Automating Routine Customer Inquiries With AI

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

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.

Automating FAQ Extraction from Sales Calls I just built an automation that's saving me hours every week while improving our client experience. Instead of manually reviewing every sales and support call to identify common questions, I created a system that automatically extracts FAQs from Fathom call transcripts. The Problem: Our team was constantly fielding the same questions, but we had no systematic way to capture and organize these insights. I didn't want to spend hours listening to recordings, and I definitely didn't want our website chatbot giving outdated or inaccurate information. The Solution: A simple automation using Make, Claude AI, and Notion that: - Automatically processes Fathom transcripts - Uses AI to extract questions, answers, topics, and user journey stages - Creates structured FAQ entries in Notion - Includes human review to prevent AI hallucinations - Pushes approved content to our website chatbot via Google Sheets The Impact: Now our team can quickly reference common questions in Notion, train new employees more effectively, and keep our website chatbot current with real customer inquiries. The human-in-the-loop approach ensures accuracy while the automation handles the heavy lifting. What's Next: I'm expanding this to work with Zoom transcripts and adding sentiment analysis to identify upsell opportunities and at-risk clients before they churn. This uses maybe 10 operations, so its inexpensive to use, and saves countless hours while improving our client experience. What repetitive processes are you automating in your business?

There's one use case for AI agents not being talked about enough: volatile or seasonal industries. Think about what crypto, fintech, travel, and even retail have in common. Their surges in volume (some random, some not) and customer inquiries make it extremely challenging for traditional CX systems to keep up. But where legacy systems struggle, AI systems step up. Here's how: 1. Scalability When inquiry volumes spike, AI agents can handle the influx without missing a beat. There are no delays from hiring surplus human agents to handle more volume, making AI agents both cost- and process-efficient. 2. Consistency Whether it's 1K or 1M customer inquiries, AI agents guarantee the same level of accuracy and precision every time. Humans need downtime, AI doesn't. 3. Prioritization Customer inquiries come with varying degrees of complexity. While AI agents take care of the low-hanging fruit and repeatable tasks, human agents can focus on the high-touch cases that demand personal attention. Take Coinbase’s customer support, for example. They handle $226B in quarterly trading volume in 100+ countries. Their margin of error is slim, and CX mistakes could cost billions. Instead of leaning on human CX alone, they use AI agents to: • Handle thousands of messages per hour • Reduced customer service handling time • Improve search relevance for their help center The enterprises we work with at Decagon experience the same benefits using AI customer service agents—scalable support, no gaps in performance, and higher customer satisfaction. Just because your industry is volatile doesn't mean your CX should be.

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

Voice has been AI's blind spot. RingCentral just made it the centerpiece. For years, enterprise AI investment has flowed into documents, emails, and meeting summaries — the written record of business. Voice, the richest signal of customer intent, has been treated as an afterthought: recorded, transcribed, analyzed after the moment that mattered most had already passed. RingCentral is changing that calculus. This week the company announced a deep integration with OpenAI (GPT-5.2), bringing generative AI directly into live enterprise voice conversations — not post-call summaries, but real-time intelligence in the flow of the interaction itself. Two new agentic products anchor the launch: AI Receptionist (AIR) automates inbound call handling — answering, scheduling, routing, and executing natural-language follow-up without human intervention. AI Virtual Assistant (AVA) picks up where AIR leaves off. When a call does route to a human, AVA surfaces full customer context and recommended next actions before the employee says a word. Together, they form a unified intelligence layer across the entire interaction lifecycle. This isn't a chatbot bolted onto a phone system. It's AI that actively shapes conversation outcomes. The early numbers are hard to ignore. AIR surpassed 5,000 customers in just two quarters. Televero Health reported a 97% patient satisfaction rate, a 14% increase in monthly appointments, and more than $200K in additional monthly revenue within four months of deployment. On the governance side — a persistent friction point in enterprise AI adoption — customer data is not used to train public models. For regulated industries like healthcare and financial services, that's not a nice-to-have. It's a requirement. The vendors that win the AI transition won't be those that bolted features onto legacy platforms. They'll be those that rebuilt the intelligence layer from the ground up. I'm in Phoenix this week with the RingCentral team and looking forward to going deeper on this partnership — including what flexibility looks like if they ever want to evolve beyond OpenAI. In a market moving this fast, keeping your options open is just good strategy. The passive phone system era is effectively over. The question now is which platform can deliver AI-powered voice responsibly, at scale, and with measurable results. #UnifiedCommunications #EnterpriseAI #RingCentral #AI #CX #VoiceAI #AgenticAI

