Implementing AI-Driven Support

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?

AI in Customer Support isn’t new. I’ve been rethinking how we actually use it. Customer Support is moving past basic "faster replies" and learning to implement Claude as a core part of our workflow. The goal? Shifting from reactive firefighting to structured, scalable systems. It’s a work in progress, but here is the blueprint we’re using to turn Claude into a true CX reasoning engine: 1️⃣ It’s not about speed. It’s about structure. Yes, you can draft replies faster. But the real value comes from setting it up properly: → align it with your tone and guidelines → connect it to your knowledge base → define clear boundaries (what it can and can’t say) → train it to understand context, not just keywords That’s how you get consistent, reliable output across the team. 2️⃣ It helps move Support from reactive → proactive Used well, it’s not just answering tickets. It’s helping you: → detect sentiment and urgency → identify recurring friction points → surface gaps in self-service → spot early churn signals That’s where Support starts influencing the whole customer experience. 3️⃣ It fits into your existing workflows (not replaces them) The most effective setups I’ve seen are simple: → Claude + Zendesk → ticket analysis → Claude + Zapier → automate workflows → Claude + Gong→ review calls → Claude + Intercom → inbox support → Claude + n8n → workflow automation → Claude + Notion → knowledge management No complex rebuilds. Just better use of what you already have. 4️⃣ The quality of output = quality of input Small things make a big difference: → assign a role (support agent, CX lead, analyst) → provide context (customer, goal, constraints) → iterate with examples (good vs bad responses) Without this, you get generic answers. With it, you get something your team can actually use. From a leadership perspective, this isn’t about “adding AI.” It’s about designing how your Support team operates at scale. Because the goal isn’t to answer more tickets. It’s to build a system where fewer things break, and when they do, the experience still feels consistent. If you’re already using AI in Support, what’s actually working for you? 👇

60% of support tickets are repetitive. And, customers expect immediate responses. That creates pressure on teams and frustration for customers. This is why support is one of the most practical and now proven places to apply AI. AI can handle common, repeat questions instantly, in your tone, using your knowledge base and CRM data. That frees up humans to focus on situations that require judgment, empathy, and creativity. One of our customers, The Knowledge Society (TKS) Society, did exactly that. Every enrollment season, they saw a surge of messages across email, Facebook Messenger, and WhatsApp. The busiest time of year was also the most overwhelming for their team. They implemented the Customer agent to answer common enrollment questions around the clock. Today, close to 80% of inquiries are handled automatically. Their team now spends more time on complex conversations and less time copying and pasting the same answers. The (ISSA) International Sports Sciences Association also scaled with Customer Agent. They were managing multiple support channels across different tools. The experience was fragmented for their team and inconsistent for customers. By introducing an AI agent to handle repetitive questions across channels, they cut response times in half and created a more consistent experience. Over 8,000 companies are already using HubSpot’s Customer Agent, with resolution rates above 67%. This is the real opportunity with AI in support.

SMBs are facing a critical challenge: how to maximize efficiency, connectivity, and communication without massive resources. The answer? Strategic AI implementation. Many small business owners tell me they're intimidated by AI. But the truth is you don't need to overhaul your entire operation overnight. The most successful AI adoptions I've seen follow these six straightforward steps: 1️⃣ Identify Immediate Needs: Look for quick wins where AI can make an immediate impact. Customer response automation is often the perfect starting point because it delivers instant value while freeing your team for higher-value work. 2️⃣ Choose User-Friendly Tools: The best AI solutions integrate seamlessly with your existing technology stack. Don't force your team to learn entirely new systems. Find tools that enhance what you're already using. 3️⃣ Start Small, Scale Gradually: Begin with focused implementations in 1-2 key areas. This builds confidence, demonstrates value, and creates organizational momentum before expanding. 4️⃣ Measure and Adjust Continuously: Set clear KPIs from the start. Monitor performance religiously and be ready to refine your AI configurations to optimize results. 5️⃣ Invest in Team Education: The most overlooked success factor? Proper training. When your team understands both the "how" and "why" behind AI tools, adoption rates soar. 6️⃣ Look Beyond Automation: While efficiency gains are valuable, the real competitive advantage comes from AI-driven insights. Let the technology reveal patterns in your business processes and customer behaviors that inform better strategic decisions. The bottom line: AI adoption doesn't require disruption. The most effective approaches complement your existing workflows, enabling incremental improvements that compound over time. What's been your experience implementing AI in your business? I'd love to hear what's working (or not) for you in the comments below. #SmallBusiness #AI #BusinessStrategy #DigitalTransformation

