SHRM观点：2018年HR必须关注的6个HRTech的发展趋势AI, bots and digital twins will shape the year.
Aliah D. Wright
2018年，随着人工智能（AI），机器人，预测软件和增强现实技术的重塑，物理和数字世界将继续融合。
首先接受人工智能将塑造组织环境，特别是智能系统学会适应用户的需求。“我们不再需要学习这些软件，”位于北卡罗来纳州Raleigh的技术公司WalkMe的总裁兼联合创始人Rephael Sweary说道，“AI已经在更多地了解我们的个人角色，行为和行动，以个性化我们使用人力资源和其他商业软件。“
根据研究公司Gartner发布的2018年度十大战略技术趋势， 企业平台也将发展为提供更自然和沉浸式的互动。
Sweary说，这样的进步将使人力资源专业人员能够显着减少学习和开发预算和资源，因为采用了可以根据情况指导人们如何使用任何系统的技术。
据Gartner称，2018年影响人力资源最多的六大趋势将是：
1.区块链。这项技术对于希望更有效地验证候选人的招聘人员具有希望，并且对于想要使其组织的全球薪酬流程成本更低且更及时的薪资管理者而言。区块链使用加密的公共记录的数字分类账，将公共记录结构化为称为区块的数据集群，并分散在网络中。这是一个功能强大的工具，用户可以找到可靠且易于浏览的工具 专家预测，HR将在未来18-24个月内开始使用区块链。
2. AI基础。据Gartner报道，制造自主学习，适应和行动的系统至少将成为技术供应商的重点。人工智能将用于改善决策制定，重塑工作流程并改善客户体验。它将推动到2025年数字商业计划的投资回报。
3.智能应用和分析。公司正在使用AI实践来制作新的应用类别，例如虚拟客户助理和机器人，以提高员工绩效，销售和营销分析以及安全性。智能应用有可能改变工作的性质和工作场所的结构。Gartner表示：“在构建或购买人工智能应用程序时，请考虑其影响将在如何完成，分析或改善用户体验的过程中发挥作用。”
4.物联网（IoT）。人工智能正在推动“智能”物品的进步，例如自动驾驶汽车，机器人和无人机。它还增强了许多现有产品，包括物联网（IoT）连接的消费和工业系统。例如，在某些时候，人力资源专业人员需要雇用可以操作无人机，监视无人机安全并遵守FAA规定的人员。
5.数字双胞胎。此工具是真实世界实体或系统的数字表示。来自多个数字双胞胎的数据可以汇总为真实世界实体的综合视图。例如，未来的人类模型可以提供生物识别和医疗数据，而数字双胞胎将允许进行高级模拟，报告解释道。数字双胞胎在物联网项目的背景下可以通过帮助用户响应变化，改进运营和提高性能，显着改善企业决策。
6.会话平台。想想Alexa或Siri。在人力资源部门内部，这些计划可以通过让员工与团队成员“交谈”来改善员工的自助服务。这些工具将推动人类与数字世界交互方式的下一个大范式转变。随着技术的成熟，“极其复杂的要求将成为可能，结果会非常复杂，”该报告指出。
准备就绪，设置，实施
人力资源领导者如何应对这些技术进步？Gartner分析师建议他们：
使用AI设计业务场景以通知新业务设计。
通过会话平台和增强现实创造更自然和身临其境的用户体验。
通过开发有针对性的高价值业务案例并确定优先次序来支持物联网举措，以构建数字双胞胎并协同开发云计算和边缘计算。
采用基于风险和信任的不断调整的安全和风险战略方法。
如果你不把这些技术趋势归因于你的创新战略，你就有可能失败。“包括数据科学家，开发人员和业务流程负责人在内的多个选区需要协同工作，”副总裁兼Gartner研究员David Cearley说。
Sweary预测，2018年将是人力资源的一个分水岭年，因为节省时间的技术将释放人力资源团队作为其组织内的战略顾问。
“数字化转型始于对员工的理解，HR将在调整公司文化，人才，结构和流程方面发挥关键作用，确保企业选择合适的工具来提供最佳员工数字体验。”
一个美丽的新世界
当Gartner公司的分析师凝视他们的水晶球时，他们看到了未来的情况：
到2019年
大多数领先的数字资产和产品信息管理系统将实施功能，允许品牌自动公开标签和元数据以改善语音和视觉搜索结果。
所有主要公司和零售商中有一半将重新设计其在线网站以适应语音搜索和语音导航。招聘委员会和招聘人员可以效仿。人才搜索引擎已经开始使用工具来帮助招聘人员找到并联系候选人或特定角色，方法是允许他们提出基于语音的搜索查询。
到2020年
人工智能创造的假冒内容将超过AI检测它的能力，这可能加剧不信任和错误信息的扩散。
到2021年
铁道部企业电子超过50％会花更多的每年创造的机器人和聊天机器人比传统的移动应用程序开发。
大多数 稳定经济体的人们会消费比真实内容更多的虚假信息。
到2022年
物联网（IoT）的所有安全预算中有一半将针对“故障修复，召回和安全故障”，而不是保护。
来源： 2018年前十大技术趋势 （Gartner Inc.）。
以上由AI翻译完成，仅供你参考。HRTechChina倾情奉献，转载请注明HRTechChina
Aliah D. Wright是SHRM的前任编辑，现在负责管理SHRM Speakers Bureau。
人力资源杂志Stephan Schmitz的插图。
n 2018, the physical and digital worlds will continue to merge, as the workplace is reshaped by artificial intelligence (AI), bots, predictive software and augmented reality.
Start by accepting that AI will mold the organizational landscape, especially as intelligent systems learn to adapt to users' needs. "We'll no longer need to learn the software," says Rephael Sweary, president and co-founder of WalkMe, a technology company based in Raleigh, N.C. "AI is already learning more about our individual roles, behaviors and actions to personalize how we use HR and other business software."
Enterprise platforms will also evolve to provide more natural and immersive interactions, according to the Top 10 Strategic Technology Trends for 2018 report from the research firm Gartner.
Such advancements will allow HR professionals to significantly reduce learning and development budgets and resources, as technologies are adopted that can contextually guide people on how to use any system, Sweary says.
The six trends that will affect HR the most in 2018, according to Gartner, will be:
1. Blockchain. This technology holds promise for recruiters hoping to verify candidates more efficiently, and for payroll managers who want to make their organization's global compensation process less costly and more timely. Blockchain uses an encrypted, digital ledger of public records structured into clusters of data called blocks and dispersed over networks. It is a powerful tool that users find reliable and easy to navigate. Experts predict HR will begin using blockchain within the next 18-24 months.
2. AI foundation. Making systems that learn, adapt and act autonomously will be a major focus for technology vendors through at least 2020, Gartner reports. AI will be used to improve decision-making, reinvent work processes and revamp the customer experience. It will drive the return on investment for digital business plans through 2025.
3. Intelligent apps and analytics. Companies are using AI practices to make new app categories, such as virtual customer assistants and bots to improve employee performance, sales and marketing analysis and security. Intelligent apps have the potential to change the nature of work and the structure of the workplace. "When building or buying an AI-powered app, consider where its impact will be in the process of how things get done, analysis, or to improve a users' experience," according to Gartner.
4. Internet of Things (IoT). AI is driving advances for "smart" items such as autonomous vehicles, robots and drones. It is also enhancing many existing products, including Internet-of-things (IoT)-connected consumer and industrial systems. At some point, for instance, HR professionals will need to hire individuals who can operate drones, monitor drone safety and comply with FAA regulations.
5. Digital twins. This tool is a digital representation of a real-world entity or system. Data from multiple digital twins can be aggregated for a composite view across real-world entities. For example, future models of humans could offer biometric and medical data, and digital twins will allow for advanced simulations, the report explains. Digital twins in the context of IoT projects could significantly improve enterprise decision-making by helping users respond to changes, improving operations and enhancing performance.
6. Conversational platforms. Think Alexa or Siri. Within HR, such programs could be applied to improve employee self-service by enabling employees to "talk" to members of your team. These tools will drive the next big paradigm shift in how humans interact with the digital world. As the technology matures, "extremely complex requests will be possible, resulting in highly complex results," the report states.
Ready, Set, Implement
How can HR leaders respond to these technological advancements? Gartner analysts recommend they:
Devise business scenarios using AI to inform new business designs.
Create a more natural and immersive user experience with conversational platforms and augmented reality.
Support IoT initiatives by developing and prioritizing targeted, high-value business cases to build digital twins and exploit cloud and edge computing synergistically.
Adopt a strategic approach to security and risk that continuously adapts based on risk and trust.
If you don't factor these technology trends into your innovation strategies, you risk losing ground. "Multiple constituencies, including data scientists, developers and business process owners, will need to work together," says David Cearley, vice president and Gartner Fellow.
2018 will be a watershed year for HR, Sweary predicts, because time-saving technology will free up HR teams to serve as strategic advisors within their organizations.
"Digital transformation starts with understanding your employees. HR will play a pivotal role in aligning company culture, talent, structure and processes to make sure that businesses select the right tools for delivering the best employee digital experience."
A Brave New World
When analysts at Gartner Inc. gaze into their crystal ball, here's what they see ahead:
By 2019
Most leading digital asset and product information management systems will implement features that allow brands to automatically expose tags and metadata to improve voice and visual search results.
Half of all major companies and retailers will redesign their online sites to accommodate voice searches and voice navigation. Job boards and recruiters may follow suit. Talent search engines are already working on tools to help recruiters find and contact candidates or specific roles by allowing them to pose voice-based search queries.
By 2020
AI-driven creation of fake content will outpace AI's ability to detect it, which could fuel distrust and the proliferation of misinformation.
By 2021
More than 50 percent of companies will spend more per year creating bots and chatbots than on traditional mobile app development.
Most people in stable economies will consume more false information than true content.
By 2022
Half of all security budgets for the Internet of Things (IoT) will be directed toward "fault remediation, recalls and safety failures," rather than protection.
Source: Top 10 Technology Trends for 2018 (Gartner Inc.).
人工智能正在改变人力资源工作方式
新技术和人工智能可用于提高绩效评估，开放招生和员工发展。
New technologies and artificial intelligence can be used to improve performance appraisals, open enrollment and employee development.
作者：Alexander Alonso，SHRM-SCP
像“终结者”电影中看到的那些机器的兴起可能会给我们灌输对人工智能（AI）和自动化的健康恐惧，但明智的人力资源专业人员会关注当今的发展如何能够产生积极的变化 - 即更高的效率在日常运营中和更好的员工体验。
现代技术（从简化流程的应用程序到改善通讯的机器人）正在改变我们的工作方式，这并不奇怪。然而，令人震惊的是，他们扩散到工作场所的速度很快。
以下是三个已被企业完全接受的AI示例，它们正在改变我们实践HR的方式：
众包和性能数据。为了更好地评估，商业思想领导者鼓励使用来自各种来源的及时数据。例如GloboForce这样一家员工识别软件供应商，声称众包信息比传统的评估方法能够以更定期的间隔提供更全面的性能图片。
乍一看，这可能看起来很直观。但是许多人力资源专业人士对这种软件在考虑到大量信息的性能数据流方面的准确性持怀疑态度。例如，会议结束后，Karma Notes向与会者询问个人作为团队成员的有效性。令人生畏的是，应用程序在每次会议后提出了这个问题。更重要的是，这个过程引发了人们提供反馈动机的问题。有些可能是由隐藏的议程驱动的。这项技术正在得到进一步完善，以收集与截止日期和预算有关的信息。近100家财富1000强公司正在试用这种众包的表演系统。这比以往任何时候都更加重视人力资源专业人员，以更好地理解数据管理和分析，
机器人和福利问题。如果你像大多数人力资源从业人员一样，只需要在开放的招生季节中生存下去，你很高兴。但那些幸运地通过人力资源信息系统（HRIS）来利用人工智能的人通常并没有那么糟糕。例如，今天的一些基于HRIS的聊天机器人可以自动回复员工的福利问题，并为您的员工量身定制解决方案。这意味着您花费更少的时间进行查询。虽然这些工具从来都不是完美的，但大多数使用的是一种AI，它使得信息交付非常可定制。要充分利用这一点，您必须建立真正动态的面向消费者的问答数据库，以反映您的员工和他们的偏好。
算法和学习偏好。近年来，我们看到了无数支持学习和发展活动的技术的兴起。其中最有趣的是使用AI来创建交互式测试和评估以匹配考生的个人学习风格和参与度的应用程序。与Lumosity的互动式大脑游戏类似，这些工具可在用户学习时产生无数的数据点，包括他们的步伐和学习风格。对于人力资源部门来说，这些创新突出了对员工发展的定制学习路径和数据驱动方法的需求。
很明显，人工智能在人力资源中的作用越来越大，这代表了您通过数据实现价值的机会。有些人会哭，“机器正在接管！”事实是，机器已经在这里。我们需要确定如何最好地使用它们。
SHRM-SCP的Alexander Alonso是SHRM知识发展高级副总裁。
以上由AI翻译完成，仅供你参考。HRTechChina倾情奉献，转载请注明HRTechChina
以下为英文原文：
The rise of machines like those seen in the “Terminator” movies may instill in us a healthy fear of artificial intelligence (AI) and automation, but wise HR professionals will focus on how today’s developments can give rise to positive changes—namely, greater efficiency in day-to-day operations and a better employee experience.
It’s no surprise that modern technologies—from process-streamlining apps to communication-improving bots—are altering the way we work. What is shocking, however, is the fast pace of their diffusion into the workplace.
Here are three examples of AI that have been fully accepted in businesses today and are changing the way we practice HR:
Crowdsourcing and performance data. For better appraisals, business thought leaders encourage the use of timely data from a wide array of sources. Companies such as GloboForce, an employee recognition software provider, claim that crowdsourced information provides more-holistic pictures of performance at more-regular intervals than traditional appraisal methods.
At first glance, that may seem intuitive. But many HR professionals are skeptical about the accuracy of such software with regard to performance data flow, which takes into account large volumes of information. For instance, after a meeting, Karma Notes asks fellow attendees about an individual’s effectiveness as a team player. What’s daunting is that the app poses this question after every meeting. What’s more, the process raises questions about people’s motivations for providing feedback. Some may be driven by a hidden agenda. The technology is being further refined to gather information related to deadlines and budgets, too. Almost 100 Fortune 1000 companies are piloting this type of crowdsourced performance system. More than ever, that puts the onus on HR professionals to better understand data management and analytics, and to account for relationship dynamics when interpreting such records.
Bots and benefits questions. If you’re like most HR practitioners, you’re happy just to survive open enrollment season. But those fortunate enough to leverage AI via their HR information systems (HRIS) usually don’t have it so bad. Some of today’s HRIS-based chatbots, for example, can automatically reply to employees’ benefits questions with answers tailored to your workforce. That means you spend less time fielding inquiries. While these tools are never perfect, most use a form of AI that makes information delivery extremely customizable. To take full advantage of that, you must build truly dynamic, consumer-oriented Q&A databases that reflect your workers and their preferences.
Algorithms and learning preferences. In recent years, we’ve seen the rise of countless technologies that support learning and development activities. Among the most interesting are apps that use AI to create interactive tests and assessments to match test takers’ personal learning styles and engagement levels. Similar to Lumosity’s interactive brain games, these tools generate countless data points about users as they learn, including their pace and learning style. For HR, such innovations highlight the need for customized learning paths and data-driven approaches to employee development.
It’s clear that AI’s increasing role in HR represents an opportunity for you to drive value through data. Some would cry, “The machines are taking over!” The truth is that the machines are already here. It’s up to us to define how best to use them.
Alexander Alonso, SHRM-SCP, is senior vice president for knowledge development at SHRM.
