AI-Powered Support Ticketing

If I were the VP of Support at an enterprise company dealing with repetitive customer support tickets, here’s how I’d use AI to power KCS and improve ticket resolution while turning my support agents into “heroes”: First, some context: - Most support tickets are recurring, yet agents have to field every single one of them individually (this is unscalable).  - Agents are only rewarded based on the number of tickets resolved and have a hard time improving support quality (can be unrewarding) The best way to go about this problem? Enabling agents to externalize documentation on their own and improve support quality with every logged request, using AI to power Knowledge-Centered Support (KCS) Here’s how I’d implement this at an enterprise company: 1) Democratize knowledge creation Support agents know customer issues best, so it doesn’t make sense to wait for technical writers (who are already swamped) to create knowledge articles. With the help of AI, you can enable support agents to generate knowledge articles on their own, just by clicking a button. 2) Externalize new knowledge All new knowledge articles can be pushed to your external customer help center/knowledge hub right away. With that, customers can either resolve issues on their own or ask an AI Chatbot (that has immediate access to all knowledge articles). 3) Iterate & improve knowledge Now that recurring tickets are handled, support agents can dedicate their time to tickets that *actually* need human help. AI can then help them update existing articles as similar requests come in. This is WAY more efficient than relying on technical writers because your agents are already “on the ground.” 4) Gamify support process On the backend, AI can track & display: - Which customer issues were resolved  - Which knowledge articles were referenced - How many customers were assisted by each agent - How many tickets were resolved or deflected This makes it easier to boost support morale because agents see the REAL impact of what they’re doing for customers and the company – in short, they become “heroes.” (We do this ourselves at Ask-AI) TAKEAWAY An AI-powered KCS will help you improve your overall customer experience. You can resolve customer issues faster, your support agents are empowered – and the VP of support can report better TTR and CSAT metrics. Any thoughts on this?

For months, one of our biggest operational challenges was the mandatory human touchpoint needed to route customer interactions. Every new support ticket required a Tier 1 agent to read the description, classify the Intent, judge the Sentiment, and then manually route it to the correct specialist or seniority level. This delay was a drain on agent time and, worse, a source of customer frustration. In the last few days we've successfully implemented an AI-powered system using the Gemini API to solve this problem. We trained a model on our historical data to automatically and accurately classify every incoming interaction in real-time. The Model Now Automatically Determines: 🎯 Intent: Is this a 'General Inquiry,' 'Subscription Cancellation,' or 'Billing Inquiry'? 😠 Sentiment: Is the customer 'Neutral' or 'Critical Negative'? 📈 Priority Score: A dynamic score (1-5) that combines intent and sentiment. The Impact is Immediate and Measurable: Eliminated Triage Bottleneck: Senior agents now spend 100% of their time solving problems, not reading tickets. Faster Crisis Response: Critical issues (Priority Score 5) are routed directly to the L3 team in seconds, not minutes. Improved Customer Satisfaction (CSAT): By routing complex issues immediately, we're cutting down on resolution time and reducing the need for costly agent transfers. This shift is a game-changer for our customer experience and a prime example of how targeted AI tools can drive real operational efficiency.

We’ve spent the last two years building production AI agents for customer support. Real agents, live in enterprises. And it honestly pains me to see companies relying on fragile RAG setups to handle their customers. RAG alone fails because customer support isn't a search problem. It's an action problem. Most "AI solutions" are just ChatGPT connected to a knowledge base. They fail spectacularly when customers need actual help. When a customer says "I need a refund for my cancelled order from last month," RAG might find your refund policy. But that's useless. The customer needs the refund processed, not a policy explanation. Here's what actually works for AI customer support- Agentic AI with three critical components RAG systems lack: 1. Tool access Your AI needs to connect to billing systems, CRMs, and internal tools. Reading knowledge bases isn't enough. Processing refunds, updating accounts, and troubleshooting require real system integration. 2. Context memory Every customer interaction builds on previous ones. AI agents must remember past tickets, purchase history, and conversation threads. RAG retrieves documents. Agents maintain user-level relationships. 3. Action boundaries The difference between helpful and dangerous AI is knowing when to stop. Agents need guardrails that define exactly what actions they can take and when to hand off to humans. “Agentic AI” has become the hottest buzzword in enterprise AI. But very few have actually shipped it. ---- At Fini, we've built enterprise Agentic AI that solves 80% of tickets with zero human intervention. The companies winning in AI support aren't using better models. They're building better systems. Are you still stuck with basic RAG chatbots? Or already moving to Agentic AI?