Shopify's AI Mandate Just Changed CX Forever: What Leaders Need to Know "𝘉𝘦𝘧𝘰𝘳𝘦 𝘢𝘴𝘬𝘪𝘯𝘨 𝘧𝘰𝘳 𝘮𝘰𝘳𝘦 𝘩𝘦𝘢𝘥𝘤𝘰𝘶𝘯𝘵, 𝘱𝘳𝘰𝘷𝘦 𝘈𝘐 𝘤𝘢𝘯'𝘵 𝘥𝘰 𝘵𝘩𝘦 𝘫𝘰𝘣." When I read that line in Shopify CEO Tobias Lütke's leaked memo, I had to pause. Not because it was shocking—but because it's exactly what I've been preparing my team for since last year. This memo wasn't just another tech CEO's AI manifesto. It's the future of our industry written in plain text. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄: ⛔ The old playbook—adding headcount to manage growing ticket volume—is officially dead. ✅ The new approach? AI-first support that leverages human expertise where it truly matters. 5 Key Takeaways from Shopify's AI Mandate: 1. AI usage is now a baseline expectation ↳ Not for tech teams. For EVERYONE. 2. Teams must justify why AI can't handle a task ↳ Not the other way around. 3. Performance reviews will include AI proficiency ↳ This isn't optional anymore. 4. AI must be part of the prototype phase ↳ Start with AI, then add human elements. 5. Learning is self-directed, but sharing is mandatory ↳ This is a team evolution, not a solo journey. What Support Leaders Can Do Today: • Audit your team's current AI literacy ↳ Set a team goal to complete an AI course this quarter (we did) • Map processes that could be AI-first ↳ Which workflows would benefit most? • Create sharing mechanisms for AI wins ↳ What worked? What didn't? Why? • Build AI experimentation into support ops ↳ Small, regular tests > big initiatives • Redefine what "support headcount" means ↳ Hint: It's not just bodies in seats After a decade+ in support leadership, I've seen many shifts, but nothing like this. The gap between AI-empowered support teams and traditional ones will widen dramatically in the next 12 months. Which side of that gap will your team be on? P.S. Has your company made any "AI mandate" moves yet? What was your team's reaction? Would love to hear your experiences 👇