People Analytics
2018年03月01日
People Analytics
人力资源走向敏捷--HR Goes Agile
人力资源走向敏捷
总览：
人力资源的敏捷性不断提高
敏捷不再仅仅是高科技的代名词，它已经从产品开发到制造到营销，渐渐步入了其他领域和功能中。现在人力资源的灵活性正在改变组织雇佣、发展和管理他们的员工的方式。（在2017德勤的一项调查中，79%的全球高管认为灵活的绩效管理是培养优秀组织中的重要一环。
员工体验的共同创造
那些采用更灵活的人才策略的公司更多的在思考这样一个问题，员工是对工作场所的体验度是怎样的，他们希望像对待顾客一样对待他们的员工。IBM首席人力资源官Diane Gherson，最近跟哈佛商业评论谈及，在标志性的技术公司中员工体验如何对其业务模式进行重组。
一家银行对灵活团队的实验
当网络和移动技术影响到了银行业，消费者越来越意识到他们要为自己做些什么，他们逐渐接受了全球银行集团首席执行官Ralph Hamers的观点，“Banking on the go.”
人力资源走向敏捷
敏捷不仅仅是为了技术而已。它一直在进入其他领域和功能，从产品开发到制造到营销 - 现在它正在改变组织如何雇用，开发和管理他们的员工。
你可以说人力资源正在“敏捷简化”，应用一般原则而不采用科技界的所有工具和协议。这是从基于规则和计划的方法转向由参与者反馈驱动的更简单和更快的模型。这种新的范式在绩效管理领域确实起了作用。（在2017年德勤的一项调查中，79％的全球高管将敏捷绩效管理评为高组织优先事项。）但其他人力资源流程也开始发生变化。
在许多正在逐渐发生的公司中，几乎是有组织的，因为IT的溢出效应，超过90％的组织已经在使用敏捷实践。例如，在蒙特利尔银行（BMO），这一转变始于技术人员加入跨职能产品开发团队，使银行更加关注客户。业务部门从IT同事那里学习了敏捷原则，IT部门从业务中了解到客户需求。其中一个结果是，BMO现在考虑的是团队绩效管理，而不仅仅是个人。在其他方面，敏捷人力资源部门的转变速度更快，更加慎重。GE是一个很好的例子。作为控制系统管理的典范，多年以来，它转而采用了FastWorks，这是一种精简方法，可以减少自上而下的财务控制，并使团队能够根据需求的变化管理项目。
人力资源的变化已经有很长一段时间了。第二次世界大战后，制造业主导了工业景观，计划是人力资源的核心：公司招募了生命力，为他们提供轮换任务以支持他们的发展，提前培养他们以承担更大和更大的角色，并将他们捆绑在一起直接提升到梯子上的每个增量移动。官僚主义是这样一个观点：组织希望他们的人才实践是基于规则和内部一致的，以便他们能够可靠地实现五年（有时是十五年）的计划。这是有道理的。从核心业务到行政职能，公司的其他各个方面都在其目标设定，预算和运营方面采取了长远眼光。人力资源反映并支持他们正在做的事情。
到了20世纪90年代，由于企业变得难以预测，企业需要快速获得新技能，传统方法开始弯曲 - 但并没有完全突破。为了获得更大的灵活性，从外部进行横向招聘取代了大量的内部开发和促销活动。“宽带”补偿为管理者提供了更大的自由度来奖励员工在角色中的成长和成就。然而，大多数情况下，旧模式依然存在。像其他职能一样，人力资源部门仍然是围绕着长期而建立的 继续进行员工队伍和继任计划，尽管经济和业务的变化常常使这些计划无关紧要。尽管几乎普遍不满，但年度评估仍在继续。
现在我们看到了更彻底的转变。为什么这是它的时刻？因为快速创新已经成为大多数公司的战略重点，而不仅仅是一个子集。为了得到它，企业已经向硅谷和软件公司寻找，模仿他们的敏捷实践来管理项目。因此，自上而下的规划模型正在让位于更适合近期适应的灵活的，用户驱动的方法，如快速原型设计，迭代反馈，基于团队的决策以及以任务为中心的“冲刺”。作为BMO首席转型官Lynn Roger表示：“速度是新的商业货币。”
随着旧的人力资源系统的业务合理化，以及敏捷的操作手册可供复制，人员管理终于也获得了期待已久的检修。在本文中，我们将说明公司在人才实践中所做的一些深刻变革，并描述他们在向敏捷人力资源转型过程中所面临的挑战。
我们在哪里看到最大的变化
因为人力资源涉及组织的每个方面 - 每个员工 - 所以它的敏捷转型可能比其他功能的变化更为广泛（也更困难）。公司正在重新设计他们在以下领域的人才实践：
绩效评估。
当企业在核心业务中采用敏捷方法时，他们放弃了试图提前一年或多年计划如何去做以及何时结束的猜忌。所以在很多情况下，第一个传统的人力资源实践是年度绩效评估，以及每年从业务和单位目标“下降”的员工目标。由于个人从事不同领域的短期项目，往往由不同领导人组织，并围绕团队组织，因此一年一次的业绩反馈意见将从一位老板开始，这种想法毫无意义。他们更需要更多的人，更多的人。
早期的行政首长协调会调查显示，人们实际上减少了反馈和支持，当他们的雇主丢弃年度评论时。但是，那是因为许多公司没有任何东西代替它们。管理者认为没有迫切需要采用新的反馈模式，并将注意力转移到其他优先事项上。但是，如果没有填补空白的计划而放弃评估当然是失败的秘诀。
自从学习这一艰难的教训以来，许多组织都转向频繁进行绩效评估，而且经常按项目逐项进行。这一变化已经蔓延到包括零售（Gap），大制药（Pfizer），保险（Cigna），投资（OppenheimerFunds），消费品（P＆G）和会计（所有四大公司）等多个行业。它在通用电气，整个公司的业务范围以及IBM都是最有名的。总的来说，重点是全年提供更为即时的反馈，以便团队可以变得灵活，“过程正确”的错误，提高绩效并通过迭代学习 - 所有关键的敏捷原则。
在以用户为中心的方式中，管理人员和员工已经参与了塑造，测试和改进新流程。例如，强生为其企业提供了参与实验的机会：他们可以尝试新的持续反馈流程，使用定制的应用程序，员工，同事和老板可以实时交换意见。
新流程试图摆脱强生的事件驱动的“五个对话”框架（侧重于目标设定，职业讨论，年中绩效评估，年终评估和薪酬审查），并转向模型持续对话。那些尝试过的人被要求分享一切正常，漏洞是什么等等。实验持续了三个月。起初，只有20％的试点经理积极参与。前几年年度评估的惯性难以克服。但随后该公司利用培训向经理们展示了什么样的良好反馈，并指定了“变革之王”来模拟团队中所需的行为。到三个月结束时，试点组中的46％的经理人员加入，交换了3,000条反馈。
作为快速发展的生物技术公司，Regeneron制药公司正在进行进一步的评估检查。Regeneron公司劳动力发展主管Michelle Weitzman-Garcia认为，从事药物开发，产品供应集团，现场销售人员和公司职能的科学家的表现不应该以相同的周期或以相同的方式进行衡量。她观察到，这些员工群体需要不同的反馈意见，他们甚至在不同的日历上进行操作。
为什么Intuit向敏捷的转型几乎停滞不前
因此，该公司创建了四个独特的评估流程，针对各个群体的需求量身定制。例如，研究科学家和博士后渴望衡量标准并热衷于评估能力，因此他们每年与管理人员会面两次，以进行能力评估和里程碑评估。面向客户的群体包括来自客户和客户评估的反馈。虽然必须管理四个独立的流程增加了复杂性，但它们都强化了持续反馈的新规范。Weitzman-Garcia说，组织的收益远远超过了人力资源成本。
教练。
那些最有效地采用敏捷人才实践的公司投资于提高管理者的教练技能。Cigna的主管们通过为繁忙的管理人员设计的“教练”培训：它被分成每周90分钟的视频，可以被视为人们有时间。主管还参与学习课程，这些课程就像敏捷项目管理中的“学习冲刺”一样简短并且分散开来，以便个人在工作中反思和测试新技能。对等反馈也纳入信诺的经理培训中：同事组成学习小组分享想法和策略。他们正在进行各种公司希望主管与他们的直接报告进行对话，但他们觉得可以自由分享彼此的错误，而不必担心“评估”在他们头上。
DigitalOcean是一家专注于软件即服务（SaaS）基础架构的纽约新创公司，现场聘请全职专业教练帮助所有经理向员工提供更好的反馈，并且更广泛地说，可以开发内部指导功能。这个想法是，一旦经历了良好的教练，就会成为更好的教练。并不是每个人都可以成为一名优秀的教练 - 公司中那些喜欢编码教练的人可以在技术职业生涯中前进 - 但教练技能被认为是管理职业生涯的核心。
宝洁公司也打算让管理人员成为更好的教练。这是为上司重建培训和发展并加强其在组织中的角色的更大努力的一部分。通过简化绩效评估流程，将评估与开发讨论区分开来，并且消除人才校准环节（主管之间的任意马交易往往带有主观和政治化的排名模型），宝洁已经腾出了大量的时间来投入员工的工作，生长。但是，让监督人员从评判员工到在日常工作中指导他们，这一直是宝洁传统丰富文化中的挑战。因此，该公司在培训主管方面投入了大量资金，涉及如何建立员工的优先事项和目标，如何提供有关捐款的反馈，以及如何使员工的职业理想与业务需求和学习与发展计划保持一致。打赌是，建立员工的能力和与主管的关系将增加参与度，从而帮助公司创新并加快步伐。尽管陪审团仍然处于全公司范围内的文化转变之中，宝洁已经在这些领域报告了各级管理层的改进。
团队。
传统人力资源侧重于个人 - 他们的目标，绩效和需求。但是现在有那么多公司按项目组织他们的工作项目，他们的管理和人才系统正在变得更加专注于团队。团队通过Scrum创建，执行和修改他们的目标和任务 - 在团队层面上，现在正在快速适应新信息。（“Scrum”可能是敏捷词典中最着名的术语它来自于橄榄球，玩家紧紧围在一起重新开始游戏）。他们也在自己追踪自己的进步，找出障碍，评估他们的领导力，并且获得关于如何提高表现的见解。
在这种情况下，组织必须学会应对：多向
反馈。在敏捷的环境中，同伴反馈对课程改正和员工发展至关重要，因为团队成员比任何人都更了解每个人的贡献。这很少是一个正式的流程，并且评论通常针对的是员工，而不是主管。这使投入保持建设性，并防止有时在超级竞争性工作场所发生的破坏同事。
但一些高管认为，同行反馈应该对绩效评估产生影响。IBM人力资源主管Diane Gherson解释说：“管理人员和员工之间的关系会随着网络（员工工作的项目集合）而发生变化。”由于敏捷的环境使得“监控”绩效成为可能旧的意义上，IBM的管理人员征求其他人的意见，以帮助他们尽早发现并解决问题。除非它很敏感，否则该输入将在团队的日常站立式会议中共享并在应用程序中捕获。员工可以选择是否将经理和其他人的意见纳入同行。由于同事对主管的评论也转到团队中，因此可以减轻残酷行为的风险。任何试图削弱同事的人都会被暴露。
在敏捷组织中，员工对团队领导和主管的“向上”反馈也很受重视。Mitre公司的非营利研究中心已采取措施鼓励它，但他们发现这需要集中精力。他们开始定期进行机密的员工调查和焦点小组，以发现人们想与管理人员讨论哪些问题。然后人力资源部门将这些数据提供给主管，通过直接报告来通知他们的谈话。然而，员工们最初不愿意提供反馈意见 - 尽管它是匿名的，仅用于开发目的 - 因为他们不习惯表达他们对管理层所做事情的看法。
Mitre还了解到让下属坦诚的最关键因素是管理者明确表示他们想要并赞赏评论。否则，人们可能会合理地担心他们的领导者没有真正愿意接受反馈并准备好应用它。与任何员工调查一样，征求向上反馈并且不采取行动会对参与产生减弱的影响; 它削弱了员工与管理人员之间的辛苦信任。当米特的新绩效管理和反馈过程开始时，首席执行官承认，研究中心需要重复并进行改进。修订的向上反馈系统将于今年推出。
由于反馈流向团队的所有方向，因此许多公司都使用技术来管理团队的数量。应用程序允许主管，同事和客户从任何地方立即给予反馈。最重要的是，主管可以稍后下载所有评论，当时是评估的时候。在一些应用程序中，员工和主管可以对目标进行评分; 至少有一个可以帮助管理人员分析像Slack这样的项目管理平台上的对话，以提供合作反馈。思科利用专有技术收集员工每周的原始数据或“面包屑”，了解他们同行的表现。这些工具使管理者能够看到随着时间的推移个人表现的波动，即使在团队内部也是如此 当然，这些应用程序并不提供正式的性能记录，员工可能希望面对面讨论问题，以避免将问题记录在可下载的文件中。我们知道，企业认可并奖励改进以及实际表现，但隐藏问题并不总是为员工付出代价。
前线决策权。团队的根本转变也影响了决策权：组织正在将他们推向前线，为员工提供装备并赋予其独立性。但这是一个巨大的行为改变，人们需要支持才能实现。让我们回到蒙特利尔银行的例子来说明它如何工作。当BMO引入敏捷团队来设计一些新的客户服务时，高层领导者还没有准备好放弃控制权，而且他们下面的人不习惯接受。所以银行在业务团队中嵌入了敏捷教练。他们首先通过“回顾” - 包括高层管理人员 - 每次迭代后举行定期反思和反馈会议。这些是行动后评论的敏捷版本; 他们的目的是不断改进流程。
复杂的团队动态。最后，由于主管的角色已经从管理个人转向了促进生产性和健康团队动力学的复杂任务，人们也经常需要帮助。思科的特别团队智能部门提供了这种支持。负责识别公司表现最佳的团队，分析他们的运作方式，并帮助其他团队学习如何变得更像他们。它使用名为Team Space的企业级平台，该平台跟踪团队项目，需求和成就的数据，以衡量和改进团队在单位内部和整个公司内部进行的工作。
补偿。
工资也在变化。在梅西百货等零售公司看到，对于敏捷工作的简单调整就是使用现金奖励来确认发生的贡献，而不是仅仅依靠年终工资增长。研究和实践表明，在期望的行为发生后，尽快出现补偿最有利于激励。即时奖励以强大的方式强化即时反馈。由于时间过长，每年以绩效为基础的提高效率不高。
巴塔哥尼亚实际上已经取消了其知识型员工的年度加薪。相反，公司根据市场利率走向的研究，更频繁地调整每项工作的工资。当员工承担更多困难的项目或以其他方式超越时，也可以分配增加额。公司保留个人贡献者前1％的预算，并且主管可以为任何有利于该指定的贡献提供支持，包括对团队的贡献。
敏捷组织重视员工对团队领导的向上反馈。
补偿也被用来加强敏捷价值，如学习和知识共享。例如，在初创的世界里，在线服装租赁公司Rent the Runway分出了不同的奖金，将这笔钱滚到基本工资。首席执行官詹妮弗海曼报告说，奖金计划正在接受诚实的同行反馈。员工并没有分享建设性的批评意见，他们知道这会给他们的同事带来负面的经济后果。海曼说，新系统通过“解开两者”来防止这个问题。
DigitalOcean重新设计了奖励，以促进员工的公平待遇和合作文化。薪资调整现在每年发生两次，以应对外部劳动力市场以及工作和业绩的变化。更重要的是，DigitalOcean缩小了同等工作的薪酬差距。它故意不顾内部竞争，痛苦地意识到超级竞争文化中的问题（比如微软和亚马逊）。为了个性化薪酬，该公司绘制了人们对其角色有影响以及他们需要成长和发展的地点。有关个人对企业影响的数据是讨论薪酬的关键因素。谈判提高自己的薪水是非常沮丧的。而只有成就最高的1％才会获得财务奖励; 否则，没有奖励过程。所有员工都有资格获得奖金，这是基于公司业绩而不是个人缴款。为了进一步支持协作，DigitalOcean正在多元化其奖励组合，以包括非金融和有意义的礼物，如带有首席执行官“最佳书籍”选择的Kindle。
DigitalOcean如何激励人们在没有虚增财务奖励的情况下表现最好？其副总裁马特霍夫曼说，它着重于创造一种激发目的和创造力的文化。到目前为止，似乎工作。通过Culture Amp进行的最新参与调查将DigitalOcean评为高于行业基准的17分，以满足补偿。
招聘。
随着经济大衰退以来经济的改善，招聘和招聘变得更加紧迫和灵活。为了在2015年迅速扩大规模，GE新的数字部门率先进行了一些有趣的招聘实验。例如，一个跨职能团队就所有招聘申请一起工作。“人数经理”代表内部利益相关者的利益，他们希望他们的职位能够快速适当填补。招聘经理轮流和离开团队，取决于他们目前是否在招聘，而Scrum大师负责监督流程。
为了保持事情的顺利进行，团队专注于解决所有障碍的职位空缺 - 如果辩论仍在继续讨论候选人的期望属性，则无需开始工作。职位空缺被排名，并且团队专注于最优先的员工，直至他们完成。它可以同时雇佣多名雇员，以便成员可以分享有关可能更适合其他角色的候选人的信息。该团队跟踪其填充职位的周期时间，并监控看板上的所有未决申请，以确定瓶颈和被阻止的流程。IBM现在采用类似的招聘方式。
公司也越来越依赖技术来寻找和跟踪非常适合敏捷工作环境的候选人。通用电气，IBM和思科正在与Ascendify供应商合作开发可以实现这一目标的软件。IT招聘公司HackerRank提供了一个用于同样目的的在线工具。
学习和发展。
像招聘一样，L＆D不得不改变，以更快速地将新技能带入组织。大多数公司已经有一套在线学习模块，员工可以按需访问。虽然对那些有明确需求的人有帮助，但这有点像给学生一个图书馆的钥匙，告诉她找出她必须知道的东西，然后学习它。较新的方法使用数据分析来识别特定工作和晋升所需的技能，然后根据他们的经验和兴趣向个别员工建议何种培训和未来工作对他们有意义。
IBM使用人工智能来产生这样的建议，从员工的简介开始，包括先前和当前的角色，预期的职业轨迹以及完成的培训计划。该公司还为敏捷环境创建了特殊培训 - 例如，使用围绕一系列“角色”构建的动画模拟来说明有用的行为，例如提供建设性的批评。
人力资源可以从技术中学习什么
传统上，L＆D将继任计划包括在内 - 是自上而下的长期思维的缩影，由此人们提前几年挑选出最重要的领导角色，通常希望他们能够按计划发展某些能力。不过，世界往往不能与这些计划合作。公司经常发现，在高级领导职位开放之时，他们的需求已经发生了变化。最常见的解决方案是忽略计划并从头开始搜索。但是，无论如何组织通常会继续进行长期的继任计划。（大约一半的大公司有计划为顶尖工作开发接班人。）百事可乐公司通过缩短时间框架，从这个模型中脱身而出。
持续的挑战
可以肯定的是，并非每个组织或团体都在追求快速创新。有些工作必须基本以规则为基础。（考虑会计师，核控制室操作员和外科医生所做的工作。）在这种情况下，敏捷人才实践可能没有意义。
即使他们合适，他们也可能遇到阻力 - 尤其是在人力资源部门。许多流程必须改变，让组织摆脱基于规划的“瀑布”模型（这是线性的而不是灵活的和适应性的），并且其中一些流程被硬连接到信息系统，职位名称等等。向独立发生的基于云计算的IT迈进，使采用基于应用的工具变得更加容易。但人们的问题仍然是一个棘手的问题。许多人力资源工作，例如传统的招聘，入职和计划协调方法，将会变得过时，这些领域的专业知识也会过时。
同时，新的任务正在创建。帮助主管取代对教练的评价不仅是技术方面的挑战，也是因为它削弱了他们的地位和正式的权威。将管理重点从个人转移到团队可能更加困难，因为团队动态对于那些仍在努力理解如何指导个人的人来说可能是一个黑盒子。最大的问题是公司是否可以帮助管理者把所有这些都看好，并看到其中的价值。
人力资源职能也需要重新培训。它需要更多的IT支持方面的专业知识 - 尤其是考虑到新应用程序产生的所有性能数据 - 以及对团队和实际操作监督的深入了解。近几十年来，人力资源并没有像它所支持的生产线一样改变。但是现在压力已经开始了，它来自于经营层面，这使得坚守旧的人才实践变得更加困难。
共同创造员工体验
作者：Lisa Burrell
采用敏捷人才实践的公司正在对员工如何体验工作场所给予很多思考 - 在某些方面，将他们视为客户。IBM首席人力资源官Diane Gherson最近与HBR讨论了这个标志性科技公司如何改变其业务模式，这是如何发生的。编辑摘录如下。
HBR： IBM将人力资源经验放在人力资源管理的中心在什么意义上？
佳森律师事务所：和其他很多公司一样，我们始于相信如果人们与我们合作感觉很好，我们的客户也会这样。这不是一个新的想法，但它确实是我们非常认真对待的一个问题，大约需要四五年。我们已经看到它证实了。我们发现员工敬业度解释了我们客户体验分数的三分之二。如果我们能够将客户满意度提高5个点，我们平均可以获得额外20％的收入。很显然，这有一个影响。这是变革的商业案例。
但它需要思想转变。以前，我们倾向于依靠专家来建立我们的人力资源计划。现在，我们将员工带入设计流程，与他们共同创造，随着时间的推移迭代，以满足人们的需求。
IBM人力资源主管戴安·吉尔森
这在实践中看起来如何？
员工入职是一个很好的例子 - 我们非常认真地看待第一个流程。我们知道我们希望人们走出去思考，“我很高兴我在这里，我明白我需要知道要走的路。”但是我们开始太小了。我们以一种传统的方式接近了它，所有这些都是关于你的第一天的体验。一旦我们开始询问新员工他们的入职情况如何，我们听到了诸如“我没有及时拿到笔记本电脑”，或者“我无法及时获得我的信用卡来参加我的第一次会议”或“我在访问内部网络时遇到了问题。“所有这些都会影响到有人加入公司的感觉。
一旦你意识到这一点，入职团队的职责就变成了人们如何体验整个过程，从头到尾。为了做到这一点，你必须与更广泛的玩家合作。你带上安全设备以确保身份证件在那里。你带来房地产，以确保人们有一个物理空间，并知道去哪里。您可以使用Networking来确保其远程访问已启动并正在运行。所有这些都是入职培训的一部分。这不仅仅是在第一天和其他一批新员工进行一次精彩的会面。
我们花了一段时间才明白这一点。你必须扩大你的范围，并停止思考，以创造一个伟大的员工体验。
IBM的学习和开发方法如何改变？
人们现在在手机和平板电脑上消费内容 - 他们使用YouTube和TED会谈来加快他们不知道的事情。所以我们不得不放弃传统的学习管理体系，对教育和发展有不同的想法。再次，我们引进了我们的千禧一代，引入了我们的用户，并且为我们的380,000名IBM员工中的每一位提供了个性化的学习平台。
它是根据角色量身定制的，智能建议不断更新。它的组织有点像Netflix，有不同的渠道。你可以看到其他人如何评价各种产品。还有一位现场聊天顾问，他现在帮助学习者。
我们测量人力资源服务，如使用净推动力分数进行学习 - 这是不可抗拒体验的终极指标。之前，我们使用了经典的五点满意度量表。即使有人给你评分3.1，你最终会说他们很满意，而对于Net Promoter来说，你必须处于最后的规模，因为你必须减去所有的反对者。要做到这一点很难，它会给你提供更好的人们反馈信息。为了学习，最后我们的NPS为60.这是在“优秀”范围内，但当然还有改进的空间。
你用什么工具来定制学习？
通过Watson Analytics，我们能够从公司内部的数字足迹中推断出人们的专业知识，并将其与他们应该在其特定工作家庭中的位置进行比较。该系统是认知的，所以它知道你 - 它已经摄入了关于你的技能的数据，并能够给你个性化的学习建议。它会告诉你，“好的，你需要增加这些领域的深度 - 这里有一些产品可以帮助你做到这一点。”然后，你可以将它们固定在日历中，或者排列在日历中以备将来学习。该系统还研究了您可能距离获得数字徽章有多近，我们在过去几年中已经开始使用该徽章来展示哪些员工应用了技能。该工具可帮助您通过推荐特定的网络研讨会和内部和外部课程来实现徽章。这全都基于人工智能。在这一点上，技能推论的准确率大约为96％。