“The IT Director said, ‘I didn’t hire engineers to reset passwords all day.’ That’s when we brought in AI Agents.” We deployed AI Agents for an IT Helpdesk drowning in 2,400 tickets a week. What happened next — no one expected. Not even us. Day 1: The IT Director looked defeated. “Two engineers just quit. 73% of our tickets are the same 12 issues — password resets, VPNs, access requests. We’re not doing IT anymore. We’re doing repetition.” His team was exhausted. Marcus, a Level 1 analyst: “I spend 6 hours a day on stuff that could be automated.” Jennifer, a Level 2 engineer: “I get pulled from cloud migration projects to reset passwords.” So we built three AI Agents — not chatbots, not copilots — real digital teammates. 🤖 Agent 1: First Responder – handled resets via ServiceNow, AD, and Okta 🧩 Agent 2: Troubleshooter – guided users using 50K past tickets 📊 Agent 3: Analyst Assistant – analyzed trends, flagged anomalies, learned daily At first, the team resisted. Then, something changed. Week 6: An “ordinary” access issue came in. The AI noticed three failed logins, unusual location data, and a pattern in old tickets. It escalated immediately. Result: A live security breach caught in 2 hours instead of days. Jennifer turned to me and said, “Okay. I’m listening now.” 90 Days Later: ✅ 68% of routine tickets auto-resolved ✅ Avg. resolution time: 22 → 8 minutes ✅ CSAT: 67% → 89% ✅ Burnout? Gone. ✅ Team morale? Highest in years. Marcus earned his Azure certification. Jennifer rejoined the cloud migration. David started building proactive monitoring tools. The IT Director told us, “The agents didn’t replace my team. They freed them to do what I actually hired them for.” The Real Lesson: AI’s greatest ROI isn’t headcount reduction. It’s human potential — finally released from repetitive work. 💡 If your IT team could reclaim 60% of their time from routine tickets, what would they build next? #AIAgents #HelpDeskAutomation #ITOperations #EnterpriseAI #AIAutomation #ITLeadership #DigitalTransformation #Pronix #FutureOfWork

𝗔𝗜 𝗮𝘀 𝗟𝟬 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 #𝟭 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝗼𝗳 𝗔𝗜 𝗳𝗼𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀. If you look beyond the hype cycles and shiny new demos, one pattern is emerging clearly in the real world: companies that are serious about deploying AI don’t start with moonshot use cases — they start with support. Almost every business we work with begins their AI journey with the same simple but powerful idea: use AI to handle Level 0 (L0) support. The first step is internal. They deploy a support chatbot trained on their entire knowledge base — documentation, websites, support articles, technical manuals, videos, podcasts — anything a human agent would otherwise have to read, remember, and search through under pressure. They give this AI to their own support staff first. Why? Because good support teams know their pain points better than anyone. They pressure-test the AI on real tickets and real questions. They find the gaps. They learn how to use it to triage issues. They build trust. Once the team is confident, they roll the AI out to their customers — usually as a website assistant available 24/7. From that moment on, repetitive questions get resolved instantly. Tickets get deflected. Only the complex, high-value issues reach a human agent. Everyone wins. * Customers get immediate answers instead of waiting in queue. * Support agents spend their time on meaningful, challenging cases — not password resets or basic troubleshooting. * Companies scale without needing to scale headcount at the same rate, freeing resources for growth. It’s a simple shift: treat AI as your L0 support agent. Let it handle the front line. Let your people focus where they add the most value. We’re seeing real, measurable ROI from this pattern — millions saved in support costs and customer satisfaction scores that climb instead of flatline. And because tools like CustomGPT.ai make this no-code and affordable, businesses don’t need to hire an AI engineering team just to get started. If you want a validated, practical place to start with AI in your business: this is it. L0 support is real. It works. It pays for itself — and then some.

At Rackspace, we reduced IT ticket volume by 70% without adding headcount. By integrating an AI coworker directly into Microsoft Teams, it now automates 500+ tickets end-to-end each month. AI works best when employees don’t have to change how they work. So our team built an AI coworker for IT (RITA) that doesn’t need a new portal or separate interface. By running inside Microsoft Teams, an app Rackers use every day, RITA fits naturally into existing workflows. Employees don’t need to switch tools or change how they work, which drives widespread adoption. Beyond answering questions, RITA executes workflows in real time and handles device provisioning, account lockouts, and everyday software issues. It completes the work, not just the request, which lets IT teams spend less time on triage and more time on higher-value work. As a result, we see a widening gap in the market. Teams that treat AI as a tool stay stuck in pilots, while teams that design AI as a participant in operations scale faster. After running RITA inside Rackspace and refining it in production, we deploy it for other IT teams that want to scale without adding headcount. Happy to start a conversation via LinkedIn DMs if this is something you’re actively working on. And if helpful, we’ve written up how this approach played out alongside three other agentic AI solutions we deployed at Rackspace Technology. The link is here: https://bit.ly/4q177Ii.

Support teams face constant pressure to resolve cases faster without overloading engineering. For one Glean customer, valuable resources were tied up in avoidable tickets, MTTR (mean time to resolution) hovered at nearly two days, and agents spent hours manually triaging cases. Their goal: boost self-solves, improve MTTR, and reduce R&D reliance – without adding more tools. So they embedded Glean in Zendesk, giving agents prompts to quickly gather knowledge across all company data. In triage, agents use Glean to find similar tickets, summarize runbooks and past Jira investigations, and compile clear updates for customers or well-packaged escalations. That streamlined process now drives faster resolutions, smoother knowledge transfer, and consistent workflows—leading to: • 34% increase in self-solves with more future automation planned - this is incredible progress • 24% faster MTTR (1.9 → 1.5 days) • 2–4 hours saved per week for 85% of users (13–26 business days/year) • Reduced R&D involvement in lower-tier tickets By streamlining resolutions, knowledge transfer, and process consistency, the team achieved remarkable results – proof of what’s possible when AI is embedded into everyday workflows. Stories like this are energizing – showing how teams are using Glean to reimagine what they can accomplish.

