人工智慧代理將典型聊天機器人的工作方式更進一步。由於這些聊天機器人經過了數十億次 CX 互動的預先訓練,因此它們可以自主解決複雜的詢問並邊學習邊提供更好的支援。 AI 代理還可以預測 客戶需求,使他們能夠提供個人化建議或主動向使用者發送有關問題的訊息。另外,它們整合到後端系統中以無縫交互 客户数据库软件 和 API 来提供超个性化的支持。
AI 以多種方式為聊天機器人提供支持,但 Zendesk AI 等解決方案正在迅速提升客戶體驗機器人處理交互、響應複雜查詢、隨著時間的推移調整行為以及提供更準確和相關信息的方式。借助專為 CX 打造的 AI 而不是通用人工智慧,Zendesk AI Agent 可以解決互動中的細微差別,這對於提供卓越的客戶服務至關重要。
此外,在投資 時,您不再需要在成本和品質之間做出選擇。最好的人工智慧聊天機器人。實施 Zendesk AI 和我們的 AI 代理不需要技術專業知識,這使得我們的解決方案經濟高效且易於使用。根據我們的人工智慧驅動的客戶體驗趨勢報告,當我們努力實現大多數客戶互動將實現自動化(並且 100% 的互動將以某種形式利用人工智慧)時,機器人的能力必須提高。此外,隨著客戶互動的增加,投資於能夠滿足期望並提高團隊效率的人工智慧和聊天機器人對於未來的成功至關重要。
聊天機器人的挑戰
每項新技術都需要考慮挑戰。下面,我們列出了與聊天機器人相關的一些最常見的挑戰,以及使用 Zendesk AI 來緩解這些挑戰的方法。
實作時間表: 部署機器人可能需要時間和資源。借助 Zendesk,您可以將 AI 代理程式的部署時間從幾週縮短到幾天,無需任何技術技能。
培訓: 有偏見、過時且耗時的培訓可能會為聊天機器人的使用帶來問題和困難。然而,Zendesk AI 代理經過數十億客戶互動和真實對話資料的預先訓練,可以自動檢測 客戶意圖 並像人類特工一樣做出反應。
多語言對話流程: You need to reach customers where they are in the language that’s most comfortable for them. With a solution that interacts in more than 100 languages out of the box, you can easily serve customers in multiple languages.
Contextualized responses: As basic and rule-based bots are unable to respond outside of scripted conversations, their responses can miss the point of a customer’s inquiry. With Zendesk AI Agents, you can automatically deliver contextualized responses based on interaction history and customer feedback.
Despite their limits, chatbots are here to stay, so choose the next generation of AI-powered chatbots to provide high-quality service every time.
How have chatbots evolved?
In the 1960s, a computer scientist at MIT was credited for creating Eliza, the first chatbot. Since the creation of this simple bot, chatbots have significantly evolved—and continue to create seamless conversational experiences.
Discover the evolution of chatbots below:
1966 to 2009: The first generation of chatbots—including Eliza and A.L.I.C.E.—were created and later improved. These basic chatbots used recognition capabilities to produce scripted responses for specific keywords. These bots built the foundation for modern chatbots.
2010 to 2020: The second generation of conversational chatbots was born. These bots use advanced NLP and ML processing to understand human language and process voice commands.
2021 to 2023: Generative AI bots like ChatGPT and Gemini are trained on massive data sets. By using transformers and large language models, these bots generate brand-new, contextually relevant outputs to all types of inputs.
Present: Chatbots across the board have become far more sophisticated and specialized in their functionality. Today’s bots leverage advanced AI capabilities to deliver more nuanced, personalized interactions. This shift has allowed them to become more targeted in their use cases, designed to handle specific tasks with greater precision and efficiency. For example, in customer experience, the next generation of chatbots—AI agents—can autonomously solve issues of any complexity. AI agents are trained on billions of interactions, enabling them to provide personalized, accurate responses. These bots are purpose-built for CX and can summarize calls, draft emails, converse with customers, and more.
Modern chatbots are designed to connect with customers without the need for human interference. With the increase in mobile device use and unique messaging channels, utilizing customer service chatbot software has become more popular.
Using advanced AI technology, chatbots have evolved from answering a limited number of common questions to understanding customer sentiment and answering complex queries in a brand’s tone of voice.
Chatbot best practices
If you’re ready to improve your digital customer service experience by investing in a chatbot, consider these best practices:
Disclose when you’re using AI: Inform customers when they’re conversing with a chatbot and when you use AI to draw from your knowledge base to improve AI transparency, set expectations, and promote acceptance and customer trust.
Choose the right software for you. Invest in a custom-made solution that allows you to make your AI agent an extension of your brand with a specific chatbot persona rather than a clunky tool.
Start quickly by connecting to your knowledge base. Provide instant and accurate responses to customers.
Integrate with your backend systems to drive automation. Seamlessly connect with any business system to collect, organize, and analyze customer data to deliver accurate and contextual responses.
Use a hybrid approach to prioritize personalization. Identify topics that require more guidance and build customizable and controllable conversation flows that deliver step-by-step resolutions.
Follow quality assurance principles to catch blind spots. Fine-tune and optimize support processes by automating QA to track areas for improvement and regulate compliance.
Vice President, Product Marketing, AI and Automation
Candace Marshall is a seasoned product marketing leader with a passion for solving complex problems and driving innovation in fast-paced environments. Her career began in operations and research, but her love for understanding customers and translating insights into impactful strategies led her to product marketing. Currently, Candace leads product marketing for Zendesk AI including AI agents and Copilot, driving growth across AI-powered solutions and the core service offerings. Her team delivers end-to-end product marketing strategies, from market validation and messaging to go-to-market execution and customer adoption. Before joining Zendesk, Candace spent nearly a decade at LinkedIn, where she built and led the product marketing team for the rapidly scaling Marketing Solutions division, overseeing key advertising products in the multi-billion-dollar business.
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