Why Traditional Call Centers Are Transitioning to AI-First Support Customer expectations have evolved. They now demand instant responses, round-the-clock availability, and consistent experiences across every channel. Traditional call-center models cannot meet these requirements at scale - AI can. Key Drivers Behind the Shift Rising Customer Expectations Customers prefer real-time support over waiting on hold. AI enables instant, accurate responses across chat, voice, and digital channels. Increasing Operational Costs Recruitment, training, and agent attrition create ongoing cost pressures. AI manages repetitive queries at near-zero marginal cost, allowing organizations to scale efficiently. High Volume of Repetitive Queries Up to 70% of support requests are routine (order updates, resets, FAQs). AI resolves these immediately, allowing human agents to focus on complex, high-value interactions. 24×7 Availability Is Now Essential While human agents work in shifts, customers expect continuous support. AI ensures uninterrupted service - even during nights, weekends, and peak times. Faster Resolution, Better CX AI can instantly search knowledge bases, suggest responses, and predict next issues, reducing handling time and minimizing customer frustration. Seamless Omnichannel Experience AI connects conversations across chat, email, voice, WhatsApp, and in-app channels, ensuring context moves with the customer. AI Enhances Human Capability AI is not replacing human agents - it is augmenting them. AI handles scale and speed. Humans handle empathy and complex decision-making. The result: higher customer satisfaction and more empowered support teams.

The 10-Year Evolution of AI in Customer Support  Srushti Pawar — one of our forward deployed engineers — and I were chatting over lunch about how wild the evolution of AI customer support has been over the last decade. Not just in theory, but in what we’re seeing work in production right now. We’ve deployed an AI workforce for a fast-growing fintech in the mobile payments space — helping them scale customer support without scaling headcount. We expected great results: well-designed workflows, tight guardrails, strong integrations, and Qurrent’s native orchestration layer. But even then… sometimes we still say, “wait, they figured that out?” These agents handle edge cases faster — and more accurately — than humans. It’s kind of like watching a toddler suddenly speak in full sentences at dinner... and solve a household problem, before asking a follow-up question. That conversation sent me down a rabbit hole. I looked back at how we got here — and just wanted to share: a quick timeline of how AI in customer support has evolved… and why what we’re building now feels like a whole different species. 💬 2015–2017: Rule-Based Bots (mainstream adoption) Tech: regular expression(Regex), decision trees, menu-based flows How it worked: Fully scripted keyword-response flows Limitations: Rigid and fragile Introduced: First wave of automation to deflect repetitive queries 🧠 2018–2020: NLU-Powered Assistants Tech: BERT, intent classification, entity extraction How it worked: Parsed intent + entities (“Cancel my last order” → intent= cancel_order, entity=last) Limitations: No memory or contextual nuance Introduced: True natural language understanding 🔄 2021–2022: Context-Aware Assistants Tech: Session memory, KB search, API calls How it worked: Maintained short-term memory and connected to systems Limitations: Session-bound, logic-heavy scaling Introduced: Multi-turn workflows and live integrations 🤖 2023–2024: Generative AI Agents Tech: GPT-4+, RAG, semantic search How it worked: Generated natural replies using LLMs + contextual prompts Limitations: Hallucinations; no action or workflow memory Introduced: Humanlike fluency, real adaptability 🚀 2025–Present: Autonomous AI Agents Tech: LLMs with planning, agentic workflows, memory, orchestration How it works: Understands, retrieves, executes, manages workflows, self-optimizes Limitations: Still maturing in nuance and edge handling Introduced: End-to-end resolution. Named AI agents functioning as real team members! 📊 The agentic workflow we built for this customer now RESOLVES 60%+ of their incoming email support tickets — with a 1-min average first response time. Jaws dropping! I welcome more surprises to come. #AIagents #CustomerSupport #ConversationalAI #AutonomousAgents #GenerativeAI #EnterpriseAI