“人们在成型时不太可能抵制变革。”
你怎么知道？
我们过去一直在进行这种费力的手动过程，让人们填写技能调查问卷，让他们的经理签字。但那会很快过时。所以我们停止了这样做。相反，特定工作家庭或行业的领导者会对我们推断的结果进行抽查。他们采访员工并确定他们的位置，并将其与我们系统中的推断进行比较。
IBM也对其性能管理系统进行了改革。员工如何参与这个过程？
如你所知，绩效管理在大多数公司中都是一种避雷针。而不是做典型的事情 - 这将是做一些基准测试，集合一批专家，提出新设计并试用它 - 我们决定全力以赴和我们的员工共同创造一种延长的黑客马拉松。我们使用了设计思维，提出了你可能被描述为“概念车”的东西 - 这是人们试驾和踢轮胎的东西，而不是仅仅处理概念。我们在2015年夏天做到了这一点，并在五个月后在整个公司实施。这就是让全体员工参与的力量 - 人们在掌握变化时不太可能抵制变革。
为了开始共同创作过程，我有一天在博客上写道：“我们很乐意接受你的建议。如果你讨厌它，我们会重新开始，没问题。但我们真的想要你的想法。“我们做了一些关于我们认为可能的样子的视频。我在一夜之间得到了18,000个回应 幸运的是，我们有技术来分析这一切，看看人们喜欢和不喜欢的东西。
起初有人说：“这真是一个骗局 - 你已经知道你想做什么。”但我们解释说我们真的想听到他们的消息，并且我们把他们带到了各种讨论论坛。这花了一段时间，但我想我们确实把他们转过来了。我们不断沟通，说：“好吧，你喜欢这个; 你不喜欢那样。这里是你不能同意的地方。“与此同时，我们正在组装原型来向人们展示。
我清楚地知道有一些基本规则。例如，我们不会摆脱关于绩效的讨论，我们希望为绩效付费。但总的来说，它是开放的。与大多数公司相比，整个过程花费的时间少于重新设计绩效管理计划的时间，我们涉及大约10万名员工。最后，我们问道：“你想怎么称呼它？”成千上万的人投了票。我们最后有三个名字，并选择了检查站。
绩效管理永远不可能是完美的。但是你的宝宝从来不会很难看。我们的员工创建了自己的计划，并为此感到自豪。你可以在他们正在进行的博客中看到它，我们要求他们谈论什么在工作，什么不在，并告诉我们如何改进系统。自从我们把它放在那里以来，我们一直这么做。他们的总体信息是“这就是我们想要的”。它被认为是参与度提高的首要原因。人们以更加丰富的方式从这个系统中获得更多的反馈。更重要的是，他们在我们的转变中并不像是旁观者。他们是积极的参与者。
“我们能够迅速发现问题并承诺为他们做些事情。”
你如何利用“情绪分析”来进一步解决员工的需求？
情绪分析在人们总是在线评论的世界中非常有用。我们的认知技术着眼于人们选择的语言并提取语气。它确定它是正面的还是负面的，然后再深入，说明它是强烈的还是强烈的消极的。这样看起来就像看音乐 - 看看哪里有很高的音符或很低的音符很响。它始终在我们的防火墙之后，永远不会外部。它不会查看任何人传递的信息或电子邮件内容或浏览行为。它只是在他们的博客和防火墙内的评论中看到语气。
使用这种方法，如果您需要深入了解某个区域，您可以快速提取。我们已经能够迅速发现开始酿造的问题，并且更重要的是，承诺为他们做些事情。这是与社交平台合作最令人兴奋的部分。我们举了几个我们做错了事情的例子。我的一些人决定，我们不会赔偿共乘。员工变得焦躁不安，我可以迅速回应已经变成请愿书的问题。“我读了你的所有评论，”我告诉他们，“你提出了我们没有想到的一些伟大的观点。我们试图寻找您的安全，但总的来说，这不是正确的选择。让我们回到我们原来的政策。“所有这些都在24小时内发生。人们听到并非常感激。
一年前我们有类似的情况。当您前往客户网站整整一周时，我们不得不计算收入，而不是马上回家，您的配偶或朋友会在周末陪伴您。因为我们会报销客人的旅行，所以造成了税务问题。我们改变了这个计划，因为这个计划变得混乱了，员工们又被激怒了。我当然可以理解为什么。如果你一直在路上，当然你可能希望你的配偶陪你一个周末。人们不希望我们为他们做出决定。那是另外一个例子，我们很快就聚在一起说：“嘿，如果他们想为自己的税收负责，他们可以做到。”这是一个很好的警告，呼吁我们不要如此家长式。
在人们身体不在一起的组织中，您可以使用情感分析来了解哪些地方出现问题，哪些地方管理不够强大，哪些地区的人群表达否定意见。它允许你检查这些网站或组，并查明发生了什么。
现在的员工是否比过去拥有更多权力？
是。现在对组织内部的内容给予更多的重视，因为它也可以通过社交媒体在外面听到。Glassdoor就是一个很好的例子。在过去，你可能有一些公司不适合工作，但只有一小部分人知道。现在全世界都知道这件事，因为它在Glassdoor上 - 这使得公司变成了玻璃屋。人们可以看看发生了什么，并以他们以前无法做到的方式判断他们是否想在那里工作。
让我们回过头来看看IBM向敏捷人才实践转变的背后的商业原因 - 您能否更多地谈论这些？
我提到客户满意度。今天的客户正在寻找前所未有的速度和响应能力。在较早的时代，他们真正想要的是最好的产品，最好的价格 - 效率很重要，但速度并不如此。
在二十一世纪初，我们将为来自世界各地的专家组织一个项目，他们将花费一小部分时间在这个项目上，因为他们也在从事其他项目。他们会加入电话会议，因为人们处于不同的时区，这一直很难。我相信他们在进行这些电话时是多任务的。该项目可能需要六个月到一年的时间。现在，我们将采用一小组专门的人员，并将他们放在一起三个月，他们将使用敏捷方法完成所有工作。这是关于如何为客户创造价值的另一种思考方式。它响应他们对速度的需求。
是否有人希望敏捷的人才方法能够帮助IBM弥补其在向云计算和其他业务转型过程中失去的收入和增长？
我们是一家正在改变自己的公司：我们45％的收入来自我们五年前没有的企业，而我们是一家800亿美元的公司。当你正在经历这种转变，并看到你的一些传统业务出现低迷时，并且当你开始新业务时你正在翻新这些业务，你可能会看到一些不平衡的表现。你在开车的时候基本上是换胎。是的，这需要敏捷。
一家银行的敏捷团队实验
由Dominic Barton，Dennis Carey和Ram Charan撰写
当网络和移动技术打乱银行业时，消费者越来越意识到自己可以为自己做些什么。他们很快接受了全球银行集团ING首席执行官拉尔夫哈默斯称的“随时随地的银行业务”。
到2014年，与ING零售客户的所有互动中约有40％通过移动应用程序进入。（现在这个数字已经接近60％了 - 分支机构的访问量和联系中心的呼叫数已经下降到1％以下）。即便移动客户希望能够随时随地轻松访问最新的信息。例如，某人在乘火车回家的路上，他开始进行贷款交易，希望能够在当晚的桌面上继续使用。“我们的客户将大部分在线时间花费在Facebook和Netflix等平台上，”Hamers说。“这些为用户体验设定了标准。”
这意味着ING需要变得更加灵活和更加以用户为中心，在其金融之旅的每一个角落为全球3,000多万客户提供服务。因此，哈默尔与荷兰荷兰集团首席执行官Nick Jue一起，在ING最大的荷兰零售业务部门总部启动了试点转型。第一步是帮助其他高层领导和董事会设想一个新的灵活的，基于团队的系统来部署，开发和评估人才。（ING已经在荷兰IT部门采用敏捷和Scrum方法，但这些工作方式对组织其他部门来说是新的。）Hamers和他的领导团队随后在他们所崇拜的科技公司会见了人员，了解他们的人才系统提供更好的客户服务。到2015年春荷兰荷兰国际集团的总部，部落，小队和章节。
部落，小队和章节
创建了13个部落来解决特定的领域，例如抵押服务，证券和私人银行业务。每个部落最多可容纳150人。（例如，销售，服务和支持职能部门的员工在这种结构之外工作 - 例如在较小的客户忠诚团队中工作 - 但他们与部落合作）。并且每个部门都有领导者确定优先事项，分配预算并确保知识和见解在部落内部和部落之间共享。
部落领导还有另外一项重要责任：通过部落成员的投入，创建由九人或更少人组成的自我指导小组，通过交付和维护新产品和服务来解决特定客户需求。这些小组是跨学科的 - 通常由营销专家，数据分析师，用户体验设计师，IT工程师和产品专家组成。一名小队成员被指定为“产品负责人”，负责协调活动并确定优先事项。只要满足客户的需求，团队就会一直呆在一起 - 无论是提高移动应用程序的用户体验还是构建特定功能。有些任务在两周内完成; 其他人可能需要18个月。有时候团队解散，成员加入其他团队。最经常，
通过在这样的小单位工作，并与来自不同学科的同事一起工作，小队成员可以迅速解决之前可能从部门反弹到部门的问题。通过Scrum和日常站点等机制鼓励信息共享，这是您在科技初创公司可以找到的聚会类型。从开始到结束看到一个项目，让每个小组都感受到对客户的所有权和联系。
实施敏捷人才系统并不意味着陷入混乱。实际上，设计良好的系统遵循明确规定的规则和保障措施，以确保机构稳定。例如，每个部落都有一对敏捷教练，帮助队员和个人在鼓励员工在实地解决问题而不是传递给其他人的环境中有效协作。尽管你可能认为适应对于长期银行员工来说是最难的，但根据ING荷兰首席信息官Peter Jacobs的说法，情况并非如此。“他们中的许多人”比年轻一代更快，更容易适应“，他说，也许是因为他们的专业知识现在比过去有更多的影响力，因为需要签署这么多的签字。
在小型跨职能部门工作，班组可以快速解决问题。
然后是章节，它们协调同一学科的成员 - 数据分析或者系统过程 - 分散在班组中。章节负责人负责跟踪和分享最佳实践以及诸如专业开发和绩效评估之类的内容。即使在省去了耗时的交接和官僚作风的情况下，也可以将章节看作是保留传统管理的有用部分的一种方式。
系统内置定期评估。每两周一次的班组审查他们的工作。哈默斯说：“他们可以决定他们将如何继续为我们的客户改进产品，或者他们是否想'快速失败'。”（从失败中学习是值得称赞的）。小组在完成任务之后还会进行全面的自我评估参与和部落进行季度业务评论（QBR），观察他们最大的成功和失败，回顾他们最重要的学习，并明确未来三个月的目标。
这些保障措施有助于抵消ING荷兰公司现任首席执行官Vincent van den Boogert（以及启动新组织结构的团队的一部分）所认为的基于班组系统的两大挑战。一个是自负的小队主要响应客户的需求可能会采取与公司战略不同步的变化。QBRs可以缓解这种风险。第二个挑战有点违反直觉。自我评估小组有时满足于他们每两周进行的渐进式改进。QBR也在这方面提供帮助，因为高层管理人员使用它们来制定和加强延伸目标。
哈默尔在两年多的时间里认为这个人才实验取得了巨大的成功。客户满意度和员工敬业度都提高了，ING更快地推出新产品。因此，该银行已开始推出这种新工作方式，为本国以外的约4万名员工工作。对于哈默斯来说，改变不会很快。每个ING 13个零售市场的应用程序在外观，设计和功能上各不相同。Hamers希望让事情变得更简单，这样任何地方的任何客户都会遇到同样的ING。“技术公司在全球有一个平台，”他说。“无论您在哪里使用Netflix，Facebook或Google，都可以获得相同的服务。ING必须这样做。这是我们将所有客户带入银行业未来的唯一途径。“
以上由AI翻译完成，HRTechChina.com倾情奉献，转载请注明。
HR Goes Agile
by Peter Cappelli & Anna Tavis
Agile isn’t just for tech anymore. It’s been working its way into other areas and functions, from product development to manufacturing to marketing—and now it’s transforming how organizations hire, develop, and manage their people.
You could say HR is going “agile lite,” applying the general principles without adopting all the tools and protocols from the tech world. It’s a move away from a rules- and planning-based approach toward a simpler and faster model driven by feedback from participants. This new paradigm has really taken off in the area of performance management. (In a 2017 Deloitte survey, 79% of global executives rated agile performance management as a high organizational priority.) But other HR processes are starting to change too.
In many companies that’s happening gradually, almost organically, as a spillover from IT, where more than 90% of organizations already use agile practices. At the Bank of Montreal (BMO), for example, the shift began as tech employees joined cross-functional product-development teams to make the bank more customer focused. The business side has learned agile principles from IT colleagues, and IT has learned about customer needs from the business. One result is that BMO now thinks about performance management in terms of teams, not just individuals. Elsewhere the move to agile HR has been faster and more deliberate. GE is a prime example. Seen for many years as a paragon of management through control systems, it switched to FastWorks, a lean approach that cuts back on top-down financial controls and empowers teams to manage projects as needs evolve.
The changes in HR have been a long time coming. After World War II, when manufacturing dominated the industrial landscape, planning was at the heart of human resources: Companies recruited lifers, gave them rotational assignments to support their development, groomed them years in advance to take on bigger and bigger roles, and tied their raises directly to each incremental move up the ladder. The bureaucracy was the point: Organizations wanted their talent practices to be rules-based and internally consistent so that they could reliably meet five-year (and sometimes 15-year) plans. That made sense. Every other aspect of companies, from core businesses to administrative functions, took the long view in their goal setting, budgeting, and operations. HR reflected and supported what they were doing.
By the 1990s, as business became less predictable and companies needed to acquire new skills fast, that traditional approach began to bend—but it didn’t quite break. Lateral hiring from the outside—to get more flexibility—replaced a good deal of the internal development and promotions. “Broadband” compensation gave managers greater latitude to reward people for growth and achievement within roles. For the most part, though, the old model persisted. Like other functions, HR was still built around the long term. Workforce and succession planning carried on, even though changes in the economy and in the business often rendered those plans irrelevant. Annual appraisals continued, despite almost universal dissatisfaction with them.
Now we’re seeing a more sweeping transformation. Why is this the moment for it? Because rapid innovation has become a strategic imperative for most companies, not just a subset. To get it, businesses have looked to Silicon Valley and to software companies in particular, emulating their agile practices for managing projects. So top-down planning models are giving way to nimbler, user-driven methods that are better suited for adapting in the near term, such as rapid prototyping, iterative feedback, team-based decisions, and task-centered “sprints.” As BMO’s chief transformation officer, Lynn Roger, puts it, “Speed is the new business currency.”
With the business justification for the old HR systems gone and the agile playbook available to copy, people management is finally getting its long-awaited overhaul too. In this article we’ll illustrate some of the profound changes companies are making in their talent practices and describe the challenges they face in their transition to agile HR.
Where We’re Seeing the Biggest Changes
Because HR touches every aspect—and every employee—of an organization, its agile transformation may be even more extensive (and more difficult) than the changes in other functions. Companies are redesigning their talent practices in the following areas:
Performance appraisals.
When businesses adopted agile methods in their core operations, they dropped the charade of trying to plan a year or more in advance how projects would go and when they would end. So in many cases the first traditional HR practice to go was the annual performance review, along with employee goals that “cascaded” down from business and unit objectives each year. As individuals worked on shorter-term projects of various lengths, often run by different leaders and organized around teams, the notion that performance feedback would come once a year, from one boss, made little sense. They needed more of it, more often, from more people.
