梅斯特（Jeanne Meister）在她的文章“工作的未来：人工智能和人力资源的交叉点”中指出，HR领导者如何开始尝试人工智能的各个方面，为他们的组织提供价值。据她介绍，HR领导者正在开始试点人工智能，通过使用聊天机器人进行招聘，员工服务，员工发展和辅导，为组织提供更大的价值。
到目前为止，招聘和人才挖掘是AI解决方案中最有效的领域。越来越多的把HR作为目标的创业公司和服务提供商将基于AI的解决方案用于以下活动：
采购（例如Textio）;
面试（myInterview）;
入场（Talla）;
教练（Saberr）和;
员工服务中心（ServiceNow）。
“目前，这些针对HR和劳动力的基于人工智能的解决方案更像是由数据驱动的分析产品，并且由下一代People Analytics提供支持。”
谈到HR领域的AI时，根据Bersin的说法，“人工智能的应用基本上都是分析应用，软件使用的历史、算法和数据会随着时间变得越来越智能。”人们分析的最有趣部分是人工智能和人工熟练程度之间的接口。
AI投资呈指数增长。研究公司IDC预测，人工智能市场将从2017年的125亿美元增长到2020年的460亿美元，会影响几乎所有行业的所有业务实践。
麦肯锡研究院在其2017年1月的报告“未来如何工作：自动化，就业和生产力”中提到，先进的机器人技术和人工智能等自动化技术是促进生产力和经济增长的强大动力，有助于创造经济盈余，增加整体社会繁荣。
根据麦肯锡的说法，自动化可以使全球经济的生产力每年提高0.8到1.4个百分点;假定被自动化取代的人力工作重新加入劳动力队伍的话。
另一方面，他们的自动化分析发现各个经济部门以及这些部门的职业之间存在显著差异。考虑到影响自动化速度和程度的技术，经济和社会因素，麦肯锡估计，目前的工作活动中高达30％可能会在2030年前取代。
“麦肯锡估计，到2030年，目前的工作活动中高达30％可能会被取代”
当人工智能及其对就业和经济的影响的话题出现时，谈话的主要焦点曾经是蓝领工作。根据CB Insights和State of Automation Report，仅在美国就有4600万零售销售人员因AI而面临失业风险。同样的事情发生在430万厨师和服务员，380万清洁工，2.4M搬运工和仓库工人，180万卡车司机和120万建筑工人。
根据CB Insights的观点，越来越多的AI注入式专家自动化和增强软件（EAAS）平台将引领我们迈向AI辅助和/或AI增强生产力的新时代。这些EAAS平台使用机器智能来复制和增强人类的理解和认识。
这种AI增强的生产力也开始威胁白领工作，比如影响到律师，人力资源，教师，销售，市场营销，研究人员，会计师，软件开发人员等大多数常见职业。
“AI和自动化是否会夺走我们的工作？这个问题在过去曾多次被提出，只要我们能够为自己的未来而努力，答案就是'不'。然而，我们可以期望我们的工作有结构性转变。”
历史和转变
现在许多使用的AI和机器学习的算法已经存在数十年了。近半个世纪以来，先进的机器人，自动驾驶汽车和无人驾驶飞行器（UAVs）已被国防机构使用。
技术一直引发人们对大规模失业的担忧。自称解决主义者，promethean兼设计师Louis Anslow在他的出版书籍“Robots have been about to take all the jobs for more than 200 years”中解释了这一反应。在20世纪30年代，他被称为经济学家约翰梅纳德凯恩斯（John Maynard Keynes），将技术作为大萧条失业的一个原因。因此，这一直是一个热门话题。
BBC Capital最近发表了对未来工作毫无根据的担忧的历史，并在其中指出，早在1959年，数学家I.J. Good的预测到，“科学技术的所有问题都将交给机器，人们不再需要工作”。
麦肯锡研究所最近发表的另一篇文章“工作的未来会对就业，技能和工资意味着什么”表明，这种技能转移或就业流离失所现象并不新鲜。
左图标题：1850到2015年间，美国各行业部门的员工总数份额
右表标题：1850到2015年间，就业的改变
第一次工业革命始于18世纪的英格兰，欧洲，美国和其他国家的经济自那以后经历了两次剧烈的结构变革。机械化推动了农业和工业的革命，鼓励工人从农村迁移到城市。过去60年来发生了第二次结构性转变，一些国家制造业的就业份额下降，而服务业的就业份额开始增长。
根据麦肯锡的研究，伴随这一结构转型过程的就业转移十分剧烈。在整个行业中大量劳动力转移的情况下，整体就业人数占总人口的比例普遍持续增长。
像美国，中国，印度，德国，日本和巴西这样的全球经济体将比印度尼西亚，韩国，土耳其等新兴经济体受到的影响更大。人工智能和自动化的影响依赖于国家的收入水平，人口和产业结构。
期望与现实
那么，人工智能和自动化将使我们的工作自动化吗？
