Implementing AI for 24/7 Customer Support Availability

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

Wizr Cx platform provides advanced and enterprise-grade AI agents for customer support. A few key observations deploying these AI agents live with some of our customers.  - Use Case Selection is Important With one of our enterprise clients in the automotive space, we started by AI agents for automating dealer support. The focus was on streamlining inquiries regarding parts availability and service scheduling. By choosing these high-volume tasks, we saw a 29% reduction in response times within 2 months. Selecting well-defined, impactful areas for automation drives early momentum, tangible ROI and better acceptance.  - AI-Augmented, Not AI-Only For a SaaS company handling L1 and L2 support, we found that the most effective model was one where AI agents handled the initial triage and common troubleshooting. AI agents deflected routine inquiries while human agents took over more complex, technical issues at L2. This hybrid approach resulted in a 41% improvement in case resolution times without sacrificing the personalized touch customers value.  - Continuous Tuning for Business Changes is Critical During a major software release for a SaaS client, AI agents struggled with new feature-related queries that weren’t yet part of the existing knowledge base. After the client used our AIOps services to update their knowledge base with release-specific documentation and retrained the AI agents on new workflows, accuracy in handling release-related questions improved by 72%, restoring high effectiveness.  - Agent Training is Just as Important as AI Training In the software industry, rolling out AI wasn't friction-free. Some support agents were initially hesitant to trust AI assistants. By co-training agents and demonstrating how AI-generated solutions could enhance their work, adoption rates soared to 92%, creating a smoother collaboration between AI assistants and human agents, and resulting in significantly higher solve rates. Would love your thoughts on what you are seeing in similar real-life implementations. #AIAgents #CustomerSupport #Enterprise Sirish Kosaraju Srinivas K

Financial services is turning customer service into a 24/7 AI front door. Leaders expect 20% lower cost to serve and response times improving by more than 20%. Here’s what we learned reviewing Roland Berger’s new FS report: ✅AI is moving from pilots to production. By 2027, 92% of leaders expect AI to be very important in service. ✅The outcomes they are targeting are clear: 16% higher process efficiency, 12% NPS lift, and a 12% reduction in service headcount while preserving quality. ✅The tech stack is ready. The gap is operational - clean data, unified systems, and human-in-the-loop guardrails. Main Street takeaway: community banks, credit unions, and local agencies can win on speed and trust at the same time. You do not need a massive transformation. You need one well-chosen workflow and a clean handoff to your team. Small businesses can implement this by: 1️⃣Start with one high volume queue, password resets, balance checks, claim or payment status. 2️⃣Connect the context, core system, CRM, phone, and knowledge base so AI can read status and show its work. 3️⃣Set guardrails, auditable logs, escalation paths, and clear handoff to a person for edge cases. 4️⃣Measure outcomes, cost to serve, first contact resolution, handle time, and CSAT or NPS. 5️⃣Train people, not just models, teach agents to review, correct, and coach the AI. The results speak for themselves: when you unify data and pilot beside your agents, customers get faster answers and fewer call backs, and your team focuses on the conversations that actually need a human. If you run a local bank or insurance office, this is the moment to pick one queue and prove the ROI in 30 days. Keep it simple. Track the numbers. Then scale. P.S. This is Report 4 of 7 in my Industry AI series this week. What industry should I break down next?