AI Agent 时代，编排成为客户体验的新挑战
英文标题：Orchestration is the new challenge for CX in the age of AI agents
Tata Communications 表示，企业部署 AI Agent、语音 AI 和自动化能力的速度，已经超过了底层 CX 架构的演进速度，许多项目只是将对话式 AI 叠加到旧有系统之上。该公司认为，企业当前的核心挑战已从单点自动化转向编排：让 AI、人工坐席、数据、工作流和各类渠道基于共享上下文协同工作。实现这一目标需要统一的企业本体、上下文图谱和能够低延迟同步数据的网络基础设施。对开发者和企业而言，这意味着 CX 建设重点将从接入更多模型与工具，转向构建云优先、API 驱动、具备实时上下文能力的统一架构。
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由 Tata Communications 呈现
企业正在消息、语音和数字渠道中部署 AI Agent、语音 AI 与自动化，其速度已经超过了支撑这些能力的架构演进速度。Tata Communications Customer Interaction Suite 全球负责人 Gaurav Anand 表示，其中大多数部署都涉及将对话式 AI 附加到原本并非为此设计的遗留系统上。
“在急于部署 AI 的过程中，企业在很大程度上只是将对话式 AI 直接加装到遗留系统中，”Anand 表示。“因此，尽管许多企业已经采用数字工具，但真正拥有整合化、可规模化且能够实现无缝编排的平台的企业极少。”
这种缺口为人工坐席带来了沉重的认知负担：他们必须在割裂的工具之间拼凑上下文，才能理解 AI 系统此前已经向客户传达了什么。挑战不仅仅是数据访问问题，更在于缺少一种共享的企业上下文，无法将客户身份、交互、交易、政策、旅程及运营系统连接成统一认知。传统 CX 架构是为线性、由人工驱动的路由而构建的，并非用于管理自主 AI 系统、数据湖和人工员工之间的实时数据流。
“如今的运营复杂性已经不再是增加更多智能的问题，”他补充道。“问题在于如何协调企业现有的智能能力，让企业客户感受不到这些内部孤岛带来的摩擦。这需要一个共享上下文层，使 AI 系统、应用程序和人员能够基于对客户和业务的同一理解开展工作。”
为什么编排正在取代自动化，成为 CX 的首要优先事项
随着协调问题日益突出，Anand 表示，企业内部的战略重点正在从自动化转向编排。
“自动化解决的是单个任务，而编排将它们连接为端到端的结果，”Anand 表示。“下一阶段的演进是具备上下文感知能力的编排，即 AI Agent、应用程序和人工员工不再依据彼此孤立的系统记录工作，而是基于对客户、流程和业务意图的共享理解进行协作。”
随着组织积累越来越多的机器人、Agent 和 AI 工具，管理它们的复杂性呈指数级增长。Anand 表示，如今的竞争优势较少取决于是否部署了自动化，而更多取决于系统如何智能地交接工作、协作和升级处理。
将 AI 叠加到遗留系统上的陷阱
仅仅在既有系统前端部署一个语音 AI Agent 的公司，正在重复同样的老错误。它们非但没有改善体验，反而重新造出了 AI 本应取代的确定性电话菜单。AI 的真正价值在于其提供的规模、速度和编排能力。
Anand 指出，行业内正出现一波整合潮：成熟的联络中心供应商正在收购 AI 原生公司，以弥补能力缺口并强化客户体验产品。更广泛的行业转变表明，企业越来越认识到，它们需要的不只是渠道和自动化，还需要一个能够跨业务协调 AI、人员、数据和工作流的智能层。
跨行业的目标，是让 AI 成为连接客户、员工和企业系统的纽带。为了实现这一点，组织越来越需要一种通用的企业本体：一套共享的业务词汇，用于在原本相互割裂的平台之间对齐客户数据、产品、政策、SOP、交易和工作流。
Tata Communications 的解决方案是 Interaction Fabric，这是一个编排层，可统一联络中心、消息、协作、AI 和客户数据，并实时协调 AI Agent、渠道和企业系统。其编排能力建立在上下文驱动的架构之上，该架构持续连接身份、对话、交易和运营数据，使交互在不同渠道和触点间保持连续性。
这意味着 AI 和坐席能够在语音、WhatsApp、聊天、电子邮件和 CRM 工作流之间切换，而不会丢失客户上下文。身份、意图和 AI 驱动的洞察能够持续跨渠道流动，而不是被困在彼此孤立的应用程序中。
编排的下一阶段不只是跨系统协调任务，更是通过对企业的共享理解来协调任务。构建于企业本体之上的上下文图谱，通过连接客户、交互、产品、政策、决策和结果，跨越组织孤岛建立这种共同理解。这使 AI Agent 和人工员工能够基于同一上下文来源开展工作，从而推动更准确的决策、更顺畅的交接以及更一致的客户体验。
但是，跨渠道同步客户意图、对话历史、企业数据和 AI 决策，只有在没有延迟的情况下才能发挥作用。并非针对现代数据频率设计的遗留网络会产生 Anand 所称的“数据重力”，当用户切换渠道时，这会带来延迟和不一致的客户旅程。
“底层网络需要被设计得与其上运行的 AI 系统一样敏捷，”他解释道。“交互保持同步，技术本身变得不可见，最终只留下轻松自然的体验。”
让 AI 成为人工坐席更好的合作伙伴
人工坐席与 AI 系统之间实现有效的共享可见性，应从坐席体验出发，而非从某一项单独技术出发。最有效的实施方式，是让 AI 和人工坐席都能基于对客户的同一上下文理解开展工作，确保在一次交互中收集的信息，无论渠道或系统如何变化，都可以为下一次交互提供参考。自动通话摘要、实时情绪分析和 AI 驱动的辅助功能，可直接在坐席工作流中提供即时、可操作的洞察和建议的下一步行动。
这使 AI 能够处理密码重置、配送追踪和账户更新等常规、高频任务，而人工坐席则专注于需要判断力和同理心的交互。
“如果客户正面临诸如欺诈交易这样的突发危机，AI 可以立即冻结卡片，但它无法在客户恐慌时提供所需的情感安慰和细致沟通，”Anand 表示。“这一困境的答案是智能编排，而不是在不同系统之间二选一。”
在实际场景中，AI 处理即时的技术性交易，而实时情绪分析识别客户的焦虑，并将通话路由给人工专家。目标是将 AI 与人工坐席协同编排，确保效率的提升不会以品牌信任和客户忠诚度为代价。
构建统一的 CX 架构
Anand 表示，从碎片化试验走向协调化编排，需要技术和组织层面的双重变革，第一步是将数据和零散的点解决方案整合到一个统一的、云优先的平台上。
“IT 团队和 CX 团队需要更紧密地协作，”他解释道，并将这种协同描述为第二项必要转变，这次发生在组织层面。
在架构层面，Anand 表示，通信 API 需要嵌入企业核心，以便每个职能部门都能够基于同一客户上下文运行，而不是各自维护孤立的数据。越来越多情况下，这意味着不能止步于集成，而应转向上下文架构：由共享本体和上下文图谱在 CX、运营、销售、服务和 AI 系统之间提供共同理解。他表示，更深层的组织变革是一种思维转变，即从被动支持转向主动、预测性和个性化互动；他将其称为“三个 P”。
AI Agent 将如何塑造 CX 的未来
未来数年，客户互动将由实时智能、不断提升的自主性、跨触点的无缝编排，以及持续存在的企业上下文所定义；无论客户、员工和 AI Agent 在何处发生交互，这种上下文都会随之而行。企业将越来越多地实时塑造对话，而不是在事后分析交互。
“CX 的未来将由简化定义，即围绕清晰的客户结果来对齐数据、基础设施和运营模式，而不是增加更多模型和工具，”Anand 表示。“AI 驱动 Agent 及 Agent 与 Agent 之间交互的兴起，是一项决定性趋势。AI 系统将不再仅仅辅助人类，而是独立管理和解决交互，形成一层在很大程度上不可见、却能提升速度和效率的互动层。”
人工坐席将越来越多地与 AI 协作，在实时对话智能和下一最佳行动建议的支持下，提供 Anand 所称的 Total Experience：一种统一模型，将客户体验、员工体验和 AI 驱动体验整合在一起。Tata Communications 正通过其 Voice AI、AI Workers 和 Total Experience Hub 解决方案构建这一未来。
“归根结底，客户互动将从被动响应演变为预测性互动，并且日益具备生成式能力，”Anand 表示。“企业将不再只是响应需求，而是主动实时塑造和改善客户旅程。”
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English / 原文
Presented by Tata Communications
Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications.
"In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration."
That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers.
"Today's operational complexity is no longer about adding more intelligence," he adds. "It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business."