“到目前为止，人工智能和机器人不是用来”自动工作“，而是用来”自动化任务“和”增强“人类功能，从而提高生产力和性能。”
我们大多数日常工作都与文书工作，日程安排，时间表，会计，费用等任务相关（平均百分比如下所示）。 当然，将这些重复的任务外包给数字助理或自动化软件是非常有用的，从而腾出更多的时间进行深入的思考和创造。
当谈到如何利用当前市场上可用的人工智能认知技术，迄今为止他们的主要影响是扩大现有的工作职能，而不是消除工人。能够推理，学习并与人自然互动的机器或系统可能会继续消除重复性任务，帮助员工更好，更快地完成工作，腾出时间完成更有趣的任务。
对于大多数劳动力来说，认知技术可能使他们能够进入新的、更有价值的角色。 因此，大多数组织及其员工可能会从基于AI的技术和自动化中体验到积极的影响。
人类未来研究所（FHI）、耶鲁大学、牛津大学和政治科学部门（Department of Political Science）实际上揭示了一个问题——人工智能会超越人类的表现吗？
根据他们的研究，机器超越人类的时间将会非常长。如果所有任务都是成本效益更高的机器，那么AI将会产生深远的社会影响。
他们的调查采用了以下定义：“高级机器智能”（HLMI）是在无人帮助的机器能够比人类工作者更好，成本更低地完成每项任务的情况下实现的。
例如时间线显示实现所选AI里程碑的概率为50％。具体而言，时间间隔表示从25％到75％的事件发生概率的日期范围。
应用和战略
从所有这些分析中可以清楚地看到，在可预测的环境中（包括生产工人，建筑和地面清洁工）涉及（很多）体力工作的职业以及办公室辅助人员（如文员和行政助理）可能会因人工智能和自动化而开展的活动面临重大影响。另一方面，医生和专业人士，比如工程师和商业专家则不太可能经历太多的影响。
目前的职业教育需求水平往往与这些活动自动化的可能性呈正相关。比起那些只需要高中文凭和一些经验的职业，需要高等教育的职业通常包含了自动化更少的工作内容。
“受自动化影响的工作人员很容易被识别出来，而由技术间接创造的新工作和技能组合的转变在各个行业和地区都不太明显，并且分布广泛。”
世界经济论坛“就业的未来”报告着眼于未来的就业，技能和劳动力战略。报告的作者向全球领先企业的首席HR主管和战略主管询问了目前的转变意味着什么，特别是针对跨行业和地域的就业，技能和招聘。
他们发现AI和自动化的最新发展将改变我们的生活方式和工作方式。一些工作会消失，另一些工作会增长，而今天根本不存在的工作将会变得司空见惯。可以肯定的是，未来的劳动力队伍需要调整其技能以跟上节奏。
未来技能
复杂的问题解决
批判性思维
创造力
人员管理
情绪智力
建立关系
谈判
认知灵活性
有风险的技能
记录和报告
行政的
体力劳动
可预测的分析
质量控制
校准
驾驶或骑马
信息收集
根据未来工作与消费研究员Laetitia Vitaud的观点，我们现代企业的大部分人力资源部门都已经成为把人当作资产一样管理、按照流程驱动的“机器”，而不需要关注个性化、独特的人。
相反，HR部门运行自上而下的流程设计'系统' - 招募大量人力资源，处理工资，组织年度评估，同时批量对员工进行培训等等 - 为员工的个性化，灵活性以及创造力留下少许空间。
Laetitia在她的出版物“AI能否将‘人’投入到人力资源？”中解释说，许多HR专业人员不了解的是，AI如何提供独特的机会来重新定义人力资源，并提升其相关性。
简而言之
因此，人力资源部门的关键是开发人工智能和自动化战略，首先要分析AI将会重新定义哪些工作角色，流程和工作流程。 Jeanne Meister在最近的文章“AI +人类智能是工作的未来”中指出，人们可以开始思考人工智能和自动化对工作任务，关键工作角色和工作流程的影响。你可以简单地开始问：
自动化：该角色中的哪些关键活动可以自动化以提供更高的效率和有效性来完成日常任务？
扩张：如何通过应用人员分析来确定新的业务洞察力以创建更好的战略规划和行动，从而创造更多价值？
放大：AI技术可以重新设计哪些工作过程和流程来促进人类活动和决策制定？
下图显示了HR和劳动力需要的AI战略所需的关键因素。基于这些基本原理和重要因素，我们便可以为企业及其（未来）人才创造价值主张。
AI战略中基本、重要的要素
基本原则
领先的正确思维
清晰的视野和商业案例
使用正确的管理方式
使用创新模式的COE
要素
领导力和整体方向
人才与变革管理
道德，合规和公正
扩展主动性和策略
技术不仅是创造最佳员工体验的关键推动力。有了正确的准备，HR部门的领导可以利用这些概念提供创新的文化。以最有效的方式实现数字化和自动化肯定会提高组织的人员绩效。
未来掌握在我们自己的手中，我们应该通过接受我们的未来是人类与机器之间的合作这一事实，来规划并实施必要的策略，为我们自己的美好未来做好准备。
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