思想领袖
关于声音AI最重要的东西不是声音
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语音AI最重要的不是语音。 这也是体验的声音可以实现的。
想想你有过的最好的服务经验你感觉理解。 你不必重复，学习组织如何运作，或处理其内部的复杂性。 你解释了你需要什么，有人利用组织的背景，判断和知识帮助你走向成功的结果。
如果企业能够以更一致和更大规模地提供更多的这些经验，怎么办？
而不是简单地用说话或实现人类互动自动化来代替打字。 Voice agents将经验从航海系统转移到表达意向。 顾客或雇员用他们自己的话来描述他们想要完成什么。 agent理解背景，协调幕后的适当知识和系统，采取允许的行动，当需要判断，同情或权威时，带来在真实人身上。
几十年来，人们都适应了技术。 我们学会了在哪里点击、打开哪个菜单以及如何将一个简单的意向转化为一系列命令。 我们开始用日常语言交流，声音走下一步，开始完全扭转关系：技术适应人们自然沟通的方式，而企业的复杂性则退入背景。
为什么声音改变企业的经验
语音很重要，因为它可以消除员工的摩擦，客户和新的聘请都一样。 向服务台打电话的雇员不需要知道哪个系统有问题，或有一个解决办法。 客户不应在频道之间移动时重复同样的信息。 新的雇用不应局限于静态培训材料，因为此时他们可以进行现实的对话并获得反馈。
声音还拓宽了远离键盘的人的进入空间，他们在外地工作，或者只是比打字更舒服地说话。 多语种能力有助于各组织在不把每一次互动变成单独的制作努力的情况下，在地理上进行连贯的沟通。
然而，声音并不是每个屏幕或每个人类对话的替代。 视觉和书面媒介比较密集的信息和审查准确细节仍然更好，当情况模糊、敏感或因此而来时，人的判断仍然至关重要。 机会是使用对话是取得结果的最自然途径的语音，然后在没有对话时设计明确的交割。
语音界面不足
自然出声之相不足。 实际价值来自agent是否能够使经验有意义地更好：去除摩擦，保存上下文，加速解析和帮助某人达到正确的结果。 这需要的不仅仅是一个令人信服的接口。 它需要连接的数据和工作流程、明确的界限、可信赖的交接和对接下来发生的事情的问责。
所以脚本和意图之间有区别。 传统的工作流程脚本会提前一步完成。 然而，围绕结果设计的声音agent得到意图，理解规则和背景，并调整其方法，直到目标实现或需要个人的判断。
它有助于了解买家的实际位置，因为企业在短时间内经历了三个阶段。 一段时间以来，我们处于收养狂热之中——收养主要是为了其本身。 然后，领导人发现自己对这些投资回报负责，他们往往无法说明自己得到的价值。 由此对结果进行了调整。 问题不再仅限于部署是否产生结果，而是实现部署是否有损于任何关键因素。 有一种慷慨的怀疑主义——真正愿意投资，同时期望两个问题都得到回答。
预测也指出了同样的方向。 Gartner期望代理AI在2029年之前自主解决80%的常见客户服务问题。 确切数字是80%并不重要。 这里的重要词是决心。 对话从访问开始，产品是解析。
在非常公开的环境下的客户零
在华盛顿，我们最早的声音AI部署之一发生在一个异乎寻常的环境：我们的收入呼吁。 在连续三个季度中，CEO劳尔·费尔南德斯(Raul Fernandez)写出他准备的剧本，而由ElevenLabs所建造的定制AI语音模型则用他的声音交付。
第一个呼叫显示了核心能力。 第二，他的发言可以西班牙语、法语、德语、葡萄牙语和阿拉伯语进行交流。 到第三部，剧本同时以六种语言出道。 劳尔每次都告诉听众，他已经写出这些文字，一个AI模型正在传送这些文字。
收入呼吁并不是一个容易的场所：它受到仔细审查、管制和与任何听起来稍有错误的东西不相符合。 在那里进行实验有四件事而不是在更安全的地方
- 授权比交付更重要评论是他的；模型只是读取。 各组织可以扩大各语文之间的沟通，而不改变谁写了信息或谁仍然对此负责。
- 披露加强了结果而不是破坏了结果。 最初还不清楚宣布AI的声音是否会使我们失去信誉。 情况正好相反，另一种方案——希望没有人注意到——在道德和实践上都是错误的。
- 多语种的交付提供了实际价值。 否则，股东们将等待翻译，现在以同样的声音，同时听到同样的话。 这是在谁平等获得信息方面的变化，而不是生产效率。
- 信任取决于问责制，而不是抛光。 当人们知道是谁撰写的，是谁支持的，以及他们有什么办法时，就接受人工智能的信息。 这些声音本身都听不到。