Little known fact: Lovable's support center has been powered by Mintlify agents for over a year now, handling tens of thousands of users every month. Here's what most people get wrong about AI support agents: they think the hard part is the AI. It's not. It's the knowledge base underneath it. You literally cannot build a good support agent without solid documentation. The model is only as useful as what it knows. But here's the thing that I cannot emphasize enough: it's not just about having docs. It's about having correct docs. Imagine your support agent is powered by a knowledge base where the pricing page is a week out of date. Or a feature that got deprecated is still documented as active. What happens? Thousands of tickets get answered confidently and incorrectly. Your AI agent doesn't hesitate. It doesn't caveat. It just tells the customer the wrong thing with a straight face. That's often worse than having no agent at all. This is why I believe self-updating documentation is inevitable. Not a nice-to-have - a prerequisite for AI support actually working at scale. The companies winning at AI support aren't always the ones using the most expensive models. They're the ones with the most accurate, up-to-date knowledge powering those models.

What if 90% of the bugs in your backlog never made it to Product or Engineering? For the last year, I’ve been not-so-secretly-wishing for AI that could triage and respond to every issue or bug before someone on our team ever sees it. At Luxury Presence, that dream is now very close to reality. 🤩 For context, we are a $75M+ ARR company supporting: • Millions of unique MLS real estate listings • Tens of thousands of websites • Hundreds of mobile apps Because of all this volume, It’s not uncommon for an issue to pop up that affects just one listing on one website. But for that customer, it’s mission-critical to resolve quickly. Our teams have been building two AI agents to make triage and resolution faster than ever. 🤖 🏠 Proppy – our MLS data expert. It lives in Slack, available to everyone in the company. It can scan millions of listings, run over a dozen data integrity checks, and validate authorizations to see why a listing isn’t showing as expected. What often took days can now be done in less than a minute. Next step: integrate with Linear to auto-create well-formatted tickets when engineers need to step in. Huge shoutout to Raj Vegulla MS, MBA Bradford Cook Dayton Tipton Saneel Bidaye and their teams for making this a reality! 🤖🔨 Auggie – short for Augmented Engineering. Zach Wills Andrew C. and team are just getting started at transforming how we do engineering. I'm sure we'll share more publicly on what we are doings soon, but here's the tl;dr It's powered by Claude Code, Devin.ai, and MCP servers. It takes the first pass at Linear bugs, asks clarifying questions, understands our codebase, past tickets, and Slack knowledge, and creates shovel-ready tickets — with suggested fixes. Next step: have it write the PR itself. Once we connect both with Fin from Intercom, customers will get near-instant resolution to even niche and technical issues. 🤯 Feels like the future is already here. Please share if you are doing any similar and have any tips to share. We're all just trying to figure this stuff out together!

I scoped 23 AI projects over the last 6 months. Here is what drives the highest ROI: It’s all about small, repetitive tasks. Ones that are executed thousands of times. These tasks will often look boring. Something you would hire an intern to do. But if they happen thousands of times, across dozens of employees? That’s the gem. Automate just 10% of that, and you’re already saving a few full-time roles. Here are the real use cases I came across: Case 1: B2B Retail – Customer Support Problem: A 15-person team spends their day on the phone, answering the same 10–15 questions all over again. AI Solution: AI assistant for customer support. Handles the top 2 question categories (delivery + invoices) ROI: Reducing 30% of workload In Switzerland: ~ €18K savings annually per employee  Impacted: Customer support employees Case 2: Finance – Processing Subsidiary Reports Problem: Global company with >1,000 subsidiaries. Each sends monthly reports as semi-structured PDFs. Each can use their own format. Team spent ~20 mins/file: parsing, formatting and inputting into ERP. AI Solution: Document parsing + data extraction Extracted key fields: revenue, amortization, actives, passives Flags inconsistencies, sends for review ROI: In Switzerland: ~ €19K savings per employee annually  Impacted: Accounting specialists Case 3: IT Support – Internal Ticket Automation Problem: Simple, recurring IT tickets made up 40% of IT workload (password resets, access requests, laptop replacement inquiries) AI Solution: AI-powered chatbot for first-level IT support, integrated with ITSM tool Handles verification, password resets, basic troubleshooting Triggers internal workflows automatically ROI: Saved 6 minutes per ticket in IT department 73% faster resolution = happier employees In Switzerland: ~ €16K savings per employee annually  Impacted: IT support employees You don’t need to aim for a moonshot. You need small use cases with a clear structure, repeated hundreds of times. If you're looking for AI use cases that deliver real value, not just hype: start small, think boring, scale fast. Want help spotting these in your org? Let’s talk.