Two weeks ago I said AI Agents are handling 95% of our sales and support and I replaced $300k of salaries with a $99/mo Delphi clone. 25+ founders DM’d me… “HOW?” Here’s the 6 things you MUST do if you want to run your entire customer-facing business with AI: 1. Create a truly excellent knowledge base. Your AI is only as good as the content you feed it. If you’re starting from zero, aim for one post per day. Answer a support question by writing a post, reply with the post. After 6mo you have 180 posts. 2. Have Robb’s CustomGPT edit the posts to be consumed by AI. Robb created a GPT (link below) that tweaks posts according to Intercom’s guidance for creating content for Fin. The content is still legible to humans, but optimized for AI. 3. Eliminate recursive loops - because pissed off customers won’t buy If your AI can’t answer a question but sends the customer to an email address which is answered by the same AI, you are in trouble. Fin’s guidance feature can set up rules to escalate appropriately, eliminate loops, and keep customers happy. 4. Look at every single question every single day (yes, EVERY DAY). Every morning Robb looks at every Fin response and I look at every Delphi response. If they aren’t as good as they could possibly be, we either revise the response, or Robb creates a support doc to properly handle the question. 5. Make sure you have FAQs, Troubleshooting, and Changelogs. FAQs are an AI’s dream. Bonus points if you create FAQ’s written exactly how your customers ask the question. We have a main FAQ, and FAQs for each sub section of our support docs. Detailed troubleshooting gives the AI the ability to handle technical questions. Fin can solve 95% of script install issues because of our Troubleshooting section. Changelogs allow the AI to stay on top of what’s changed in the app to give context to questins about features and UI as it changes. 6. Measure your AI’s performance and keep it improving. When we started using Fin over 1y ago, we were at 25% positive resolutions. Now we’re above 70%. You can actively monitor positive resolutions, sentiment, and CSAT to make sure your AI keeps improving and delivering your customers an increasingly positive experience. TAKEAWAY: Every Founder wants to replace entire teams with AI. But nobody wants to do the actual work to make it happen. Everybody expects to flip a switch and have perfect customer service. The reality? You need to treat your AI like your best employee. Train it daily. Give it the resources it needs. Hold it accountable for results. Here’s the truth that the LinkedIn clickbait won't tell you… The KEY to successfully running entire business units with AI? Your AI is only as good as the content you feed it. P.S. Want Robb's CustomGPT? We just launched 6-part video series on how RB2B trained its agents well enough to disappear for a week and let AI run the entire business. Access it + get all our AI tools: https://www.rb2b.com/ai

After helping dozens of companies implement AI systems, I've developed a proven 4-step process that actually works. My complete AI implementation process 👇 (From chaos to automated efficiency) Step 1: Map Your Current State Before you even think about AI, understand what you're working with. → Internal Survey: Ask your team about time-consuming tasks, tools they use, and bottlenecks they encounter daily. → One-on-One Interviews: Dive deeper into each bottleneck identified. Record every step of each process. → Time Tracking: Use tools like RescueTime to automatically measure time spent on individual tasks. → Process Documentation: Create flowcharts and analyze where manual work is happening. Important golden rule: Never automate a process until it's fully optimized manually. If your team can't do it properly before automation, the AI won't work either. Step 2: Build Your Foundation AI needs structure, not scattered demands. → Single Source Database: Consolidate your key data into ONE platform. If your team uses 10 different software tools, AI has no chance. → Production Line Model: Think of your business as an assembly line. Each step should be a predictable "stage" in the process. → Clean Your Data: Get all information in one place, break down each step to completion, and minimize redundancies. This foundation work isn't glamorous, but it's what separates successful AI implementations from expensive failures. Step 3: Start Small & Strategic Don't try to automate everything at once. → Identify High-ROI Tasks: Focus on automations that will have the biggest impact: - Data transfers between systems - Client onboarding sequences - Report generation - Follow-up communications → Build One at a Time: Automate the first part of a process before attempting the whole thing. → Test Everything: Thoroughly test inputs and outputs before implementing company-wide. Here's why this works: Too many changes at once overwhelm teams and prevent proper feedback collection. Step 4: Integrate & Iterate The best automation is worthless if no one uses it. → Embed in Existing Workflows: Don't create new processes. Integrate AI into what your team already does daily. → Create Feedback Loops: Your team should use it daily, suggest improvements, and report bugs. → Monitor Performance: Track time saved, error reduction, and team adoption rates. → Scale Gradually: Once one automation is working smoothly, move to the next high-impact area. Most companies want to automate their entire business in weeks. This always fails because: - Teams get overwhelmed - No time for proper feedback - Can't easily identify and fix bottlenecks Here's a better approach: Build WITH your users, not without them. Follow this process, and you'll join the small percentage of companies that actually succeed with AI implementation. Follow me Luke Pierce for more content on automation and AI systems that actually work.

