Automating Routine Customer Inquiries With AI

What does "𝘼𝙜𝙚𝙣𝙩𝙞𝙘 𝘼𝙄" really look like in the enterprise today? 🤯 Spoiler alert: It’s not synthetic AI employees taking over entire departments (yet). Instead, it’s smart, focused workflows designed to handle specific tasks efficiently and accurately. Here’s a real-world example from one of our retail clients: they’ve automated the process of helping customers who’ve lost their return labels. 𝗧𝗵𝗲 𝗼𝗹𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀: 1️⃣ A customer emails support saying they can’t find their return label. 2️⃣ A customer service agent reads the email in Zendesk, identifies the issue, and checks the CRM for details. 3️⃣ Most of the time, the problem isn’t the label—it’s something simple like a typo in the zip code or missing phone number. 4️⃣ The employee fixes the issue, selects the correct email template, drafts a response in the right tone, and sends it to the customer. 5️⃣ Finally, they resolve the ticket in Zendesk. It’s repetitive, manual, and time-consuming - requiring judgment calls and multiple tools. 💡 𝗡𝗼𝘄, 𝘁𝗵𝗶𝘀 𝗲𝗻𝘁𝗶𝗿𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱: 1️⃣ Detect the Issue Automatically When a customer emails support, AI scans the ticket to see if it’s about a missing return label. If the model is unsure, it’s routed to a human for validation. 2️⃣ Check Eligibility Instantly The agent pulls order details from Salesforce—validates if: • The return window is still open (within 30 days) • Customer info (like phone number or postal code) is correct 3️⃣ Fixes Common Errors AI corrects simple mistakes in Salesforce and then sends it to the another agent specialized in customer comms. 4️⃣ Generate a Personalized Response A text-generation agent drafts a tailored email in the brand’s voice, ensuring it’s clear, helpful, and compliant. 5️⃣ Update Systems & Close the Loop The AI agent updates the customer info in Salesforce, the email is sent via Zendesk, and the ticket is marked as resolved. This is “𝘼𝙜𝙚𝙣𝙩𝙞𝙘 𝘼𝙄” in action: Logic (e.g., classifying what the email was about / checking if an order was delivered within 30 days) + text generation agents (like an email generator trained in the brand’s voice, tone, and compliance rules) + seamless integrations with enterprise systems (e.g., Zendesk, Salesforce) working together to solve a problem from start to finish. What's exciting is once enterprises build one workflow like this, they can quickly replicate and scale—reusing components and tackling more complex processes. And this is just the beginning. As these workflows grow, they lay the foundation for 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗴𝗲𝗻𝘁𝘀 which are systems capable of coordinating across workflows to tackle enterprise-wide challenges. 🚀 The majority of 2025 will still be dominated by these highly targeted workflows. But every workflow built today is compounding toward something much bigger.

We built a Zendesk email assist AI agent and it's handling a full quarter’s work for one human support rep. Here's the step-by-step flow: 1. User sends a complex or nuanced product question to support@voiceflow.com 2. Tico (our AI agent) reviews the question and passes the content and intent. 3. The most fitting knowledge base is tapped via confidence level. 4. A personalized, accurate & highly-specific response is drafted. 5. The draft is slotted into Zendesk as a private comment. 6. Our team reviews, tweaks if necessary, and sends it to the user. This has slashed the onboarding and training time for support staff that's typically slowed down by the complexity of the product. The impact? ✅ Our support team is no longer just keeping up; they’re ahead, delivering faster, sharper responses. ✅ Customers feel understood, their issues addressed with pinpoint accuracy, boosting our CSAT scores. ✅ Tico’s continuous learning means every interaction makes it smarter, ready for even the most nuanced queries. So far, Tico Assist is tackling over 2000 tickets - a full quarter’s work for one human support rep, for less than the price of lunch. If you’re navigating high support volumes with a lean team, this type of Zendesk AI Assist Agent can help blend automation with quality for your customers. P.S. Tico doesn’t just fetch any answer. It pulls from the most relevant knowledge base (e.g. a technical code response for a developer question). From my post last week, this multi-knowledge base strategy is something that I think we will see much more of in CX this year.

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe