Tools For Automating Customer Support Tasks

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?

Email templates can help customer service reps improve efficiency. But what happens when just choosing the right one becomes overwhelming? It's a case where AI can unlock human super-skills. One company implemented an AI tool from Laivly to help agents select the right template. Laivly's "Smart Response" feature analyzes incoming emails to suggest the right template for agents to use. Agents can review the suggested template for accuracy, and add personalization before sending the final email. The Smart Response tool improved productivity by 49%. Even better, customer satisfaction increased 10% and first contact resolution rose by 17%. It's a great example of using AI to handle tedious, repetitive tasks so agents can be freed to concentrate on work where they can add more human value. I'm increasingly seeing stories like this. Rather than humans or AI, it's humans and AI.

The 3 types of AI tools every CS leader needs to understand (and how to use them) AI tools are everywhere, but as a CS leader, you need to cut through the noise and understand what actually matters for your operation. Here’s my simplified breakdown for customer success applications: 1/ Large Language Models (LLMs) What they are: The “brains” behind ChatGPT, Claude, Gemini - sophisticated tools that read and write like humans. How CS leaders use them: • Analyzing customer call transcripts to identify risk signals • Generating personalized QBR content based on usage data • Creating customer-specific success plans from templates • Summarizing months of customer interactions before renewal calls Key limitation: They don’t know your customer data unless you feed it to them. 2/ Workflow Automation Platforms What they are: Tools like Zapier, Workato, and Microsoft Power Automate that connect your existing systems and automate step-by-step processes. How CS leaders use them: • Automatically updating health scores when usage patterns change • Triggering alerts when customers miss onboarding milestones • Creating customer pulse reports by pulling data from multiple systems • Routing high-risk accounts to senior CSMs based on specific criteria CS-specific example: When a customer’s usage drops 30% week-over-week, automatically create a task for their CSM, pull recent support tickets, and generate a summary of their recent interactions. 3/ AI Agents *lWhat they are: Digital helpers that can complete specific tasks within larger processes, combining LLM intelligence with system integrations. How CS leaders use them: • Research agents that compile customer background before executive meetings • Health score agents that analyze multiple data sources to predict churn risk • Content agents that create personalized customer communications • Analysis agents that identify expansion opportunities based on usage patterns CS-specific example: An agent that monitors customer communications, identifies mentions of business challenges, researches relevant case studies, and drafts personalized recommendations for the CSM to review. —- I keep thinking about the ways to get started, it all seems like so much. Change management, getting IT or security involved… but you need to just start. Start with your biggest operational pain points: 1. Identify repetitive tasks your team does manually 2. Map which type of AI could address each task 3. Test with simple workflows before building complex agents 4. Measure impact in terms of CSM time saved and customer outcomes The technology exists today. The real work is understanding your CS processes well enough to determine where AI can replace tasks currently requiring human intervention. Remember: Agents handle individual smart tasks. Workflows organize how those tasks connect. LLMs provide the intelligence that makes it all possible. What CS process would benefit most from AI automation in your organization?

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.

You don’t need to be a developer to build intelligent AI workflows anymore, but you need a thing or two about how agents work. With no-code platforms like Make, n8n, and Zapier, deploying AI agents has become faster, more visual, and scalable for business automation. Here’s a step-by-step breakdown of how to deploy AI agents without writing a single line of code 👇 1.🔹Identify the Use Case Focus on repetitive manual tasks, customer queries, or data bottlenecks. Tools: Notion AI, Airtable, ChatGPT, Make.com, Zapier 2.🔹Define Objectives & Scope Outline expected outcomes, key integrations, and KPIs for success. Tools: Miro, Whimsical, Google Sheets, ClickUp 3.🔹Select the Right No-Code Platform Evaluate features, pricing, and scalability before choosing. Recommended: Make.com, n8n, Zapier, Pabbly Connect 4.🔹Design the Workflow Blueprint Map triggers, processes, and output flow visually. Tools: Draw.io, Whimsical, Make.com visual builder 5.🔹Integrate Data & APIs Connect CRMs, email tools, or databases to your automation. Tools: Make.com API modules, n8n HTTP Node, Postman 6.🔹Add AI Components Embed GPT, Claude, or Gemini to enable contextual reasoning and automation. Tools: OpenAI API, Flowise, Langflow, MindStudio 7.🔹Test & Validate Workflows Run real-time test cases and monitor accuracy, latency, and performance. Tools: Make.com Scenario Testing, n8n Test Mode, Postman Monitors 8.🔹Train End-Users Provide clear training materials and internal demos for adoption. Tools: Loom, Notion, Slack, Microsoft Teams 9.🔹Deploy & Monitor Go live with tracking for API usage, success rates, and performance. Tools: Make,com Dashboard, n8n Logs, Datadog 10.🔹Continuous Improvement Refine workflows, add new AI models, and scale to multi-agent systems. Tools: Airtable, LangFuse, Relevance AI, Vercel Ready to deploy your own AI agent without coding? Save this post and start experimenting with tools like Make or n8n today. #AIAgent

In my conversations with business owners, one challenge comes up time and time again: the struggle of answering the same customer questions day in and day out. As someone who's dedicated their career to improving customer experiences, I get it. It's a huge time-sink, especially for small teams. That's why I'm so excited about the potential of AI chatbots. We've seen firsthand how these tools can transform customer service operations. AI chatbots are designed to handle common customer inquiries, like store hours, product availability, or booking appointments. And the best part? They work 24/7. Even when you're not around, your customers can still get the info they need instantly. That’s a game-changer for small teams, saving you time and keeping your customers happy. If you're looking to give your customers faster responses while freeing up your team’s time for more complex tasks, I can’t recommend AI-powered chatbots enough! I’ll share a few resources for those interested in the comments below. 👇

Think AI in CX is exclusive to big tech giants? That’s the trap. Small businesses adopting AI are outplaying the giants and leading the game. If you’re not innovating, you’re missing the revolution. As a consultant working with SMBs, I've seen firsthand how AI tools are leveling the playing field. Recently I was asked by one of my clients to come up with a solution for two challenges: Challenge 1: 24/7 customer support with limited staff and agent attrition Challenge 2: Customer retention in a highly competitive market One of the solutions I stumbled upon is Freshdesk by Freshworks. 1. 24/7 Support with Freddy AI Agents Freddy AI Agents can automate repetitive customer queries, providing round-the-clock support without additional staffing. They use natural language processing to understand customer intent, offering personalized responses across multiple channels like the web, social media, and messaging apps. They can handle customer inquiries across multiple languages, provide hyper-personalized responses, and seamlessly transfer more complex issues to human agents when needed. 2. AI-Generated Customer Retention Insights Freddy AI goes beyond basic support by analyzing customer interactions using machine learning algorithms. Freddy AI can: • Provide personalized customer experiences • Generate predictive analytics about customer trends • Provide recommendations to agents to help respond to tickets faster with Freddy AI Copilot • Create actionable insights that help businesses improve their customer retention strategies 3. Multilingual Support and Global Reach     Freshdesk enables businesses to break language barriers through: • Multi-lingual portals and knowledge base • Integration with messaging platforms like WhatsApp, Instagram, and Slack • Ability to understand and respond to customer queries across different languages Tailored support based on customer preferences and history, creating unique interactions Bonus Insight: When implementing an AI solution, focus on AI that is immediately usable and directly supports business objectives, ensuring your SMB can leverage advanced technology without complex implementation. Have you given AI a thought for your CX? Any additional tactics, thoughts or tools?

I automated 95% of our customer support flow over the weekend (for real) thanks to AI tools that turn anyone into a technical product builder. There are 3 steps to the workflow: First is a RAG AI agent that's trained on our user guide and a corpus of customer support email threads. When a support ticket comes in, I just copy it in here and get an email response that I can send back to the user This agent works because it's built on a continuously-improving knowledge base, curated by a second AI system. This system periodically reads through a database of support interactions to find new learnings to add, which in turn makes the RAG agent more capable over time At the bottom is a system that collects new support email threads that I resolve and adds them to a database. I have third AI system running in the background that automatically classifies and tags threads in my inbox for this purpose These 3 AI systems working together take all of the CS heavy lifting off my shoulders, allowing me to spend valuable time elsewhere We don't automate to remove people from the equation - we do it to leverage ourselves better where we're needed It's how we manage to scale so effectively at Aomni as a 5-person team

If you’re not using AI as a Product Manager, you’re making your job harder. Here’s how AI can take work off your plate: 📊 Customer & Market Insights ↳ MonkeyLearn & Thematic – Analyzes customer feedback and support tickets, surfacing key pain points withoutmanually sifting through endless data. ↳ Crayon & Kompyte by Semrush – Tracks competitor feature launches, pricing updates, and messaging changes, so you don’t have to. 📈 Data-Driven Decision Making ↳ Amplitude AI & Mixpanel Predict – Identifies user behavior patterns, flags churn risks, and suggests next-best actions. ↳ Optimizely AI – Speeds up A/B testing by analyzing results and recommending optimizations. 🛠️ Roadmap & Prioritization ↳ airfocus AI – Helps prioritize feature requests by weighing impact, effort, and business value. ↳ Craft.io – Translates customer insights into prioritized roadmap decisions. ⚙️ Workflow Automation ↳ Slack AI – Summarizes all your pending messages you missed, so you don’t have to scroll endlessly. ↳ Fireflies.ai & Otter.ai – Transcribes and summarizes meetings, turning conversations into actionable insights. ↳ Zapier AI – Automates repetitive tasks like updating Jira tickets, notifying stakeholders, and syncing data across tools. 🚀 Product Development Acceleration ↳ Miro AI – Auto-generates user journey maps, affinity diagrams, and feature brainstorming templates. ↳ Figma AI – Suggests design variations and speeds up wireframing. ↳ GitHub Copilot – Helps engineers write and debug code faster, making technical PMs more effective. 🤖 Customer Support & Bug Tracking ↳ Intercom AI & Zendesk AI – Auto-categorizes support tickets and summarizes issue trends. Super useful when a recurring issue turns into a bug—less time digging through tickets, more time fixing the root cause. ↳ Pendo.io AI – Identifies friction points in onboarding and usage, helping refine product adoption strategies. 🔍 Finding What You Need (Faster) ↳ Atlassian Intelligence (Jira) – Lets you search for tickets in plain English instead of using JQL. No more wasting time trying to remember the exact syntax to find that one bug from last quarter. 📝 AI for Documentation & Communication ↳ OpenAI Chat GPT, Google Gemini & Notion AI – Drafts PRDs, product strategy docs, and meeting summaries effortlessly. ↳ Tome AI & Beautiful.ai – Turns ideas into polished presentations in minutes. ↳ Grammarly & Jasper AI – Refines messaging, emails, and product copy. AI won’t replace PMs—but PMs who use AI will replace those who don’t.