Lets discuss an enterprise use case today : Context Engineering for AI-Powered Customer Support: Real-Time, Governed, Enterprise-Scale. Goal: Enable an AI assistant that provides highly relevant, personalized, and accurate responses to customer queries by leveraging context from multiple enterprise data sources (CRM, order history, product catalog, policies, previous chats). 1)Key Requirements : Multi-modal context ingestion (text, structured, semi-structured, documents, images) Context retrieval in milliseconds for real-time AI responses Governance and compliance layer Continuous context enrichment and ranking Context handoff between AI agent and human support Scalability to millions of queries/day 2) Architecture Overview : Detailed Diagram below 3) High-Level Flow User Query → API Gateway Intent Classification → Context Orchestration Engine Retrieve Context → Vector DB + CRM + Knowledge Base Rank, Filter, Package → Context Envelope Send to LLM → Generate Response Response + Source Links → Delivered to User Feedback → Loop back to improve context retrieval Open to suggestions, or discuss ways to do this differently. #ContextEngineering #AICustomerSupport #EnterpriseAI #GenAI #RAG #LLM 

Checklist: What to Prepare Before Automating 50% of Your Support Tickets AI agents are powerful but if you automate chaos, you just get faster chaos. Before handing over 50% of your support tickets to automation, here is what needs to be in place 👇 1. Enlist Top Repeat Issues Start with the obvious: “Where’s my order?”, “How do I reset my password?”. These are the low-effort, high-impact wins. 2. Identify Support Friction Look for high volume, high effort, or emotional triggers - pull past tickets, tag by intent, not product. That’s where automation makes an impact. 3. Know What Not to Automate Keep humans in the loop for nuanced tasks like refunds, legal issues, or sensitive escalations. Don’t delegate complexity to bots. 4. Set Confidence Thresholds Command it to respond only when your AI is more than 90% sure. Bots guessing will result in broken trust and bad experiences. 5. Build Escalation Paths There should be no dead ends. If the bot cannot help, it should hand off instantly to a human agent seamlessly. 6. Use Real Data, Not Assumptions Use real customer conversations, not made-up examples. Train on real tickets, as the answers are already in your inbox. 7. Involve Your Team Your human agents know what is slowing them down. So, use that input to guide automation priorities. 8. Match the Tool to the Task Not everything needs AI. Use decision trees for simple queries, backend bots for lookups, and AI agents for multi-step workflows. Prepare well, and you are not just saving costs instead unlocking better CX. What is one support task you can’t wait to automate? #CustomerSupport #AIChatbots #SupportAutomation #SaaS #VoiceAgents #ArtificialIntelligence #AI

So, you want to introduce AI into your contact center without losing the human touch. Our first piece of advice – make it easy to talk to a human. But ... how easy? On your IVR menu or your chatbot, make sure you have an option that allows customers to connect directly to an agent. In your self-service portal, make your contact options so obvious they can’t be missed. HOWEVER: This doesn’t mean you always need to make human support the first option. In our experience working with dozens of growing brands across ecommerce, gaming, SaaS, and other industries, we’ve found that you don’t need to undercut your self-service and automated support channels by always making human support the default. We recently optimized a chatbot for our client Embark Veterinary. One change: we took the option to contact a human off of the first menu. Embark’s deflection rate increased from 75% to 96%, while CSAT remained at 97%. Wealthsimple is another good real-world example of this. Their chatbot (powered by Ada) uses a combination of a decision-tree and generative AI. When a customer indicates they have a question, the bot instantly does two things: ⭐ Confirms if humans are available to help and what their hours are  ⭐ Encourages the customer to give the bot a shot If the customer still wants to talk to a human, the bot tells them how to get human help, including sharing the current average wait time for human live chat support. Want to learn more about optimizing your AI for customer support? Follow me for actionable tips and real-world examples.