这反映了我们在LabX,DXC的AI-native产品孵化器中所使用的一个原则：在要求客户信任新兴技术之前，我们应该愿意在自己的环境中测试。 身为零号客户，人们无法通过抛出演示揭露问题——人们在其中犹豫不决，信任破裂，以及需要何种护栏来负责任地大规模运作。 最大的企业AI挑战通常是人，而不是技术。
从示范到转变的经验
我们与专门从事音频模型和语音agents的AI公司的ElevenLabs的战略伙伴关系，以客户零号的经验为基础，将语音应用到三个实际领域。 在每一种情况下，起点都是确定的结果，而不仅仅是技术部署。
- 在服务台中，语音agents可以帮助雇员自然地描述一个问题，检索相关知识，完成例行步骤，并在需要升级时将全部内容传给专家。 衡量标准不是有多少电话被调转。 在一次互动中
- 在客户体验平台中，自然发声的多语种界面在电话和数字频道之间建立了更一致的互动。 大多数服务组织的质量因谁回答和何时回答而不同。 存在着真正改进的机会——每一个互动都能够从同样的背景中得知，最困难的互动仍然被送到最有能力处理它们的人手中。
- 在培训中，语音将被动内容转化为实践。 员工可以排练客户对话，安全情景或困难的管理讨论，然后接受有针对性的辅导。 人们会比读到更能操练真正的对话
具体行业的应用可能是后果最大的。 保险商可以探索常规声称的导引摄入；制造商可以让外地技术人员手无寸铁地进入程序；公共部门组织可以让信息以多种语言更容易获取。 这些不是放在一个组织上的一般agents。 它们必须围绕每个行业的词汇、规则、数据和风险简介来设计。
缩放大服务的质量，而非脚本互动
当然，风险是，这个尺度会变成相同的。 企业以前曾犯过这种错误：一个决策树被统一应用，满足了一般情况，使其他人都失望了。 真正的规模应该是伟大的服务互动——知情、反应迅速、面向结果——的质量，而互动本身则适应个人的语言、背景和需要。 一致性应意味着可靠的良好，而不是完全相同。
信任是经验的一部分
agent的声音越是人性化，透明度就越重要。 一个人应该知道他们何时与AI互动。 组织需要明确的许可才能创建或使用某人的合成声音，控制谁可以访问声音，对敏感行动的有力认证，agent所作所为的记录以及通往真正人类的明确路径。
这些内容往往被视为在设计经验后就被紧紧扣住的遵守问题，但必须将其作为经验的一部分。 无法知道自己是否在与一个人交谈，也无法知道agent授权做什么
我们应该精确地了解这种信任的来源，因为它是双向的。 模型要由建设者适当校正。 围绕该系统的部署——数据处理、访问控制、登录行动、界定某人接管的地点——是属于谁将该系统投入生产的另一个问题。 也不代表别人两者在语音上都更为重要，因为互动是活的，agent可能是在说话时表现的，没有屏幕显示这个人刚刚代表他们发生的事情。
外部指导在这方面有所帮助。 联邦贸易委员会强调了恶意语音克隆的潜在危害，NISTAI风险管理框架为识别和管理AI风险提供了一个实用的结构。 将同意、披露、安全、测试和持续监测视为核心产品要求。
构建从实验到规模的开放路径
我作为AI创新与战略及LabX 带来总裁，LabX仍然是测试前沿技术与真正的客户问题对照的场所，但其任务现在包括帮助客户导航Anthropic和ElevenLabs等合作伙伴的开放生态系统。
这种开放的做法很重要，因为没有一个单一的模式、平台或接口对每一个企业和每一个使用案例都是正确的。 我们的工作是将正确的技术与领域专门知识、工作流程设计、治理和交付能力结合起来，然后证明它产生了可衡量的结果。 有时正确的界面将是文本。 有时它会是视觉的。 它将日益成为声音——而且常常是所有这三者的深思熟虑的组合。
问题不在于企业在哪里可以增加发言权。 正是在这里，对话能够产生更好的经验和更好的结果，以及本组织是否准备重新设计围绕它的工作。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Thought Leaders
The Most Important Thing About Voice AI Is Not the Voice
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The most important thing about voice AI is not the voice. It’s the experience voice can make possible.
Think about the best service experience you have ever had. You felt understood. You did not have to repeat yourself, learn how the organization worked or navigate its internal complexity. You explained what you needed, and someone used context, judgment and knowledge of the organization to help move you toward a successful outcome.
What if enterprises could provide more of those experiences, more consistently and at greater scale?
That’s the real promise of voice agents, not simply replacing typing with talking or automating every human interaction. Voice agents shift the experience from navigating systems to expressing intent. A customer or employee describes what they want to accomplish in their own words. The agent understands the context, coordinates the appropriate knowledge and systems behind the scenes, takes permitted actions, and brings in a real person when judgment, empathy or authority is required.
For decades, people have adapted themselves to technology. We learned where to click, which menu to open and how to translate a simple intention into a series of commands. Generative AI began to change that by letting us converse in everyday language. Voice takes the next step and begins to reverse the relationship entirely: the technology adapts to how people naturally communicate, while the complexity of the enterprise recedes into the background.
Why Voice Changes the Enterprise Experience
Voice matters because it removes friction for employees, customers and new hires alike. An employee calling a service desk should not need to know which system owns a problem or houses a solution. A customer should not have to repeat the same information as they move between channels. A new hire should not be limited to static training materials when they could practice a realistic conversation and receive feedback in the moment.
Voice also broadens access for people away from a keyboard, working in the field or simply more comfortable speaking than typing. Multilingual capabilities help organizations communicate consistently across geographies without turning every interaction into a separate production effort.
And yet voice is not a replacement for every screen or every human conversation. Visual and written mediums remain better for comparing dense information and reviewing precise details, and human judgment remains essential when a situation is ambiguous, sensitive or consequential. The opportunity is to use voice where conversation is the most natural path to an outcome, then design clear handoffs when it is not.
A Voice Interface Is Not Enough
A natural-sounding voice is not enough. The actual value comes from whether the agent can make the experience meaningfully better: removing friction, preserving context, accelerating resolution and helping someone reach the right outcome. That requires more than a compelling interface. It requires connected data and workflows, clear boundaries, trusted handoffs and accountability for what happens next.
So there’s a distinction between scripting and intent. A traditional workflow scripts every step in advance. A voice agent designed around outcomes, however, receives the intent, understands the rules and context, and adapts its approach until the goal is met or a person’s judgment is needed.
It helps to know where buyers actually are, because enterprise has moved through three phases in a short time. For a while we were in an adoption frenzy — adoption largely for its own sake. Then leaders found themselves accountable for what those investments returned, and they often could not articulate the value they had received. That produced a recalibration around outcomes. The question was no longer only whether a deployment produced an outcome, but whether achieving it compromised anything critical. There is a kind of generous skepticism — a real willingness to invest, paired with an expectation that both questions get answered.
The forecasts point the same way. Gartner expects agentic AI to autonomously resolve 80% of common customer service issues by 2029. Whether the exact number is 80% doesn’t matter. The important word here is resolve. The conversation begins with access, and the product is the resolution.
Customer Zero in a Very Public Setting
At DXC, one of our earliest voice AI deployments took place in an unusually visible environment: our earnings calls. Over three consecutive quarters, CEO Raul Fernandez wrote his prepared script while a custom AI voice model, built with ElevenLabs, delivered it in his voice.
The first call demonstrated the core capability. On the second, his remarks could be shared in Spanish, French, German, Portuguese and Arabic. By the third, the script went out simultaneously in six languages. Each time, Raul told listeners that he had written the words and that an AI model was delivering them.
An earnings call is not an easy venue: it is scrutinized, regulated and unsympathetic to anything that sounds slightly wrong. Four things came out of running the experiment there rather than somewhere safer.
- Authorship matters more than delivery. The remarks are his; the model simply reads them. Organizations can scale communication across languages without changing who wrote the message or who remains accountable for it.
- Disclosure strengthened the result rather than undermining it. It was initially unclear whether announcing the AI voice would cost us credibility. The opposite happened, and the alternative — hoping nobody noticed — would have been wrong on both ethical and practical grounds.
