• Operational Efficiency
    什么是Agentic AI?AI Agent如何重塑HR行业? "Agentic AI"(代理人工智能)是您可能听说过的最新流行语,但实际上这个词在人力资源工作中的应用其实已经有一段时间了! 但究竟什么是 Agentic AI?它和传统的AI Agent有何区别?2025年是否真的是AI Agent之年? 特别呈现这篇文章与您分享! 在人工智能(AI)快速发展的今天,我们已经经历了**预测AI(Predictive AI)和生成式AI(Generative AI)**的兴起,而如今,**Agentic AI(自主智能体AI)**正成为AI的下一个进化阶段。对于HR行业而言,这一技术的到来意味着更加智能的HR系统、自动化的人才管理流程,以及更精准的数据驱动决策。 但究竟什么是Agentic AI?它和传统的AI Agent有何区别?2025年是否真的是AI Agent之年?本文将为你详细解析Agentic AI的核心概念,并探讨它如何改变HR行业。 1. 什么是Agentic AI? Agentic AI(自主智能体AI)是一种具备自主行动能力的人工智能技术,它不仅能像生成式AI一样回答问题、生成内容,还能自主感知环境、推理分析、执行任务,并从反馈中不断优化自身能力。 相比于传统的AI系统,Agentic AI最大的不同在于: 自主性(Autonomy):无需人工干预,AI代理可以独立完成任务,例如审核候选人简历、优化招聘流程等。 适应性(Adaptability):AI能够根据反馈不断优化决策,例如HR系统可以自动调整绩效评估标准,以适应不同部门的需求。 目标导向(Goal Orientation):Agentic AI可以自主制定目标,并推理如何达成这些目标,例如自动匹配候选人与职位,提高招聘效率。 2. AI Agent vs. Agentic AI:有什么区别? 在HR行业中,我们常见的AI Agent(AI代理),例如智能客服或自动化面试助手,已经在许多企业得到应用。但与Agentic AI相比,传统AI Agent仍然具有局限性。 举个例子: AI Agent:只能回答员工关于公司福利的常见问题,比如“今年的年假政策是什么?” Agentic AI:不仅能回答问题,还能主动分析员工的休假情况,自动推荐合适的休假时间,并结合公司政策优化排班,确保业务顺利运行。 3. Agentic AI如何改变HR行业? 随着Agentic AI的发展,HR的许多日常工作将发生巨变。以下是几个关键应用场景: (1)智能招聘与人才管理 Agentic AI可以帮助HR从简历筛选、面试安排到人才匹配实现全流程自动化。 ? 自动筛选简历:AI代理可通过自然语言处理(NLP)分析海量简历,并根据职位要求筛选最匹配的候选人。? 优化招聘流程:Agentic AI能够自主调整招聘策略,例如根据市场趋势调整岗位描述,优化招聘渠道,提高人才获取效率。? 智能面试安排:AI代理可以结合面试官和候选人的日程,自动安排面试,并实时调整时间,减少HR的重复沟通工作。 (2)绩效评估与员工发展 HR部门可以利用Agentic AI来优化绩效考核体系,并制定个性化的员工成长路径。 ? 智能绩效评估:AI代理可实时分析员工的工作数据,提供个性化绩效反馈,帮助管理者更公平地评估员工表现。? 个性化职业发展:Agentic AI可以分析员工的职业路径,自动推荐合适的培训课程或晋升机会,帮助企业留住优秀人才。 (3)员工体验与组织管理 AI可以提高员工满意度,并优化组织架构,提高整体效率。 ? 智能员工助手:AI代理可以主动提醒员工提交报销单、更新考勤信息,甚至预测员工的离职风险,并提前采取措施留住人才。? 企业文化管理:AI可以分析员工情绪,帮助HR团队制定更合适的企业文化建设方案。 4. 为什么2025年是“AI Agent之年”? 2025年,Agentic AI的应用将迎来爆发式增长,这主要得益于以下三大趋势: (1)AI技术的成熟与算力提升 随着大模型(如ChatGPT、NVIDIA NeMo)的不断升级,AI的推理能力越来越强,使得Agentic AI在HR场景下更加实用。 (2)企业数字化转型加速 全球范围内,企业正在加快HR数字化转型。Agentic AI能够帮助HR团队自动化重复性工作,让HR更专注于战略性任务,因此将被广泛应用。 (3)人才市场变化与HR挑战 后疫情时代,企业面临招聘难、员工流动性增加等挑战。Agentic AI可以通过智能化的人才管理系统,提高招聘效率、优化员工体验,并降低HR工作负担。 ? 预测:到2025年,超过50%的企业将引入Agentic AI,以优化HR管理流程。 5. HR如何准备迎接Agentic AI时代? 2025年将是AI Agent之年,HR行业必须抓住这一变革机遇。以下是HR团队可以采取的三大行动: ✅ 学习Agentic AI相关知识,关注AI在HR领域的应用趋势,如AI招聘、智能绩效管理等。✅ 尝试小规模部署AI代理,比如在员工服务、招聘管理等领域测试AI解决方案。✅ 与AI厂商合作,寻找适合企业的AI解决方案,如NVIDIA、微软、谷歌等提供的Agentic AI技术支持。 HR的未来,不只是管理人,更是管理智能体!Agentic AI将成为HR行业的重要助手,助力企业迈向智能化管理新时代! ? RAIHR倡导:实施负责任的AI(Responsible AI in HR, RAIHR) 随着Agentic AI在HR行业的广泛应用,我们必须关注AI的伦理、安全和公平性问题。**RAIHR(Responsible AI in HR)**倡导企业在引入Agentic AI时,遵循以下三大原则,确保AI技术的透明性、公平性和责任性: ✅ 透明性(Transparency):确保AI决策过程可解释,HR能够理解AI的筛选标准、考核指标,避免“黑箱”决策。✅ 公平性(Fairness):AI招聘和绩效评估应避免算法偏见,确保候选人和员工得到公平、公正的对待。✅ 责任性(Accountability):AI在HR领域的应用应遵循合规要求,确保数据安全,并提供人工复核机制,避免AI错误影响员工职业发展。 Agentic AI的未来,不仅是效率与智能的提升,更应是“负责任的AI”!HR行业需要共同努力,确保AI技术真正惠及企业与员工,让AI成为推动组织可持续发展的正向力量! ?? 总结:Agentic AI将彻底改变HR工作方式 ? AI Agent vs. Agentic AI:传统AI Agent只是执行预设任务,而Agentic AI能自主学习、推理和优化。? HR应用场景:Agentic AI将在招聘、绩效评估、员工体验等方面发挥巨大作用。? 2025年是AI Agent之年:技术突破、企业数字化转型、HR挑战推动Agentic AI的全面应用。 ? 实施负责任的AI(Responsible AI in HR, RAIHR)我们必须关注AI的伦理、安全和公平性问题 未来,HR不再是“人力资源管理者”,而是“AI智能管理者”!准备好迎接这场AI革命了吗? ?  
    Operational Efficiency
    2025年03月16日
  • Operational Efficiency
    滴滴出行选用NICE,以提供基于实时 AI 的个性化服务 NICE has partnered with DiDi Global to enhance customer and employee experiences through its cloud-based Workforce Management (WFM) and Employee Engagement Manager (EEM) solutions. This collaboration aims to streamline DiDi's global contact center operations, improving operational efficiency and customer satisfaction with AI-driven forecasting and scheduling. The implementation of NICE's solutions facilitates real-time management and self-scheduling for agents, boosting employee engagement and operational efficiency. DiDi's choice of NICE highlights the importance of advanced, flexible technology in supporting the dynamic needs of modern, app-based transportation services. 领先的移动出行平台通过利用 NICE 的客户体验 AI 技术,使其员工能够提供轻松且高效的客户服务体验 新泽西州霍博肯-NICE (纳斯达克: NICE) 今日宣布,滴滴出行已经选用了 NICE 劳动力管理 (WFM) 和员工参与管理 (EEM) 作为其云端创新技术的一部分。滴滴现在可以全面预测、规划和管理其全球客户联系中心的运作;同时提升运营效率和员工的参与度,并确保客服代表能够在首次通话中解决问题。Betta作为全球最大的 WFM 客户群之一的支持者,在实施过程中与 NICE 价值实现服务携手合作,负责执行集成,并在多国提供咨询、培训和支持服务。 滴滴出行寻求一种能够满足其核心业务、功能及技术需求,并能够随公司成长而扩展的劳动力管理解决方案。NICE WFM 结合了 AI 技术与灵活性,能够满足跨多个大洲、具有特定区域特色的运营需求,这不仅成本效益高,而且精确度高,确保维持最佳的服务水平。通过精准预测,确保在合适的时间有合适技能的代理人,从而大幅提升客户满意度。 通过引入 NICE EEM,可以实时解决人员配置需求,使得客服代理能够自我调节工作时间表,从而增强员工参与度和工作满意度。此外,利用智能日内自动调整功能,能够主动地进行调整,预防问题的发生。 滴滴出行国际客户体验执行总监 Caio Poli 表示:“基于多个考量因素,NICE 显然是我们的首选。我们寻找的是一个顶尖的云端劳动力管理解决方案,能够使我们的全球运营在保证运营效率和员工参与度的同时,提供卓越的客户体验。NICE 的智能日内自动化功能给我们留下了深刻印象,我们的选择是基于 AI 驱动的策略以及云技术的速度和灵活性。” NICE 美洲总裁 Yaron Hertz 表示:“随着滴滴持续全球扩张,NICE 很高兴有机会为这家数字时代最具创新和活力的应用型运输公司之一提供服务。我们相信,通过采用 NICE 的 AI 驱动预测和机器学习来进行最适合的调度安排,对于联系中心和员工而言,这将有助于推动滴滴的未来发展。” 关于滴滴出行公司 滴滴出行公司是一个领先的移动技术平台,它在亚太地区、拉丁美洲及其他全球市场提供一系列基于应用的服务,包括网约车、叫车服务、代驾以及其他共享出行方式,还涵盖某些能源和车辆服务、食品配送和城市内部货运服务。滴滴为车主、司机和配送伙伴提供灵活的工作和收入机会,致力于与政策制定者、出租车行业、汽车行业及社区合作,利用 AI 技术和本地化智能交通创新解决全球的交通、环境和就业挑战。滴滴力图为未来城市构建一个安全、包容和可持续的交通与本地服务生态系统,以创造更好的生活体验和更大的社会价值。更多信息,请访问:www.didiglobal.com 关于 NICE 借助 NICE (纳斯达克: NICE),全球各地不同规模的组织现在可以更容易地创造卓越的客户体验,同时满足关键的业务指标。作为世界领先的云原生客户体验平台 CXone 的提供者,NICE 是 AI 驱动自助服务和代理辅助客户体验软件领域的全球领导者,服务范围超出了传统的联系中心。超过 25,000 个组织在超过 150 个国家,包括 85 家以上的财富 100 强公司,都选择与 NICE 合作,以改造并提升每一次客户互动。www.nice.com 商标说明:NICE 和 NICE 标志是 NICE Ltd. 的商标或注册商标。所有其他标志属于它们各自的所有者。NICE 商标的完整列表,请访问:www.nice.com/nice-trademarks。
    Operational Efficiency
    2024年02月27日
  • Operational Efficiency
    Josh Bersin人工智能实施越来越像传统IT项目 Josh Bersin的文章《人工智能实施越来越像传统IT项目》提出了五个主要发现: 数据管理:强调数据质量、治理和架构在AI项目中的重要性,类似于IT项目。 安全和访问管理:突出AI实施中强大的安全措施和访问控制的重要性。 工程和监控:讨论了持续工程支持和监控的需求,类似于IT基础设施管理。 供应商管理:指出了AI项目中彻底的供应商评估和选择的重要性。 变更管理和培训:强调了有效变更管理和培训的必要性,这对AI和IT项目都至关重要。 原文如下,我们一起来看看: As we learn more and more about corporate implementations of AI, I’m struck by how they feel more like traditional IT projects every day. Yes, Generative AI systems have many special characteristics: they’re intelligent, we need to train them, and they have radical and transformational impact on users. And the back-end processing is expensive. But despite the talk about advanced models and life-like behavior, these projects have traditional aspects. I’ve talked with more than a dozen large companies about their various AI strategies and I want to encourage buyers to think about the basics. Finding 1: Corporate AI projects are all about the data. Unlike the implementation of a new ERP system, payroll system, recruiting, or learning platform, an AI platform is completely data dependent. Regardless of the product you’re buying (an intelligent agent like Galileo™, an intelligent recruiting system like Eightfold, or an AI-enabling platform to provide sales productivity), success depends on your data strategy. If your enterprise data is a mess, the AI won’t suddenly make sense of it. This week I read a story about Microsoft’s Copilot promoting election lies and conspiracy theories. While I can’t tell how widespread this may be, it simply points out that “you own the data quality, training, and data security” of your AI systems. Walmart’s My Assistant AI for employees already proved itself to be 2-3x more accurate at handling employee inquiries about benefits, for example. But in order to do this the company took advantage of an amazing IT architecture that brings all employee information into a single profile, a mobile experience with years of development, and a strong architecture for global security. One of our clients, a large defense contractor, is exploring the use of AI to revolutionize its massive knowledge management environment. While we know that Gen AI can add tremendous value here, the big question is “what data should we load” and how do we segment the data so the right people access the right information? They’re now working on that project. During our design of Galileo we spent almost a year combing through the information we’ve amassed for 25 years to build a corpus that delivers meaningful answers. Luckily we had been focused on data management from the beginning, but if we didn’t have a solid data architecture (with consistent metadata and information types), the project would have been difficult. So core to these projects is a data management team who understands data sources, metadata, and data integration tools. And once the new AI system is working, we have to train it, update it, and remove bias and errors on a regular basis. Finding 2: Corporate AI projects need heavy focus on security and access management. Let’s suppose you find a tool, platform, or application that delivers a groundbreaking solution to your employees. It could be a sales automation system, an AI-powered recruiting system, or an AI application to help call center agents handle problems. Who gets access to what? How do you “layer” the corpus to make sure the right people see what they need? This kind of exercise is the same thing we did at IBM in the 1980s, when we implemented this complex but critically important system called RACF. I hate to promote my age, but RACF designers thought through these issues of data security and access management many years ago. AI systems need a similar set of tools, and since the LLM has a tendency to “consolidate and aggregate” everything into the model, we may need multiple models for different users. In the case of HR, if build a talent intelligence database using Eightfold, Seekout, or Gloat which includes job titles, skills, levels, and details about credentials and job history, and then we decide to add “salary” …  oops.. well all of a sudden we have a data privacy problem. I just finished an in-depth discussion with SAP-SuccessFactors going through the AI architecture, and what you see is a set of “mini AI apps” developed to operate in Joule (SAP’s copilot) for various use cases. SAP has spent years building workflows, access patterns, and various levels of user security. They designed the system to handle confidential data securely. Remember also that tools like ChatGPT, which access the internet, can possibly import or leak data in a harmful way. And users may accidentally use the Gen AI tools to create unacceptable content, dangerous communications, and invoke other “jailbreak” behaviors. In your talent intelligence strategy, how will you manage payroll data and other private information? If the LLM uses this data for analysis we have to make sure that only appropriate users can see it. Finding 3: Corporate AI projects need focus on “prompt engineering” and system monitoring. In a typical IT project we spend a lot of time on the user experience. We design portals, screens, mobile apps, and experiences with the help of UI designers, artists, and craftsmen. But in Gen AI systems we want the user to “tell us what they’re looking for.” How do we train or support the user in prompting the system well? If you’ve ever tried to use a support chatbot from a company like Paypal you know how difficult this can be. I spent weeks trying to get Paypal’s bot to tell me how to shut down my account, but it never came close to giving me the right answer. (Eventually I figured it out, even though I still get invoices from a contractor who has since deceased!) We have to think about these issues. In our case, we’ve built a “prompt library” and series of workflows to help HR professionals get the most out of Galileo to make the system easy to use. And vendors like Paradox, Visier (Vee), and SAP are building sophisticated workflows that let users ask a simple question (“what candidates are at stage 3 of the pipeline”) and get a well formatted answer. If you ask a recruiting bot something like “who are the top candidates for this position” and plug it into the ATS, will it give you a good answer? I’m not sure, to be honest – so the vendors (or you) have to train it and build workflows to predict what users will ask. This means we’ll be monitoring these systems, looking at interactions that don’t work, and constantly tuning them to get better. A few years ago I interviewed the VP of Digital Transformation at DBS (Digital Bank of Singapore), one of the most sophisticated digital banks in the world. He told me they built an entire team to watch every click on the website so they could constantly move buttons, simplify interfaces, and