An early-days CEB survey suggested that people actually got less feedback and support when their employers dropped annual reviews. However, that’s because many companies put nothing in their place. Managers felt no pressing need to adopt a new feedback model and shifted their attention to other priorities. But dropping appraisals without a plan to fill the void was of course a recipe for failure.
Since learning that hard lesson, many organizations have switched to frequent performance assessments, often conducted project by project. This change has spread to a number of industries, including retail (Gap), big pharma (Pfizer), insurance (Cigna), investing (OppenheimerFunds), consumer products (P&G), and accounting (all Big Four firms). It is most famous at GE, across the firm’s range of businesses, and at IBM. Overall, the focus is on delivering more-immediate feedback throughout the year so that teams can become nimbler, “course-correct” mistakes, improve performance, and learn through iteration—all key agile principles.
In user-centered fashion, managers and employees have had a hand in shaping, testing, and refining new processes. For instance, Johnson & Johnson offered its businesses the chance to participate in an experiment: They could try out a new continual-feedback process, using a customized app with which employees, peers, and bosses could exchange comments in real time.
The new process was an attempt to move away from J&J’s event-driven “five conversations” framework (which focused on goal setting, career discussion, a midyear performance review, a year-end appraisal, and a compensation review) and toward a model of ongoing dialogue. Those who tried it were asked to share how well everything worked, what the bugs were, and so on. The experiment lasted three months. At first only 20% of the managers in the pilot actively participated. The inertia from prior years of annual appraisals was hard to overcome. But then the company used training to show managers what good feedback could look like and designated “change champions” to model the desired behaviors on their teams. By the end of the three months, 46% of managers in the pilot group had joined in, exchanging 3,000 pieces of feedback.
Regeneron Pharmaceuticals, a fast-growing biotech company, is going even further with its appraisals overhaul. Michelle Weitzman-Garcia, Regeneron’s head of workforce development, argued that the performance of the scientists working on drug development, the product supply group, the field sales force, and the corporate functions should not be measured on the same cycle or in the same way. She observed that these employee groups needed varying feedback and that they even operated on different calendars.
Why Intuit’s Transition to Agile Almost Stalled Out
So the company created four distinct appraisal processes, tailored to the various groups’ needs. The research scientists and postdocs, for example, crave metrics and are keen on assessing competencies, so they meet with managers twice a year for competency evaluations and milestones reviews. Customer-facing groups include feedback from clients and customers in their assessments. Although having to manage four separate processes adds complexity, they all reinforce the new norm of continual feedback. And Weitzman-Garcia says the benefits to the organization far outweigh the costs to HR.
Coaching.
The companies that most effectively adopt agile talent practices invest in sharpening managers’ coaching skills. Supervisors at Cigna go through “coach” training designed for busy managers: It’s broken into weekly 90-minute videos that can be viewed as people have time. The supervisors also engage in learning sessions, which, like “learning sprints” in agile project management, are brief and spread out to allow individuals to reflect and test-drive new skills on the job. Peer-to-peer feedback is incorporated in Cigna’s manager training too: Colleagues form learning cohorts to share ideas and tactics. They’re having the kinds of conversations companies want supervisors to have with their direct reports, but they feel freer to share mistakes with one another, without the fear of “evaluation” hanging over their heads.
DigitalOcean, a New York–based start-up focused on software as a service (SaaS) infrastructure, engages a full-time professional coach on-site to help all managers give better feedback to employees and, more broadly, to develop internal coaching capabilities. The idea is that once one experiences good coaching, one becomes a better coach. Not everyone is expected to become a great coach—those in the company who prefer coding to coaching can advance along a technical career track—but coaching skills are considered central to a managerial career.
P&G, too, is intent on making managers better coaches. That’s part of a larger effort to rebuild training and development for supervisors and enhance their role in the organization. By simplifying the performance review process, separating evaluation from development discussions, and eliminating talent calibration sessions (the arbitrary horse trading between supervisors that often comes with a subjective and politicized ranking model), P&G has freed up a lot of time to devote to employees’ growth. But getting supervisors to move from judging employees to coaching them in their day-to-day work has been a challenge in P&G’s tradition-rich culture. So the company has invested heavily in training supervisors on topics such as how to establish employees’ priorities and goals, how to provide feedback about contributions, and how to align employees’ career aspirations with business needs and learning and development plans. The bet is that building employees’ capabilities and relationships with supervisors will increase engagement and therefore help the company innovate and move faster. Even though the jury is still out on the companywide culture shift, P&G is already reporting improvements in these areas, at all levels of management.
Teams.
Traditional HR focused on individuals—their goals, their performance, their needs. But now that so many companies are organizing their work project by project, their management and talent systems are becoming more team focused. Groups are creating, executing, and revising their goals and tasks with scrums—at the team level, in the moment, to adapt quickly to new information as it comes in. (“Scrum” may be the best-known term in the agile lexicon. It comes from rugby, where players pack tightly together to restart play.) They are also taking it upon themselves to track their own progress, identify obstacles, assess their leadership, and generate insights about how to improve performance.
In that context, organizations must learn to contend with:
Multidirectional feedback. Peer feedback is essential to course corrections and employee development in an agile environment, because team members know better than anyone else what each person is contributing. It’s rarely a formal process, and comments are generally directed to the employee, not the supervisor. That keeps input constructive and prevents the undermining of colleagues that sometimes occurs in hypercompetitive workplaces.
But some executives believe that peer feedback should have an impact on performance evaluations. Diane Gherson, IBM’s head of HR, explains that “the relationships between managers and employees change in the context of a network [the collection of projects across which employees work].” Because an agile environment makes it practically impossible to “monitor” performance in the old sense, managers at IBM solicit input from others to help them identify and address issues early on. Unless it’s sensitive, that input is shared in the team’s daily stand-up meetings and captured in an app. Employees may choose whether to include managers and others in their comments to peers. The risk of cutthroat behavior is mitigated by the fact that peer comments to the supervisor also go to the team. Anyone trying to undercut colleagues will be exposed.
In agile organizations, “upward” feedback from employees to team leaders and supervisors is highly valued too. The Mitre Corporation’s not-for-profit research centers have taken steps to encourage it, but they’re finding that this requires concentrated effort. They started with periodic confidential employee surveys and focus groups to discover which issues people wanted to discuss with their managers. HR then distilled that data for supervisors to inform their conversations with direct reports. However, employees were initially hesitant to provide upward feedback—even though it was anonymous and was used for development purposes only—because they weren’t accustomed to voicing their thoughts about what management was doing.
Mitre also learned that the most critical factor in getting subordinates to be candid was having managers explicitly say that they wanted and appreciated comments. Otherwise people might worry, reasonably, that their leaders weren’t really open to feedback and ready to apply it. As with any employee survey, soliciting upward feedback and not acting on it has a diminishing effect on participation; it erodes the hard-earned trust between employees and their managers. When Mitre’s new performance-management and feedback process began, the CEO acknowledged that the research centers would need to iterate and make improvements. A revised system for upward feedback will roll out this year.
