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
Operational Efficiency
Operational Efficiency
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