make information easier to find. We’re going to need to do the same thing with AI, since we can’t really predict what questions people will ask. Finding 4: Vendors will need to be vetted. The next “traditional IT” topic is going to be the vetting of vendors. If I were a large bank or insurance company and I was looking at advanced AI systems, I would scrutinize the vendor’s reputation and experience in detail. Just because a firm like OpenAI has built a great LLM doesn’t mean that they, as a vendor, are capable of meeting your needs. Does the vendor have the resources, expertise, and enterprise feature set you require? I recently talked with a large enterprise in the middle east who has major facilities in Saudi Arabia, Dubai, and other countries in the region. They do not and will not let user information, queries, or generated data leave their jurisdiction. Does the vendor you select have the ability to handle this requirement? Small AI vendors will struggle with these issues, leading IT to do risk assessment in a new way. There are also consultants popping up who specialize in “bias detection” or testing of AI systems. Large companies can do this themselves, but I expect that over time there will be consulting firms who help you evaluate the accuracy and quality of these systems. If the system is trained on your data, how well have you tested it? In many cases the vendor-provided AI uses data from the outside world: what data is it using and how safe is it for your application? Finding 5: Change management, training, and organization design are critical. Finally, as with all technology projects, we have to think about change management and communication. What is this system designed to do? How will it impact your job? What should you do if the answers are not clear or correct? All these issues are important. There’s a need for user training. Our experience shows that users adopt these systems quickly, but they may not understand how to ask a question or how to interpret an answer. You may need to create prompt libraries (like Galileo), or interactive conversation journeys. And then offer support so users can resolve answers which are wrong, unclear, or inconsistent. And most importantly of all, there’s the issue of roles and org design. Suppose we offer an intelligent system to let sales people quickly find answers to product questions, pricing, and customer history. What is the new role of sales ops? Do we have staff to update and maintain the quality of the data? Should we reorganize our sales team as a result? We’ve already discovered that Galileo really breaks down barriers within HR, for example, showing business partners or HR leaders how to handle issues that may be in another person’s domain. These are wonderful outcomes which should encourage leaders to rethink how the roles are defined. In our company, as we use AI for our research, I see our research team operating at a higher level. People are sharing information, analyzing cross-domain information more quickly, and taking advantage of interviews and external data at high speed. They’re writing articles more quickly and can now translate material into multiple languages. Our member support and advisory team, who often rely on analysts for expertise, are quickly becoming consultants. And as we release Galileo to clients, the level of questions and inquiries will become more sophisticated. This process will happen in every sales organization, customer service organization, engineering team, finance, and HR team. Imagine the “new questions” people will ask. Bottom Line: Corporate AI Systems Become IT Projects At the end of the day the AI technology revolution will require lots of traditional IT practices. While AI applications are groundbreaking powerful, the implementation issues are more traditional than you think. I will never forget the failed implementation of Siebel during my days at Sybase. The company was enamored with the platform, bought, and forced us to use it. Yet the company never told us why they bought it, explained how to use it, or built workflows and job roles to embed it into the company. In only a year Sybase dumped the system after the sales organization simply rejected it. Nobody wants an outcome like that with something as important as AI. As you learn and become more enamored with the power of AI, I encourage you to think about the other tech projects you’ve worked on. It’s time to move beyond the hype and excitement and think about real-world success.
    Operational Efficiency
    2023年12月17日