Because feedback flows in all directions on teams, many companies use technology to manage the sheer volume of it. Apps allow supervisors, coworkers, and clients to give one another immediate feedback from wherever they are. Crucially, supervisors can download all the comments later on, when it’s time to do evaluations. In some apps, employees and supervisors can score progress on goals; at least one helps managers analyze conversations on project management platforms like Slack to provide feedback on collaboration. Cisco uses proprietary technology to collect weekly raw data, or “breadcrumbs,” from employees about their peers’ performance. Such tools enable managers to see fluctuations in individual performance over time, even within teams. The apps don’t provide an official record of performance, of course, and employees may want to discuss problems face-to-face to avoid having them recorded in a file that can be downloaded. We know that companies recognize and reward improvement as well as actual performance, however, so hiding problems may not always pay off for employees.
Frontline decision rights. The fundamental shift toward teams has also affected decision rights: Organizations are pushing them down to the front lines, equipping and empowering employees to operate more independently. But that’s a huge behavioral change, and people need support to pull it off. Let’s return to the Bank of Montreal example to illustrate how it can work. When BMO introduced agile teams to design some new customer services, senior leaders weren’t quite ready to give up control, and the people under them were not used to taking it. So the bank embedded agile coaches in business teams. They began by putting everyone, including high-level executives, through “retrospectives”—regular reflection and feedback sessions held after each iteration. These are the agile version of after-action reviews; their purpose is to keep improving processes. Because the retrospectives quickly identified concrete successes, failures, and root causes, senior leaders at BMO immediately recognized their value, which helped them get on board with agile generally and loosen their grip on decision making.
Complex team dynamics. Finally, since the supervisor’s role has moved away from just managing individuals and toward the much more complicated task of promoting productive, healthy team dynamics, people often need help with that, too. Cisco’s special Team Intelligence unit provides that kind of support. It’s charged with identifying the company’s best-performing teams, analyzing how they operate, and helping other teams learn how to become more like them. It uses an enterprise-wide platform called Team Space, which tracks data on team projects, needs, and achievements to both measure and improve what teams are doing within units and across the company.
Compensation.
Pay is changing as well. A simple adaptation to agile work, seen in retail companies such as Macy’s, is to use spot bonuses to recognize contributions when they happen rather than rely solely on end-of-year salary increases. Research and practice have shown that compensation works best as a motivator when it comes as soon as possible after the desired behavior. Instant rewards reinforce instant feedback in a powerful way. Annual merit-based raises are less effective, because too much time goes by.
Patagonia has actually eliminated annual raises for its knowledge workers. Instead the company adjusts wages for each job much more frequently, according to research on where market rates are going. Increases can also be allocated when employees take on more-difficult projects or go above and beyond in other ways. The company retains a budget for the top 1% of individual contributors, and supervisors can make a case for any contribution that merits that designation, including contributions to teams.
Upward feedback from employees to team leaders is valued in agile organizations.
Compensation is also being used to reinforce agile values such as learning and knowledge sharing. In the start-up world, for instance, the online clothing-rental company Rent the Runway dropped separate bonuses, rolling the money into base pay. CEO Jennifer Hyman reports that the bonus program was getting in the way of honest peer feedback. Employees weren’t sharing constructive criticism, knowing it could have negative financial consequences for their colleagues. The new system prevents that problem by “untangling the two, ” Hyman says.
DigitalOcean redesigned its rewards to promote equitable treatment of employees and a culture of collaboration. Salary adjustments now happen twice a year to respond to changes in the outside labor market and in jobs and performance. More important, DigitalOcean has closed gaps in pay for equivalent work. It’s deliberately heading off internal rivalry, painfully aware of the problems in hypercompetitive cultures (think Microsoft and Amazon). To personalize compensation, the firm maps where people are having impact in their roles and where they need to grow and develop. The data on individuals’ impact on the business is a key factor in discussions about pay. Negotiating to raise your own salary is fiercely discouraged. And only the top 1% of achievement is rewarded financially; otherwise, there is no merit-pay process. All employees are eligible for bonuses, which are based on company performance rather than individual contributions. To further support collaboration, DigitalOcean is diversifying its portfolio of rewards to include nonfinancial, meaningful gifts, such as a Kindle loaded with the CEO’s “best books” picks.
How does DigitalOcean motivate people to perform their best without inflated financial rewards? Matt Hoffman, its vice president of people, says it focuses on creating a culture that inspires purpose and creativity. So far that seems to be working. The latest engagement survey, via Culture Amp, ranks DigitalOcean 17 points above the industry benchmark in satisfaction with compensation.
Recruiting.
With the improvements in the economy since the Great Recession, recruiting and hiring have become more urgent—and more agile. To scale up quickly in 2015, GE’s new digital division pioneered some interesting recruiting experiments. For instance, a cross-functional team works together on all hiring requisitions. A “head count manager” represents the interests of internal stakeholders who want their positions filled quickly and appropriately. Hiring managers rotate on and off the team, depending on whether they’re currently hiring, and a scrum master oversees the process.
To keep things moving, the team focuses on vacancies that have cleared all the hurdles—no req’s get started if debate is still ongoing about the desired attributes of candidates. Openings are ranked, and the team concentrates on the top-priority hires until they are completed. It works on several hires at once so that members can share information about candidates who may fit better in other roles. The team keeps track of its cycle time for filling positions and monitors all open requisitions on a kanban board to identify bottlenecks and blocked processes. IBM now takes a similar approach to recruitment.
Companies are also relying more heavily on technology to find and track candidates who are well suited to an agile work environment. GE, IBM, and Cisco are working with the vendor Ascendify to create software that does just this. The IT recruiting company HackerRank offers an online tool for the same purpose.
Learning and development.
Like hiring, L&D had to change to bring new skills into organizations more quickly. Most companies already have a suite of online learning modules that employees can access on demand. Although helpful for those who have clearly defined needs, this is a bit like giving a student the key to a library and telling her to figure out what she must know and then learn it. Newer approaches use data analysis to identify the skills required for particular jobs and for advancement and then suggest to individual employees what kinds of training and future jobs make sense for them, given their experience and interests.
IBM uses artificial intelligence to generate such advice, starting with employees’ profiles, which include prior and current roles, expected career trajectory, and training programs completed. The company has also created special training for agile environments—using, for example, animated simulations built around a series of “personas” to illustrate useful behaviors, such as offering constructive criticism.
What HR Can Learn from Tech
Traditionally, L&D has included succession planning—the epitome of top-down, long-range thinking, whereby individuals are picked years in advance to take on the most crucial leadership roles, usually in the hope that they will develop certain capabilities on schedule. The world often fails to cooperate with those plans, though. Companies routinely find that by the time senior leadership positions open up, their needs have changed. The most common solution is to ignore the plan and start a search from scratch. But organizations often continue doing long-term succession planning anyway. (About half of large companies have a plan to develop successors for the top job.) Pepsi is one company taking a simple step away from this model by shortening the time frame. It provides brief quarterly updates on the development of possible successors—in contrast to the usual annual updates—and delays appointments so that they happen closer to when successors are likely to step into their roles.
Ongoing Challenges
To be sure, not every organization or group is in hot pursuit of rapid innovation. Some jobs must remain largely rules based. (Consider the work that accountants, nuclear control-room operators, and surgeons do.) In such cases agile talent practices may not make sense.
And even when they’re appropriate, they may meet resistance—especially within HR. A lot of processes have to change for an organization to move away from a planning-based, “waterfall” model (which is linear rather than flexible and adaptive), and some of them are hardwired into information systems, job titles, and so forth. The move toward cloud-based IT, which is happening independently, has made it easier to adopt app-based tools. But people issues remain a sticking point. Many HR tasks, such as traditional approaches to recruitment, onboarding, and program coordination, will become obsolete, as will expertise in those areas.
Meanwhile, new tasks are being created. Helping supervisors replace judging with coaching is a big challenge not just in terms of skills but also because it undercuts their status and formal authority. Shifting the focus of management from individuals to teams may be even more difficult, because team dynamics can be a black box to those who are still struggling to understand how to coach individuals. The big question is whether companies can help managers take all this on and see the value in it.
The HR function will also require reskilling. It will need more expertise in IT support—especially given all the performance data generated by the new apps—and deeper knowledge about teams and hands-on supervision. HR has not had to change in recent decades nearly as much as have the line operations it supports. But now the pressure is on, and it’s coming from the operating level, which makes it much harder to cling to old talent practices.
Co-Creating the Employee Experience
by Lisa Burrell
Companies that are adopting agile talent practices are giving a lot of thought to how employees experience the workplace—in some ways, treating them like customers. Diane Gherson, the chief human resources officer at IBM, recently spoke with HBR about how that’s playing out as the iconic tech company revamps its business model. Edited excerpts follow.
HBR: In what sense is IBM putting employee experience at the center of people management?
GHERSON: Like a lot of other companies, we started with the belief that if people felt great about working with us, our clients would too. That wasn’t a new thought, but it’s certainly one we took very seriously, going back about four or five years. We’ve since seen it borne out. We’ve found that employee engagement explains two-thirds of our client experience scores. And if we’re able to increase client satisfaction by five points on an account, we see an extra 20% in revenue, on average. So clearly there’s an impact. That’s the business case for the change.
But it has required a shift in mindset. Before, we tended to rely on experts to build our HR programs. Now we bring employees into the design process, co-create with them, and iterate over time so that we meet people’s needs.
Diane Gherson, IBM’s head of HR
What does that look like in practice?
A good example is employee onboarding—the first process we took a very hard look at. We knew we wanted people to walk out thinking, “I’m superexcited I’m here, and I understand what I need to know to get going.” But we started too small. We approached it in a traditional way that made it all about the orientation class, all about the experience you have on your first day. Once we began asking new hires how their onboarding had gone, we heard things like “I didn’t get my laptop on time,” or “I couldn’t get my credit card in time to get to my first meeting,” or “I had problems accessing the internal network.” All those things affect how someone feels about having joined the company.
Once you realize that, the remit for the onboarding team becomes how people experience the whole process, end to end. To get it right, you have to work with a broader set of players. You bring in Security to make sure the ID badges are there. You bring in Real Estate to make sure people have a physical space and know where to go. You bring in Networking to make sure their remote access is up and running. All that is part of onboarding. It’s not just having a great meeting with a bunch of other new hires on your first day.
It took a while for us to understand that. You have to broaden your scope and stop thinking in silos in order to create a great employee experience.
How has IBM’s approach to learning and development changed?
People consume content on their phones and tablets now—they use YouTube and TED talks to get up to speed on things they don’t know. So we had to put aside our traditional learning-management system and think differently about education and development. Again, we brought in our Millennials, brought in our users, and codesigned a learning platform that is individually personalized for every one of our 380,000 IBMers.
It’s tailored by role, with intelligent recommendations that are continually updated. And it’s organized sort of like Netflix, with different channels. You can see how others have rated the various offerings. There’s also a live-chat adviser, who helps learners in the moment.
We measure HR offerings such as learning with a Net Promoter Score—the ultimate metric for an irresistible experience. Before, we used a classic five-point satisfaction scale. Even if someone rated you a 3.1, you ended up saying they were satisfied, whereas with Net Promoter, you have to be at the far end of the scale for it to mean anything, because you have to subtract all the detractors. It’s much harder to get that, and it gives you much better feedback on what people are experiencing. For learning, at last count, our NPS was 60. That’s in the “excellent” range, but of course there’s still room to improve.
What kinds of tools do you use to customize learning?
With Watson Analytics, we’re able to infer people’s expertise from their digital footprint inside the company, and we compare that with where they should be in their particular job family. The system is cognitive, so it knows you—it has ingested the data about your skills and is able to give you personalized learning recommendations. It tells you, “OK, you need to increase your depth in these areas—and here are the offerings that will help you do that.” You can then pin those or queue them up in your calendar for future learning. The system also looks at how close you may be to earning a digital badge, which we’ve started using in just the past couple of years to demonstrate which employees have applied skills. The tool then helps you achieve the badge by recommending specific webinars and internal and external courses. It’s all based on artificial intelligence. Skills inference is at about 96% accuracy at this point.
“People are less likely to resist change when they’ve had a hand in shaping it.”
How do you know that?
We used to have this laborious manual process of getting people to fill out skills questionnaires and having their managers sign off on them. But that gets outdated really fast. So we stopped doing that. Instead, leaders in particular job families or industries do spot checks on how well we are inferring. They interview employees and identify where they are, comparing that with what the inference was in our system.
IBM has given its performance management system an overhaul as well. How have employees been involved in that process?
As you know, performance management is kind of a lightning rod in most companies. Rather than do the typical thing—which would be to do some benchmarking, pull together a bunch of experts, come up with a new design, and pilot it—we decided to go all out and co-create it with our employees in a sort of extended hackathon. We used design thinking and came up with what you might describe as a “concept car”—something for people to test drive and kick the tires on, instead of just dealing with concepts. We did that in the summer of 2015 and implemented it across the company five months later. That’s the power of engaging the whole workforce—people are much less likely to resist the change when they’ve had a hand in shaping it.
To start the co-creation process, I blogged about it one day and said, “We’d love your input. If you hate it, we’ll start over, no problem. But we really want your thoughts.” We made a few videos about what we thought it might look like. I got 18,000 responses overnight. Fortunately, we had the technology to analyze it all and see what people liked and didn’t like.
At first some people said, “This is such a sham—you already know what you want to do.” But we explained that we really wanted to hear from them, and we got them into various discussion forums. It took a while, but I think we did turn them around. We kept communicating, saying, “OK, you liked this; you didn’t like that. And here are areas where you can’t seem to agree.” Meanwhile, we were putting together prototypes to show people.
I was clear up front that there were some ground rules. For example, we were not going to get rid of performance discussions, and we wanted pay-for-performance. But in general, it was wide open. The whole process took less time than most companies take to redesign their performance management programs, and we involved about 100,000 employees. Finally, we asked, “What do you want to call it?” Tens of thousands of people voted. We had three names in the end, and Checkpoint was selected.
Performance management can never be perfect. But your baby is never ugly. Our employees created their own program, and there is pride in that. You can see it in their ongoing blogs, where we ask them to talk about what’s working and what’s not and to tell us how we can improve the system. We’ve been doing that ever since we put it out there. Their overall message has been “This is what we wanted.” It was cited as the top reason engagement improved. People are getting much more feedback out of this system, in much richer ways. And more important, they are not feeling like spectators in our transformation; they are active participants.
“We’ve been able to swiftly detect problems and commit to doing something about them.”
How are you using “sentiment analysis” to further address employees’ needs?
Sentiment analysis is very helpful in a world where people are always commenting online. Our cognitive technology looks at the words people choose and picks up the tone. It identifies whether it’s positive or negative and then goes deeper, saying whether it’s strongly positive or strongly negative. In that way it’s almost like looking at music—seeing where there are very high notes or very low notes that are loud. It’s always behind our firewall, never external. It’s not looking at any of the information people pass around or at their e-mail content or browsing behavior. It’s just looking at tone in their blogs and comments inside the firewall.
With this approach you can pick up pretty quickly if there’s an area you need to dive into. We’ve been able to swiftly detect problems that are starting to brew and, more important, make a commitment to do something about them. This is the most exciting part of having a social platform to work with. We’ve had several examples of things we did wrong. Some of my folks decided we wouldn’t reimburse for ridesharing. Employees became agitated, and I could quickly respond to a concern that had turned into a petition. “I read all your comments,” I told them, “and you made some great points we hadn’t thought of. We were trying to look out for your security, but on balance, this wasn’t the right choice. Let’s return to our original policy.” All this happened within 24 hours. People felt listened to and were very appreciative.
We had a similar situation about a year ago. We had to impute income when you were traveling to a client site for a full week and, instead of returning home right away, you had your spouse or a friend join you for the weekend. Because we would reimburse the guest’s travel, it created a tax issue. We altered the program because that was getting messy, and again employees were incensed. I can certainly understand why. If you’re on the road all the time, of course you might want your spouse to join you for a weekend. People didn’t want us making the decision for them. That was another case where we quickly got together and said, “Hey, if they want to be responsible for their own taxes, they can do it.” It was a good wake-up call for us to not be so paternalistic.
In organizations where people aren’t physically all together, you can use sentiment analysis to get a sense of where you’ve got trouble spots, where your management isn’t strong enough, where groups of people are expressing negative opinions. It allows you to check in on those sites or groups and find out what’s going on.
Do employees have more power now than in the past?
Yes. So much more weight is now given to what is said inside an organization, because it can be heard outside as well, through social media. Glassdoor is a perfect example. In the past you might have had companies that weren’t great to work for, but only a small circle of people knew about it. Now the whole world knows about it, because it’s on Glassdoor—and that’s turned companies into glass houses. People can look in and see what’s going on and make judgments about whether they want to work there in a way that they weren’t able to before.
Let’s go back to the business reasons behind IBM’s shift to agile talent practices—can you say more about those?
I mentioned client satisfaction. Clients today are looking for speed and responsiveness like never before. In an earlier era what they really wanted was the best product at the best price—efficiency was important, but speed was less so.
In the early 2000s we would have staffed a project with experts from all over the world, and they would have spent a fraction of their time on that project, because they were also working on other projects. They would have joined conference calls, which is always hard because people are in different time zones. And I’m sure they were multitasking while they were on those calls. That project might have taken six months to a year. Now we would take a smaller group of dedicated people and put them together for three months, and they would get it all done using agile methodology. It’s a different way of thinking about how to create value for clients. It responds to their need for speed.
Is there some hope that an agile approach to talent will help IBM make up ground in revenue and growth that it lost in its transition to cloud computing and other businesses?
We’re a company that’s transforming itself: 45% of our revenue comes from businesses we were not in five years ago, and we are an $80 billion company. When you’re going through that kind of shift and seeing a downturn in some of your legacy businesses, and you’re renovating those while you’re launching new businesses, you may see some unevenness in performance. You’re basically changing the tires while you’re driving the car. And yes, that takes agility.
One Bank’s Agile Team Experiment
by Dominic Barton，Dennis Carey & Ram Charan
When web and mobile technologies disrupted the banking industry, consumers became more and more aware of what they could do for themselves. They quickly embraced what Ralph Hamers, CEO of the global banking group ING, calls “banking on the go.”
By 2014 about 40% of all interactions with ING retail customers were coming in through mobile apps. (Now the figure is closer to 60%—and branch visits and calls to contact centers have dropped below 1%.) Even then mobile customers expected easy access to up-to-date information whenever and wherever they logged in. For instance, someone who started a loan transaction during the train ride home from work wanted to be able to continue it on a desktop that night. “Our customers were spending most of their online time on platforms like Facebook and Netflix,” says Hamers. “Those set the standard for user experience.”
That meant ING needed to become nimbler and more user-focused to serve its 30 million–plus customers across the world at every point in their financial journeys. So Hamers worked with Nick Jue, then the CEO of ING’s Netherlands group, to launch a pilot transformation in the headquarters of ING’s largest unit, its Dutch retail operations. The first step was to help other senior leaders and the board envision a new agile, team-based system for deploying, developing, and assessing talent. (ING had already adopted agile and scrum methodologies in its Dutch IT unit, but those ways of working were new to other parts of the organization.) Hamers and his leadership team then met with people at tech companies they admired, learning how their talent systems enabled better customer service. By the spring of 2015 the headquarters of ING Netherlands, home to some 3,500 full-time employees, had replaced most of its traditional structure with a fluid, agile organization composed of tribes, squads, and chapters.
Tribes, Squads, and Chapters
Thirteen tribes were created to address specific domains, such as mortgage services, securities, and private banking. Each tribe contains up to 150 people. (Employees in sales, service, and support functions work outside this structure—in smaller customer-loyalty teams, for instance—but they collaborate with the tribes.) And each has a lead who establishes priorities, allocates budgets, and ensures that knowledge and insights are shared both within and across tribes.
The tribe lead has one other critical responsibility: to create, with input from tribe members, self-steering squads of nine or fewer people to address specific customer needs by delivering and maintaining new products and services. These squads are cross-disciplinary—typically, a mix of marketing specialists, data analysts, user-experience designers, IT engineers, and product specialists. One squad member is designated the “product owner,” responsible for coordinating activities and setting priorities. The squad stays together as long as is required to meet the customer need from start to finish—whether it is, for example, improving user experience on the mobile app or building a particular feature. Some tasks are completed in two weeks; others might take 18 months. Sometimes the squads disband and the members join other ones. Most often, however, squads that are working well stay together and move on to address other customer needs.
By working in such small units and with colleagues from various disciplines, squad members can quickly resolve issues that might previously have bounced from department to department. Information sharing is encouraged through mechanisms such as scrums and daily stand-ups—the kinds of gatherings you’d find at a tech start-up. Seeing a project through from start to finish gives each squad a sense of ownership and connection to the customer.
Implementing an agile talent system doesn’t mean embracing chaos. In fact, a system that’s well designed observes clearly defined rules and safeguards to ensure institutional stability. Every tribe, for example, has a couple of agile coaches to help squads and individuals collaborate effectively in an environment where employees are encouraged to solve problems on the ground rather than pass them on to someone else. Although you might think adapting would be most difficult for long-term bank employees, that’s not so, according to ING Netherlands CIO Peter Jacobs. Many of them “adapted even more quickly and more readily than the younger generation,” he says, perhaps because their expertise now has more impact than in the past, when so many sign-offs were required.
Working in small, cross-functional units, squads can resolve issues quickly.
Then there are the chapters, which coordinate members of the same discipline—data analytics, say, or systems processes—who are scattered among squads. Chapter leads are responsible for tracking and sharing best practices and for such things as professional development and performance reviews. Think of chapters as a way of retaining the helpful parts of traditional management even while dispensing with time-consuming handoffs and bureaucracy.
Regular assessments are built into the system. Every two weeks squads review their work. Says Hamers, “They get to decide how they will continue to improve the product for our customers, or if they want to ‘fail fast.’” (Learning from failure is applauded.) Squads also do a thorough self-assessment after completing any engagement, and tribes perform quarterly business reviews (QBRs), looking at their biggest successes and failures, reviewing their most important learnings, and articulating goals for the next three months.
These safeguards help counter what Vincent van den Boogert, the current CEO of ING Netherlands (and part of the team that launched the new organizational structure), sees as the two biggest challenges of a squad-based system. One is the possibility that self-empowered squads responding primarily to the needs of customers might embark on changes that aren’t in sync with company strategy. The QBRs mitigate that risk. The second challenge is somewhat counterintuitive. Self-evaluating squads are sometimes content with the incremental improvements they make every two weeks. The QBRs help in that regard, too, because top management uses them to formulate and reinforce stretch goals.
More than two years in, Hamers considers the talent experiment a big success. Customer satisfaction and employee engagement are both up, and ING is quicker to market with new products. So the bank has started to roll out this new way of working to the roughly 40,000 employees outside its home country. For Hamers, the change can’t come soon enough. The apps for each of ING’s 13 retail markets vary in appearance, design, and function. Hamers wants to make things much simpler so that any customer, anywhere, will encounter the same ING. “Tech companies have one platform across the globe,” he says. “No matter where you use Netflix, Facebook, or Google, you get the same service. ING must do the same. That is the only way we will bring all our customers along into the future of banking.”
People Analytics
2018年02月26日
People Analytics
人工智能如何改变人才获取 How Artificial Intelligence Is Changing Talent Acquisition现在大家都在关注招聘AI，并就如何改变招聘方式进行了大量的讨论。招募人工智能是下一代软件，旨在改进或自动化招聘工作流程的某些部分。
作者：Ji-A Min
人工智能对招聘的兴趣已经由三大趋势引发
经济的改善：最近的经济收益创造了一个候选人驱动型市场，这使得人才竞争比以往更加激烈。这一竞争只会继续增加 - LinkedIn调查的 56％的人才招聘领导者认为他们的招聘数量将在2017年增长。
对更好技术的需求：虽然人才招聘预计会增加，但是66％的人才招聘负责人表示他们的招聘团队将保持相同规模甚至缩小规模。这意味着时间有限的招聘人员需要更好的工具来有效地简化或自动化他们的工作流程的一部分，理想情况下用于最耗时的任务。
数据分析的进步：随着技术变得快速和成本效益足以收集和分析大量数据，人才招聘领导者越来越多地要求他们的招聘团队展示基于数据的雇佣质量指标，如新员工的表现和营业额。
人工智能在招聘中越来越受欢迎，这为招聘人员提高他们的能力提供了令人兴奋的机会，但同时也存在很多关于如何最佳利用人才的困惑。
为了帮助您理解这一切，以下是招聘人工智能最有前途的三个应用程序。
应用＃1：AI用于候选人采购
候选人采购仍然是一个主要的招聘挑战：最近的一项调查发现，46％的人才招聘领导表示他们的招聘团队正在为吸引合格的候选人而奋斗。
候选人采购人工智能技术可以搜索人们离线的数据（例如简历，专业投资组合或社交媒体档案），以找到符合您工作要求的被动候选人。
这种用于招聘的AI可以简化采购流程，因为它可以同时搜索多个候选人来源。这取代了自己手动搜索它们的需求，并可能节省每个请求的小时数。您节省采购的时间可以用来吸引，预选和面试最强大的候选人。
应用#2：人工智能进行候选人筛选
当您收到的75-88％的简历不合格时，很容易明白为什么简历筛选是招聘中最令人沮丧和耗时的部分。对于零售和客户服务等大批量招聘，大多数招聘团队没有时间手动筛选他们每个公开角色收到的数百到数千份简历。
AI筛选旨在自动执行简历筛选流程。这种智能筛选软件通过使用岗位聘用数据（例如业绩和营业额）为新申请人提供招聘建议，为ATS增添了功能。
它通过应用所学到的关于现有员工的经验，技能和其他资质的信息来自动筛选和评分新候选人，从而提出这些建议。这种类型的技术还可以通过使用关于以前的雇主和候选人的社交媒体档案的公共数据源来丰富简历。
AI进行简历筛选可实现低价值，重复性任务，并允许招聘人员将时间重点放在更高价值的优先事项上，如与候选人交谈并与其进行交流以评估他们的适合度。
应用＃3：AI用于候选人匹配
与采购相比，候选人匹配可能是一个更大的挑战：52％的招聘人员表示，他们工作中最难的部分是从大型申请人池中确定合适的人选。
用于候选人匹配的AI使用一种算法来识别打开的请求的最强匹配。匹配算法分析候选人的个性特征，技能和工资偏好等多种数据来源，根据工作要求自动评估候选人。
例如，LinkedIn求职公告通过将求职者描述中的技能与其LinkedIn个人资料中的申请人技能进行匹配来对候选人进行排名。人才市场使用匹配算法来匹配候选人社区以开放角色。这些人才市场通常迎合特定的候选技能，如软件开发或销售。
人工智能匹配用于从那些已经加入并且正在积极寻找新角色或者对新机会非常开放的人中找出最合格的候选人。这意味着招聘人员不需要浪费时间来吸引那些对新角色不感兴趣的被动应聘者。
关于人工智能的力量，让候选人与工作岗位相匹配的不同观点，请参阅“ 尽管您阅读或听取的内容，采购活动和确实如此”。
AI和招聘的未来
专家预测人工智能招聘会转变招聘人员的角色。由于低价值，耗时的招聘任务通过人工智能技术变得简化和自动化，招聘人员的角色有可能变得更具战略性。
了解AI如何提高其能力的招聘人员将通过在采购，简历筛选和候选人匹配方面节省几十个小时，从而提高效率。
人工智能招聘承诺释放招聘人员与候选人交流的时间，以确定合适人选，并确定候选人的需求并希望说服他们担任角色。它有可能授权他们与招聘经理和人才招聘领导者合作，根据未来增长和收入计划积极的招聘举措，而不是反应性回填。
了解如何最好地利用这项新技术的招聘人员将获得更高的KPI，如更高的招聘质量和更低的营业额。
以上由AI翻译完成。供参考
How Artificial Intelligence Is Changing Talent Acquisition
AI for recruiting is on everyone’s mind these days with a lot of talk on how it’s going to transform recruiting. Artificial intelligence for recruiting is the next generation of software designed to improve or automate some part of the recruiting workflow.
Interest in AI for recruiting has been sparked by three major trends:
The improving economy: The recent economic gains have created a candidate-driven market that’s made competing for talent tougher than ever. This competition will only continue to increase – 56% talent acquisition leaders surveyed by LinkedIn believe their hiring volume will grow in 2017.
The need for better technology: Although hiring is predicted to increase, 66% of talent acquisition leaders state their recruiting teams will stay the same size or even shrink. This means time-constrained recruiters need better tools to effectively streamline or automate a part of their workflow, ideally for tasks that are the most time-consuming.
The advancements in data analytics: As technology becomes fast and cost-effective enough to collect and analyze vast quantities of data, talent acquisition leaders are increasingly asking their recruiting teams to demonstrate data-based quality of hire metrics such as new hires’ performance and turnover.
The growing popularity of AI for recruiting represents exciting opportunities for recruiters to enhance their capabilities but there’s also a lot of confusion about how to best leverage it.
To help you make sense of it all, here are the three most promising applications for AI for recruiting.
Application #1: AI for candidate sourcing
Candidate sourcing is still a major recruiting challenge: a recent survey found 46% of talent acquisition leaders say their recruiting teams struggle with attracting qualified candidates.
AI for candidate sourcing is technology that searches for data people leave online (e.g., resumes, professional portfolios, or social media profiles) to find passive candidates that match your job requirements.
This type of AI for recruiting streamlines the sourcing process because it can simultaneously search through multiple sources of candidates for you. This replaces the need to manually search them yourself and potentially saves you hours per req. The time you save sourcing can be spent attracting, pre-qualifying, and interviewing the strongest candidates instead.
Application #2: AI for candidate screening
When 75-88% of the resumes you receive are unqualified, it’s easy to see why resume screening is the most frustrating and time-consuming part of recruiting. For high-volume recruitment such as retail and customer service roles, most recruiting teams just don’t have the time to manually screen the hundreds to thousands of resumes they receive per open role.
AI for screening is designed to automate the resume screening process. This type of intelligent screening software adds functionality to the ATS by using post-hire data such as performance and turnover to make hiring recommendations for new applicants.
It makes these recommendations by applying the information it learned about existing employees’ experience, skills, and other qualifications to automatically screen and grade new candidates. This type of technology can also enrich resumes by using public data sources about previous employers and candidates’ social media profiles.
AI for resume screening automates a low-value, repetitive task and allows recruiters to re-focus their time on higher value priorities such as talking and engaging with candidates to assess their fit.
Application #3: AI for candidate matching
Candidate matching can be an even bigger challenge than sourcing: 52% of recruiters say the hardest part of their job is identifying the right candidates from a large applicant pool.
AI for candidate matching uses an algorithm to identify the strongest matches for your open req. Matching algorithms analyze multiple sources of data such as candidates’ personality traits, skills, and salary preferences to automatically assess candidates against the job requirements.
For example, a LinkedIn job posting ranks candidates by matching the skills on your job description to applicants’ skills on their LinkedIn profiles. Talent marketplaces use matching algorithms to match their community of candidates to open roles. These talent marketplaces usually cater to specific candidate skill sets such as software development or sales.
AI for matching is used to identify the most qualified candidates from those who have opted-in and are either actively looking for a new role or are very open to a new opportunity. This means recruiters don’t need to waste time trying to attract passive candidates who just aren’t interested in a new role.
People Analytics
2018年02月19日
People Analytics
英文赏析：Will a Chat Bot Be Your Next Learning Coach?
By Margie Meacham
Eighty percent of major companies expect to be using artificial intelligence by 2020, but their training departments are likely to be the last places you’ll find it. We need to fix that.
A recent survey of Millennials revealed that 40 percent of them interact with a chat bot, a program that simulates a human conversation, on a daily basis; another survey indicates that many people prefer chat bots over humans for certain types of customer support transactions.
While other industries are already developing AI, the learning industry seems to be lagging behind. It’s pretty hard to implement something you don’t understand, so let’s start there.
Artificial Intelligence
Artificial intelligence, or AI, is a branch of computer science that aims to create intelligent machines, capable of performing problem-solving, pattern recognition, and learning without explicit programming.
AI requires vast amounts of data to create intelligent machines, and Big Data requires intelligent machines to perform the massive calculations necessary to find meaningful patterns and connections. For this reason, you will often find Big Data and AI are employed together and support each other.
Big Data
“Big Data” refers to data sets that are so voluminous and complex that traditional data processing application software packages are inadequate to deal with them. Big Data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.
Big Data analytics examines these massive, varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that drives artificial intelligence.
3 Dimensions of Big Data
There are three dimensions to Big Data: velocity, variety, and volume.
Velocity
Data is coming at us from all directions, and it is coming faster every day. To benefit from Big Data insights, companies must be able to capture, analyze, and use this massive amount of information as quickly as it is coming in. Human beings alone could never keep up with this firehose of information, so Big Data solutions must include strategies to control and keep up with the speed of incoming data. Bring in the smart machines!
Variety
Consider your own experience as a digital consumer. In a single hour, you may read an email on your PC, send a text on your phone, download a podcast, watch a video, and post a tweet. Each requires different strategies for capture and analysis—and these are only a few examples of the diversity of data available online today.
VolumeHere is just a snapshot of the sheer volume of data that came at us every day of 2017:
456,000 tweets on Twitter
50,926 videos viewed on Buzzfeed
3,607,080 Google searches.
The amount of data coming from your learning management system (LMS) and performance management software is puny compared to the onslaught coming from social media; but it is part of the Big Data mosaic, and most of us are simply not taking advantage of the information we have readily available.
Machine Learning
Machine learning is an application of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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In other words, machine learning focuses on the development of computer programs that can access large amounts of data and change their behavior or programming based on that information, without human intervention. Uses for machine learning in talent development include:
Assess and predict job performance.
Predict the competencies that will be needed in 10 years so learners can develop relevant skills today.
Provide personalized conversation about new information, performance coaching, or motivation on a 24-hour basis, without the need for a human coach.
Identify learner competencies and gaps to make better training and education suggestions that are truly personalized to the individual.
Examples of AI in Talent Development
Here are just a few examples of education-focused AIs that are already in use. Many early adopters are in the higher education arena, but the ideas work equally well in corporate training or K-12 education.
Jill Watson, the virtual teaching assistant at the University of Georgia, communicates with students via email.
Virtual tutors can help each learner move at a pace that is right for them.
Penn State is using chat bots to help teachers gain confidence handling difficult conversations, like bullying or hate language in class.
Think grading essays requires the human touch? Think again! At Stanford, an AI grading system achieved an 81 percent accuracy rate when compared to essays graded by humans.
Beware These Beginner Mistakes
Because some AI applications are still in the early days on the hype cycle, I interviewed an AI expert at one of my client organizations to find out what common mistakes she sees in chat bot projects led by early adopters.
Here’s a summary of her list.
Garbage In/Garbage Out (GIGO)
Many projects fail because project managers forget to check data quality, or do not have the right approach to identify and resolve these issues. When we analyze incomplete or “dirty” data sets, our AI ends up making decisions and recommendations based on a poor foundation.
Apples and Oranges
Comparing unrelated data sets or data points will result in inferring relationships or similarities that do not exist.
Overly Narrow Focus
Some projects are designed to consider one data set without considering other data points that might be crucial for the analysis. For example, a project set up to analyze learner pass/fail rates while ignoring the course completion rate may inflate performance results.
Cool but Useless
Some AI projects are quick to deliver but fail to make a significant impact on the learner’s everyday experience. Ensure that you have the right strategy to deliver the most value to your learners, and avoid giving them something cool that doesn’t really help them learn.
Getting Started
My advice is to just get on with it. Make a point of learning something about AI and machine learning every day, always with an eye to how you might be able to use it in your own organization. Here are a few suggestions:
Check out datascience.com for a huge list of data science resources.
Take this course from Google on Udacity—it’s free, and quite well done.
Brainstorm some ideas with colleagues. There are some great ideas here, and even more ideas here.
Build a Bot
There are dozens of platforms that let you create free chat bots for specific messaging apps without any special skills or coding knowledge. Snatchbot, for example, can be used on Facebook Messenger, Slack, WeChat, Skype, and more. It’s easy to use, and the interface is probably already familiar to many of your users. And Botsify has a variety of bot templates to get you started, including a whole list of education bots. Looking for more do-it-yourself tools? Here’s a nice list from business2community. com.
Engage With Colleagues
You might be surprised how many of your colleagues are eager to test the waters with a chat bot or other educational AI application. You won’t find them unless you join the conversation. One place to start is by attending the ATD 2018 International Conference & Exposition (for example, Elliott Masie will talk about some innovations changing workplace learning during the session, Learning Trends, Disrupters, and Hype in 2018) or any of our other conferences designed to educate, engage, and inspire you.
Will You Be Replaced by a Chat Bot?
While there is a vast difference of opinion on how AI is shaping the very near future of work and learning, one thing I know for sure: Those of us who are not part of the disruption will become lost in the dust that the disruptors kick up. I plan on being in front of it
Margie Meacham is an adult learning expert with a master of science in learning technologies and more than 15 years of experience in the field. A self-described “scholar-practitioner,” Margie collaborates with like-minded instructional designers to find practical applications of neuroscience to instructional design.
People Analytics
2018年02月19日
People Analytics
“人员分析现在可以成为战略性竞争优势”
工业工程师弗雷德里克泰勒在1911年发表了他的报告“ 科学管理”，该报告研究了钢厂工厂工人的流动和行为，从而开始了这一趋势。此后，公司已经部署了数千次参与调查，研究了最高领导者的特征，对留存率和营业额进行了无数次评估，并建立了大量的人力资源数据仓库。所有这些努力都是为了弄清楚“我们能做些什么来让我们的人们获得更多收益？”
那么现在这个域被称为人们的分析，它已经成为一个快速增长的核心业务举措。一项题为“ 高影响力人物分析 ”的研究报告由Deloitte在去年11月由Bersin完成，发现69％的大型组织拥有人员分析团队，并积极构建与人员相关数据的综合存储。
为什么增长和为什么业务势在必行？几个技术和商业因素相互碰撞使这个话题变得如此重要。
首先，组织拥有比以往更多的与人员相关的数据。由于办公生产力工具，员工证章阅读器，脉搏调查，集成的企业资源规划系统和工作中的监控设备的激增，公司拥有大量关于员工的详细数据。
公司现在知道人们与谁交流，他们的地点和旅行时间表，工资，工作经历和培训计划。内置于电子邮件平台中的组织网络分析的新工具可以告诉正在与谁交流的领导者，用于音频和面部识别的新工具识别谁处于压力之下，以及摄像机和热传感器甚至可以确定人们在他们身上花费了多少时间书桌。
可以认为，这些信息大部分都是保密和私密的，但大多数员工并不介意获取这些数据的组织，只要他们知道正在改进他们的工作体验，正如2015年会议委员会的研究所显示的那样，Big数据并不意味着大 哥哥。虽然从5月25日起可执行的欧盟通用数据保护条例标准将会将隐私权和治理责任放在人力资源部门，但雇主正在加紧处理这些数据并小心处理这些数据。
其次，作为获得所有这些数据的结果，公司现在可以学习重要而有力的事情。不仅高管们被迫就多元化，性别薪酬公平和营业额等议题进行报告，而且他们现在还可以使用人员分析来了解生产力，技能差距和长期趋势，这些可能会威胁或创造业务风险。
例如，一个组织发现欺诈和盗窃事件是“具有传染性”，导致同一楼层的其他员工在一定距离内出现类似的不良行为。另一种方法是使用情绪分析软件来衡量组织中的“情绪”，并根据他们的沟通模式来识别具有高风险项目的团队。
许多组织现在都在研究营业额，甚至可以通过监测电子邮件和社交网络行为来预测它，从而使管理人员能够在辞职前指导高绩效员工。组织现在使用分析和人工智能或人工智能来解码职位描述，识别造成偏倚招聘池的单词和短语，并防止性别和种族多样性。制造商使用人员分析来识别可能发生事故的员工，而咨询公司可以预测哪些人可能会因过多的旅行而被烧毁，而汽车公司现在知道为什么某些团队按时完成项目，而其他人则总是迟到。
因此，人工智能进入领域，给予它更多的权力和规模。一个新的基于人工智能的分析工具会向管理人员发送匿名电子邮件，询问简单问题以评估管理技能。通过其精心设计的算法，它为管理人员提供了一套无需赘述的建议，并在短短三个月内将管理效率提高了8％。
据Sierra-Cedar 2017人力资源系统调查显示，对于人力资源部门而言，人员分析现在是公司希望替换或升级人力资源软件的首要原因。
但对于首席执行官，首席财务官和首席运营官来说，这更重要。当一个销售团队落后于其配额实现或者商店的销售数字落后时，为什么领导者不会问“我们可能能够解决的团队中的人员，实践和管理者有什么不同？”或者甚至更大问题是“如果我们想通过收购德国的某家公司来发展我们的业务，文化和组织的影响会是什么？”这些关键的战略问题都可以通过人员分析来解决。
这门学科的历史是战术性的，有点神秘。多年来，工业心理学家领导了这项工作，主要关注员工敬业度和营业额。然而，今天，该行业正在采取新的行动，将其精力重新集中在运营，销售，风险和绩效指标上。技术工具在这里，公司已经有人工智能工程师准备以强大而有预见性的方式分析数据。分析人士表示，这个领域将会持续增长，请记住，对于大多数企业而言，劳动力成本是资产负债表中最大和最可控制的支出。
底线很明显：人们的分析现在可以成为战略竞争优势。专注于这一领域的公司可以出租，淘汰和淘汰竞争对手。
以上由AI自动翻译。
Fredrick Taylor, an industrial engineer, started this trend in 1911 when he published his report Scientific Management, which studied the movement and behaviour of factory workers in steel mills. Since then companies have deployed thousands of engagement surveys, studied the characteristics of top leaders, done countless reviews of retention and turnover, and built massive human resources data warehouses. All in an effort to figure out “what can we do to get more out of our people?”
Well now this domain is called people analytics and it has become a fast-growing, core-business initiative. A study, entitled High-Impact People Analytics and completed last November by Bersin by Deloitte, found that 69 per cent of large organisations have a people analytics team and are actively building an integrated store of people-related data.
Why the growth and why the business imperative? Several technical and business factors have collided to make this topic so important.
Firstly, organisations have more people-related data than ever before. Thanks to the proliferation of office productivity tools, employee badge readers, pulse surveys, integrated enterprise resource planning systems and monitoring devices at work, companies have vast amounts of detailed data about their people.
Companies now know who people are communicating with, their location and travel schedules, their salary, job history and training plans. New tools for organisational network analysis, built into email platforms, can tell leaders who is communicating with whom, new tools for audio and facial recognition identify who is under stress, and video cameras and heat sensors can even identify how much time people spend at their desks.
It could be argued that much of this information is confidential and private, but most employees don’t mind organisations capturing this data, as long as they know it is being done to improve their work experience, as shown in 2015 Conference Board research, Big Data Doesn’t Mean Big Brother. While European Union General Data Protection Regulation standards, enforceable from May 25, will put the burden of privacy and governance on HR departments, employers are stepping up to this and treating such data with great care.
Secondly, as a result of having access to all this data, companies can now learn important and powerful things. Not only are executives being forced to report on topics such as diversity, gender pay equity and turnover, but they can also now use people analytics to understand productivity, skills gaps and long-term trends that might threaten or create risk in their business.
One organisation, for example, found incidents of fraud and theft were “contagious”, causing similar bad behaviour among other employees on the same floor within a certain distance. Another is using sentiment analysis software to measure “mood” in the organisation and can identify teams with high-risk projects just from the patterns of their communication.
Many organisations now study turnover and can even predict it before it occurs by monitoring email and social network behaviour, enabling managers to coach high performers before they resign. Organisations now use analytics and artificial intelligence or AI to decode job descriptions, identifying words and phrases that create biased recruitment pools and prevent gender and racial diversity. Manufacturers use people analytics to identify workers who are likely to have accidents, while consulting firms can predict who is likely to be burnt out from too much travel and automotive companies now know why certain teams get projects done on time when others are always late.
AI is, therefore, entering the domain, giving it even more power and scale. A new AI-based people analytics tool sends anonymous emails to a manager’s peers asking simple questions to assess managerial skills. Through its carefully designed algorithms, it gives managers an unthreatening set of recommendations and has improved managerial effectiveness by 8 per cent in only three months.
For human resources departments, people analytics is now the number-one reason companies want to replace or upgrade their HR software, according to the Sierra-Cedar 2017 HR Systems Survey.
But for chief executives, chief financial officers and chief operating officers, it’s even more important. When a sales team is behind its quota attainment or a store’s sales numbers fall behind, why wouldn’t a leader ask “what’s different about the people, practices and managers at those teams that we may be able to address?” Or an even bigger question is “if we want to grow our business by acquiring a given company in Germany, what will the cultural and organisational impact be?” These critical strategic questions can all be answered by people analytics.
The history of this discipline is tactical and somewhat arcane. For years industrial psychologists led the effort and focused primarily on employee engagement and turnover. Today, however, the industry is taking on a new light, refocusing its energy on operational, sales, risk and performance measures. The technology tools are here and companies have AI engineers ready to analyse the data in a powerful and predictive way. And analysts say this domain will grow for years to come; remember that for most businesses, labour costs are the largest and most controllable expense on the balance sheet.
The bottom line is clear: people analytics can now become a strategic competitive advantage. Companies that focus in this area can out-hire, out-manage and out-perform their competitors.