This is why AI agents are exploding in adoption—they deliver real business value by turning LLM intelligence into automated action. They are becoming the backbone of automation in customer support, operations, sales, and internal workflows, replacing repetitive tasks that humans perform by clicking buttons and following rules. Instead of just generating text, AI agents orchestrate actions, making them far more valuable in real business environments. A perfect example is customer-support order-tracking. Every day, support teams receive hundreds of emails asking, “Where is my order?” A human agent reads the message, extracts the order number, searches in the backend system, checks the shipment status in the carrier portal, decides what’s wrong, and finally replies or creates a follow-up ticket. This manual process takes 2–3 minutes per email—highly repetitive and expensive at scale. An AI agent can now automate this entire workflow end-to-end. It first extracts the order ID from the customer’s message, then calls the lookup_order tool to fetch order details, and the check_tracking_status tool to get carrier updates. Next, it analyzes the status and determines whether delivery is delayed, lost, or on track. Based on the result, it triggers the right action, such as create_internal_ticket, initiate_carrier_trace, or reschedule_delivery. Finally, the agent generates a personalized reply to the customer with the latest status—without any human involvement. With memory, it can even handle future follow-ups intelligently. Read more on the internal architecture of an AI Agent in detail: https://lnkd.in/gEhVX5cY Build Your First AI Agent in 10 Minutes! (No Code Needed): https://lnkd.in/gjNf5yyr

Imagine scheduling a healthcare consultation and instantly triggering a comprehensive summary of your entire medical history, seamlessly available to your doctor. After your visit, prescriptions are automatically routed to pharmacies, and insurance claims are pre-verified and swiftly processed, creating real-time, frictionless coordination across multiple departments. Welcome to the era of agentic AI. Unlike its predecessor, which responded passively to prompts, agentic AI introduces autonomy, adaptability, and intelligence into customer service workflows, reshaping the function from the ground up. Which is now being referred to as the “gen AI paradox” The McKinsey & Company report "Seizing the Agentic AI Advantage" explains that the generative AI era has encountered a paradox. Despite 78% of companies deploying generative AI in at least one business function, more than 80% have not yet experienced a significant impact on their bottom line. The problem is that most deployments have been "horizontal" (general-purpose chatbots and copilots that provide only surface-level efficiencies). Agentic AI breaks this mold by enabling "vertical" transformation (embedding intelligence directly into complex business processes, including customer service). Unlike traditional models, agentic AI addresses the value gap by integrating advanced reasoning, autonomy, and adaptability into mission-critical workflows, fundamentally transforming how customer service is delivered and experienced. 🔹Proactive Issue Detection: Agentic AI anticipates and resolves problems like service outages or delays before customers even notice, shifting support from reactive to preemptive. 🔹Autonomous Problem Resolution: Routine issues such as refunds or reorders are handled automatically, freeing up human agents for higher-value work. 🔹Personalized Customer Interactions: Every engagement is dynamically tailored using real-time data, boosting satisfaction and enhancing deeper customer loyalty. Companies that reimagine their customer service operations around agentic AI report remarkable outcomes: 🔹Resolution times reduced by 60-90% 🔹Up to 80% of routine issues are handled without human involvement This shift elevates the human roles, transforming organizational roles: 🔹Human agents transition into overseeing complex or exceptional cases, becoming strategic managers rather than task executors. 🔹New specialized roles emerge, including agent orchestrators, prompt engineers, and human-in-the-loop designers, ensuring strategic human-AI collaboration. Agentic AI is completely rethinking how organizations operate, serve, and scale, and I am excited to see what’s next in customer service. See the full Agentic AI report on the carousel. #CustomerService #AgenticAI #DigitalTransformation #AI