- Multilingual delivery provided real value. Shareholders who would otherwise wait for a translation now hear the same remarks, in the same voice, at the same time. That is a change in who gets equal access to information, not a production efficiency.
- Trust rests on accountability, not polish. People accept an AI-delivered message when they know who authored it, who stands behind it and what recourse they have. None of that is audible in the voice itself.
This reflects a principle we use in LabX, DXC’s AI-native product incubator: before asking customers to trust an emerging technology, we should be willing to test it inside our own environment. Being Customer Zero exposes the issues a polished demo cannot — where people hesitate, where trust breaks down and what guardrails are needed to operate responsibly at scale. The hardest enterprise AI challenges are usually human rather than technical.
From Demonstration to Transformed Experiences
Our strategic partnership with ElevenLabs, the AI company specializing in audio models and voice agents, builds on that Customer Zero experience and applies voice across three practical areas. In each case the starting point is a defined outcome, not simply a technology deployment.
- In service desks, voice agents can help employees describe an issue naturally, retrieve relevant knowledge, complete routine steps and pass full context to a specialist when escalation is needed. The measure is not how many calls were deflected. It’s whether the person who called reached a resolution, in one interaction, without describing the problem three times.
- In customer experience platforms, natural-sounding multilingual interfaces create more consistent interactions across phone and digital channels. Quality in most service organizations varies by who answers and when. There is an opportunity for real improvement — every interaction can be informed by the same context, with the hardest ones still routed to the people best equipped to handle them.
- In training, voice turns passive content into practice. Employees can rehearse a customer conversation, a safety scenario or a difficult management discussion, then receive tailored coaching. People get competent faster practicing the actual conversation than reading about it.
Industry-specific applications may be the most consequential. Insurers can explore guided intake for routine claims; manufacturers can give field technicians hands-free access to procedures; public-sector organizations can make information easier to reach in multiple languages. These are not generic agents placed on top of an organization. They have to be designed around the vocabulary, rules, data and risk profile of each industry.
Scale the Qualities of Great Service, Not a Scripted Interaction
The risk, of course, is that scale becomes sameness. Enterprises have made that mistake before: a decision tree applied uniformly, satisfying the average case and failing everyone else. What should actually scale are the qualities of a great service interaction — informed, responsive, oriented toward an outcome — while the interaction itself adapts to the person’s language, context and need. Consistency should mean reliably good, not identical.
Trust Is Part of the Experience
The more human a voice agent sounds, the more important transparency becomes. A person should know when they are interacting with AI. Organizations need explicit permission to create or use someone’s synthetic voice, controls over who can access it, strong authentication for sensitive actions, records of what the agent did and a clear route to a real human.
These are often treated as a compliance issue bolted on after the experience is designed, but they must be treated as part of the experience. Someone who can’t tell whether they are speaking with a person, can’t tell what the agent is authorized to do, or can’t reach someone when they need to is not having a good experience, however natural the conversation sounds.
We should be precise about where that trust comes from, because it’s a two-way street. A model has to be calibrated properly by the people who built it. The deployment around it — data handling, access control, logged actions, defined points where a person takes over — is a separate problem belonging to whoever puts the system into production. Neither substitutes for the other. Both matter more in voice, because the interaction is live, the agent may be acting while it talks, and there is no screen showing the person what just happened on their behalf.
External guidance helps here. The Federal Trade Commission has highlighted the potential harms of malicious voice cloning, and the NIST AI Risk Management Framework offers a practical structure for identifying and managing AI risk. Treat consent, disclosure, security, testing and ongoing monitoring as core product requirements.
Building an Open Path From Experiment to Scale
My expanded role as President of AI Innovation and Strategy & LabX brings together two sides of the same challenge: incubating AI-native products and developing the commercial side of DXC’s AI partner platform strategy. LabX remains a place to test frontier technologies against real customer problems, but its mandate now includes helping customers navigate an open ecosystem of partners such as Anthropic and ElevenLabs.
That open approach matters because no single model, platform or interface will be right for every enterprise and every use case. Our job is to combine the right technology with domain expertise, workflow design, governance and delivery capability, then validate that it produces a measurable outcome. Sometimes the right interface will be text. Sometimes it will be visual. Increasingly it will be voice — and often a thoughtful combination of all three.
The question is not where an enterprise can add voice. It is where conversation can produce a better experience and a better outcome, and whether the organization is prepared to redesign the work around it.
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