People Analytics
2018年02月13日
People Analytics
AI 人工智能中对HR的常见术语解释，没事可以了解下在工作环境中准备人工智能对于技术厌恶的招聘团队来说是一个令人望而生畏的。
对于那些探索他们的业务意味着什么，这里有8个基本的解释开始：
算法：算法是在解决问题或计算中应遵循的一组规则。算法需要在招聘过程中生成大量数据，并将其转换为HR可用于候选人选择的信息。在之前的一项研究中，通过算法招聘的候选人比人力资源招聘的人员长15％。算法有助于提高候选人的选择并减少不良招聘的可能性。
人工智能（AI）：人工智能（AI）通常被称为“第四次工业革命”，它是一种能够模仿智能人类行为的机器，其中包括做决策和执行基本任务，如解决问题，计划和学习。AI可以自动执行重复和平凡的管理任务，包括整个招聘过程中的筛选和申请人更新。这也是聊天机器人的兴起和视频放映的使用背后的原因。
聊天机器人：聊天机器人的简称，聊天机器人在人才获取方面越来越多。与苹果的Siri或亚马逊的Alexa一样，招聘中的聊天机器人使用人工智能（例如，机器学习 - 见下文）来理解问题并作出回应。Chatbots可以在不同的平台上使用，包括电子邮件，消息应用程序和通过您的申请人跟踪软件。Chatbots旨在模拟与您就业网站的访问者的对话，并正在迅速成为高容量招聘的基本技术工具。聊天机器人有效地使用，为您的招聘过程添加更吸引人的互动元素。今年早些时候进行的一项调查发现，在申请过程中，超过一半的候选人愿意与聊天机器人进行互动。
游戏化：游戏化将游戏的常见元素应用于其他在线活动领域，包括市场营销。在招聘过程中，毕业生雇主经常使用劳埃德银行集团，德勤和普华永道倍受青睐的Multipoly，以吸引年轻人才，创造更具吸引力的候选人经验。通过人力资源技术将游戏化融入您的招聘流程中。
机器学习：类似于人工智能，机器学习为AI提供了更智能的算法。在招聘中，机器学习可以减少您的聘用时间，并用于自动化候选人筛选，通常利用招聘分析中与最成功的人员相关的数据。人力资源软件中复杂的机器学习算法可用于通过语言选择甚至面部表情来评估候选人的潜在文化适应性。
人员分析：人员分析将数据和分析结合起来，深入了解与员工相关的一系列问题，包括领导力，绩效管理和招聘。要了解更多信息，请参阅我们以前的文章，其中提供了有关人员分析的更详细的介绍。
情绪分析：这可能不是你熟悉的词，但情绪分析解释了语言对人的影响，无论是消极的，积极的还是中立的。在招聘时可以用来分析你的工作岗位的措辞的影响。例如，去年我们报道说，在社交媒体平台Buffer的“工作岗位”中用'开发者'取代'黑客'这个词，看到女性候选人申请空缺的人数有所增加。在招聘软件中的人力资源分析提供了更多的洞察力，如何使用特定的话可以阻止人才适用于您的工作。使用情感分析的软件也可以提出更合适的词汇来吸引更多元化的人才库。
图灵测试：图灵测试是由科学家阿兰·图灵（Alan Turing）在1950年设想的，前提是“机器可以想象？今天，它指的是人工智能的潜力，以说服人们，而不是一个机器与人互动。其中最成功的例子是第三次获得2017年Loebner奖（基于图灵测试）的Mitsuku聊天机器人，但尚未说服评委是人类。 随着工作场所的自动化程度的提高，这是一个与AI有关的术语。
以上由AI 自动翻译。
Algorithms : An algorithm is a set of rules to be followed in a problem solving situation or calculation. Algorithms take large amounts of data generated during the hiring process and transform it into information that HR can use in candidate selection. In a previous study, candidates recruited via algorithms remained in their job 15% longer than those hired by HR. Algorithms help to improve candidate selection and reduce the potential for a bad hire.
Artificial Intelligence (AI) : Often referred to as the ‘fourth industrial revolution’, artificial intelligence (AI) is a machine that is capable of imitating intelligent human behaviour, which includes making decisions and performing basic tasks such as problem solving, planning and learning. AI automates repetitive and mundane admin tasks, including screening and applicant updates throughout the hiring process. It is also behind the rise in chatbots and the use of video screening.
Chatbots : Short for chat robot, chatbots are becoming more prolific in talent acquisition. Like Apple’s Siri or Amazon’s Alexa, chatbots in recruitment use artificial intelligence (eg, machine learning - see below) to comprehend questions and respond. Chatbots can be used across different platforms, including e-mail, messaging apps and through your applicant tracking software. Chatbots are designed to simulate conversations with visitors to your careers site and are rapidly becoming an essential tech tool for high volume recruitment. Used effectively, chatbots add a more engaging and interactive element to your hiring process. A survey carried out earlier this year found that over half of candidates are comfortable interacting with chatbots during the application process.[1]
Gamification : Gamification applies the common elements of game playing to other areas of online activity, including marketing. In recruitment it is frequently used by graduate employers, including Lloyds Banking Group, Deloitte and in PwC’s popular Multipoly to attract young talent and create a more engaging candidate experience. Incorporate gamification into your recruitment process through your HR technology.
Machine learning : Similar to artificial intelligence, machine learning provides AI with the algorithms that make it more intelligent. In hiring, machine learning can reduce your time to hire and is used to automate candidate screening, often utilising the data available in your recruitment analytics relating to your most successful hires. Sophisticated machine learning algorithms in HR software can be used to evaluate the potential cultural fit of a candidate through language choice and even facial expressions.
People analytics : People analytics combines data and analysis to gain insight into a range of issues related to your employees, including leadership, performance management and recruitment. For more insight, please see our previous article which provides a more detailed introduction to people analytics.
Sentiment analysis : It may not be a term you are familiar with but sentiment analysis interprets the effect that language has on people, whether negative, positive or neutral. In recruitment it can be used to analyse the impact of the wording of your job posts. For example, last we year we reported that by replacing the word ‘hacker’ with ‘developer’ in their job posts, social media platform Buffer saw an increase in the number of female candidates applying to their vacancies. HR analytics in your recruitment software provide more insight into how the use of specific words can deter talent from apply to your jobs. Software which uses sentiment analysis can also suggest more suitable words to attract a more diverse talent pool.
The Turing Test : The Turing Test was conceived by scientist Alan Turing in 1950 based on the premise 'can machines think?' Today it refers to the potential for artificial intelligence to convince people they are interacting with a person rather than a machine. One of the most successful examples is the Mitsuku chatbot which has been awarded the 2017 Loebner Prize[2] (based on the Turing Test) for the third time but has yet to convince the judges it is human. It's a term you may hear more of in relation to AI as automation in the workplace rises.
People Analytics
2018年02月12日
People Analytics
Josh Bersin：2018年人力资源技术：比以往更加智能化 HR Technology for 2018 - More Intelligent than Ever
几乎每一位与我交谈的人力资源供应商都声称拥有基于人工智能（AI）的解决方案，预测分析，聊天机器人或其他形式的算法解决方案，以使HR更好。
正如我所了解的所有这些产品，并开始看到他们的行动，让我给你什么寻找提示。
在招聘市场上，数据确实在推动我们的未来。由于社交网络的无处不在以及数十种智能采购和评估工具，我们的研究表明，人工智能正在创造巨大的价值。在您寻找新的招聘工具（采购，候选人评估，智能聊天机器人和移动招聘平台）时，请供应商向您展示其AI如何工作。询问如何作出决定，以及它可能适用于您的例子。这些供应商远远领先于学习曲线，价值将变得清晰。
在面试管理中，也越来愈多的工具开始提供候选人与面试官的协调沟通，自助服务等，比如优面宝，通过自动化的协调沟通机制安排好候选人的面试时间等。
在学习和发展市场上，现在很多学习管理系统（LMS）平台，学习体验平台和微型学习平台都使用人工智能和算法解决方案来推荐内容，策划内容，并通过最合适的内容来指导学习者学习。这些供应商中的许多都有丰富的经验分析通过内容的最佳路径，正确的时间来查看下一个内容，甚至正确的学习模块来查看您的信心，你的理解的主题。学习活动数据现在可以通过体验API或xAPI（一种记录和跟踪学习过程中点击的所有内容的方式）获得，因此所有这些供应商都变得“聪明”。
在员工敬业度和调查市场，同样的AI波即将到来。一系列供应商的产品开始作为参与和脉搏调查工具，现在提供文本分析，情感分析，词云和员工情绪的智能评估。他们中的一些人可以测量信任网络，并使用组织网络分析来识别网络中的可信任人员，甚至指出可能存在欺诈或不良行为的领域。虽然这些软件都不是完美的，但它比单独阅读每条评论要好，可以让管理者更好地了解他们如何与同行进行对比。
在绩效管理市场中，持续绩效管理软件现在通过查看您在工作中获得的反馈模式，提供活动流，公共和私人评论以及组织网络分析。到时候，这些平台会向管理人员推荐学习和辅导，有些已经这样做了。
在员工自助服务和案例管理方面，平台也变得更加智能。您现在不仅可以在线（或通过您的消息系统）与您的员工系统进行聊天，还可以发送消息（“星期五预订我的休假日”），系统将执行交易。很快，它会向你推荐什么课程，如何放慢和放松以及其他员工福利。
我可以继续下去。市场上大多数人力资源工具都包含“人工智能”和“智能”这两个词，越来越多的人开始工作。
虽然这一切都是积极的，而且肯定会让我们的工作更轻松，但是让我也给你一个警告：AI不是魔法; 它只是高度精炼的统计和数学模型，试图根据大量数据预测和推荐行动。如果你没有足够的数据，AI可能没有那么有用。所以听起来很令人兴奋，我建议你让供应商给你一个真实世界的演示，并尽可能多的参考。
在我看来，AI，预测分析，情感分析，视觉识别和自然语言界面的成熟速度比我们预期的要快得多。所有这些都将影响我们的人力资源技术。只要确保你买的东西确实符合你的需求，并且你所实施的“智能”在你的组织需要的领域是聪明的。
Josh Bersin是德勤咨询（Deloitte Consulting LLP）Bersin™的负责人和创始人。本文件中使用的“Deloitte”是Deloitte LLP的子公司Deloitte Consulting LLP。请参阅www.deloitte.com/us/about，了解我们法律结构的详细说明。根据公共会计规则和条例，某些服务可能无法向证明客户提供。
以上由AI翻译，下面是英文原文：
Almost every HR vendor I talk with claims to have artificial intelligence (AI)-based solutions, predictive analytics, chatbots or some other form of algorithmic solution to make HR better. As I've learned about all these products and started to see them in action, let me give you tips on what to look for.
In the recruitment market, data is really driving our future. Thanks to the ubiquitous nature of social networks and dozens of intelligent sourcing and assessment tools, our research shows, AI is creating significant value. As you search for new recruiting tools (sourcing, candidate assessment, intelligent chatbots and mobile recruiting platforms), ask the vendor to show you how its AI works. Ask to see how decisions are made and for examples of where it might apply to you. These vendors are well ahead of the learning curve, and the value will become clear to you.
In the learning and development market, many learning management system (LMS) platforms, learning experience platforms, and micro-learning platforms now use AI and an algorithmic solution to recommend content, curate content and guide learners through the most appropriate content to learn. Many of these vendors have extensive experience analyzing the best path through content, the right time to view the next content and even the right learning module to view based on your confidence in your understanding of the subject matter. Learning activity data is now available through the Experience API, or xAPI (a way to record and track everything you click on while learning), so all these vendors are becoming "intelligent."
In the employee engagement and survey market, the same AI wave is coming. A flurry of vendors whose products started as engagement and pulse survey tools now provide text analytics, sentiment analysis, word clouds and intelligent assessment of employee sentiment. Several of them can measure trust networks and use organizational network analysis to identify trusted people in your network and even point out areas of potential fraud or bad behavior. While none of this software is perfect, it's better than trying to read every comment individually and can certainly give managers a better idea of how they stack up against their peers.
In the performance management market, software for continuous performance management now provides activity streams, public and private comments, and organizational network analysis by looking at the patterns of feedback you get on the job. In time, these platforms will recommend learning and coaching to managers, and some do this already.
In the area of employee self-service and case management, the platforms are also getting smarter. Not only can you now chat with your employee system online (or through your messaging system), you can send it messages ("Book my vacation day on Friday") and the system will perform a transaction. Soon, it will actually make recommendations to you on what courses to take, how to slow down and relax, and other employee benefits.
I could go on and on. It feels like the words "AI" and "intelligent" have been included on most HR tools in the market, and more and more of this is starting to work.
While all this is positive and definitely making our work lives easier, let me also give you a warning: AI is not magic; it is simply highly refined statistics and mathematical models that try to predict and recommend action based on a mass amount of data. If you don't have enough data, the AI may not be as useful. So as exciting as it sounds, I recommend you ask the vendor to give you a real-world demo and talk with as many references as you can.
There's no question in my mind that AI, predictive analytics, sentiment analytics, visual recognition and natural language interfaces are maturing far faster than we expected. All of this will impact our HR technologies. Just make sure that whatever you buy really fits your needs and that the "intelligence" you implement is intelligent in the areas of need for your organization.
Josh Bersin is principal and founder, Bersin™, Deloitte Consulting LLP. As used in this document, "Deloitte" means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting.
People Analytics
2018年02月11日
People Analytics
6 Best Recruiting Tools Of 2018 [Infographic]In 2018, hiring volume is again predicted to increase with 61% of recruiters expecting to hire more people.
According to SourceCon’s State Of Sourcing Survey, increased hiring volume coupled with stagnant recruiter headcount means the most important trend to learn and understand for recruiters are tools and technology.
Here’s a list of the 6 best recruiting tools you should be using in 2018 summarized in an infographic.
Recruiting tool #1: AI for screening
2017 was the year of AI and automation tools and adoption will only pick up steam in 2018.
One of the best recruiting tools of 2018 will be AI to automate screening because it helps solve a major challenge for recruiters: too much volume.
Jobvite reports the typical high-volume job posting receives more than 250 resumes with 65% of these resumes ignored on average.
Designed to integrate with your existing ATS, automated screening software uses AI to learn what good candidates look like based on your past hiring decisions.
The software learns what your employees’ experience, skills, and other qualifications are and then applies that knowledge to automatically screen, grade, and shortlist new candidates (e.g., from A to D).
The benefits of using AI for screening are the potential to reduce your cost per hire by 70% and reduce time to hire from 34 to 9 days.
Recruiting tool #2: Rediscovering previous candidates
Virtually unknown as a concept in 2017, candidate rediscovery is the practice of mining the existing resumes in your ATS to source prior applicants for a current req.
Software that allows you to conduct this type of rediscovery is poised to become one of the best recruiting tools of 2018 because a typical ATS isn’t set up to be able to easily search and rank previous candidates for current job openings so
Rediscovery is different from keyword or boolean searches because it uses AI to learn the requirements of the role and then scans resumes to find candidates with matching qualifications.
In 2018, candidate rediscovery will gain interest as a tool that allows you to tap into the talent pool that you’ve already spent resources attracting, sourcing, and engaging.
Recruiting tool #3: Recruitment chatbot
Recruitment chatbots were introduced to the market in 2017 and are poised to gain serious attention in 2018.
As a recruiting tool, a chatbot uses natural language processing to understand text like a human would.
The main functions of a recruitment chatbot is to streamline the top of the funnel by providing real-time, on-demand communication to candidates. Its functions include answering FAQs about the job, providing feedback and updates, and scheduling a follow up or interview with a human recruiter.
One of the biggest trends in 2018 will be candidate experience.
A recruitment chatbot holds the potential to massively improve the candidate experience by enabling time-strapped recruiters to provide unlimited and instantaneous, albeit electronic, touch points.
Recruiting tool #4: De-biasing software
Unconscious bias was a huge topic in 2017 and an entire industry of de-biasing recruiting software has sprung up in reaction.
Specifically, recruiting tools that use AI to identify and remove bias from job descriptions, resume screening, and sourcing.
These recruiting tools use AI to fight unconscious bias during the sourcing and screening phases by ignoring candidates’ demographics (e.g., implied race, gender, and age) from their resumes and online profiles.
Related to bias, workplace diversity will continue to be a big focus in recruiting in 2018 and tools that work to diversify the candidate pool will be in hot demand.
Recruiting tool #5: Super-targeting job ads
2017 saw the introduction of targeted job descriptions and this trend will continue in the next year.
New methods of job ads include re-targeting candidates (e.g., advertising your role to people who’ve visited your company website before) and geo-targeting (e.g., advertising your role to people physically nearby).
With a tighter labour market and the spray and pray model of sourcing officially dead, recruiters will be eager for better tools to get their job postings in front of the right eyeballs.
Recruiting tool #6: Recruitment marketing software
2018 will be the year that candidate experience finally gets its due. A big part of that push will involve recruitment marketing.
Recruitment marketing is the application of marketing best practices, such as analytics, multi-channel use, targeted messaging, and tech-enabled automation, to attract, engage, and nurture candidates who haven’t yet applied to a job and converting them into applicants by communicating your employer brand and value.
In 2018, recruitment marketing software will be the best tool to create brand awareness of your company and interest in your open roles, attract candidates who self-select themselves into the application process, and keep candidates informed and engaged throughout the recruitment cycle.
Ji-A Min
Head Data Scientist at Ideal
Ji-A Min is the Head Data Scientist at Ideal. With a Master’s in Industrial-Organizational Psychology, Ji-A promotes best practices in data-based recruitment. She writes about research and trends in talent acquisition, recruitment tech, and people analytics.
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