Why orchestration is replacing automation as the top CX priority
As that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration.
"Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes," Anand says. "The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records."
As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate.
The trap of bolting AI onto legacy systems
Companies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides.
Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business.
The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms.
Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints.
That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications.
The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences.
But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels.
"The underlying network needs to be engineered to be as agile as the AI systems running on top of it," he explains. "Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless."
Making AI a better partner for human agents
Effective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow.
That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy.
"If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic," Anand says. "The answer to the dilemma is intelligent orchestration, rather than a choice between systems."
In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer's distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty.
Building a unified CX architecture
Moving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform.
"IT and CX teams need to work more collaboratively," he explains, describing that alignment as the second necessary shift, this time at the organizational level.
At the architecture level, Anand says communication APIs need to be embedded into the enterprise's core so every function operates from the same customer context instead of maintaining its own siloed data. Increasingly, this means moving beyond integration alone toward a contextual architecture where a shared ontology and context graph provide a common understanding across CX, operations, sales, service, and AI systems. The deeper organizational change, he says, is a mindset shift from reactive support toward proactive, predictive, and personalized engagement, which he calls the three Ps.
How AI agents will shape the future of CX
Customer engagement over the next several years will be defined by real-time intelligence, increasing autonomy, and seamless orchestration across touchpoints, and persistent enterprise context that follows customers, employees, and AI agents wherever interactions occur. Rather than analyzing interactions after the fact, enterprises will increasingly shape conversations in real time.
"The future of CX will be defined by simplification, aligning data, infrastructure, and operating models around clear customer outcomes rather than adding more models and tools," Anand says. "The rise of AI-powered agents and agent-to-agent interactions is a defining trend, with AI systems moving beyond assisting humans to independently managing and resolving interactions, creating a largely invisible layer of engagement that improves speed and efficiency."
Human agents will increasingly work alongside AI, supported by real-time conversational intelligence and next-best-action recommendations to deliver what Anand calls Total Experience: a unified model that brings together customer, employee, and AI-driven experiences. Tata Communications is building toward that future through its Voice AI, AI Workers, and Total Experience Hub solutions.
"Ultimately, customer engagement will evolve from being reactive to predictive and increasingly generative," Anand says. "Enterprises won't just be responding to needs, but actively shaping and improving customer journeys in real time."
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