Enhancing Customer Support Scalability With AI

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.

I have been working with AI in customer support for a while now. And lately, one thing is becoming clear. This space is getting crowded. Every vendor claims their AI is the magic wand. Just plug it in, and your support problems disappear. But the reality is different. AI isn’t magic. It’s a strategy. It has to be planned, adapted, and rolled out based on: 🔹 Your goals 🔹 Your current challenges 🔹 And your team’s capacity Most support leaders we speak with aren’t confused about the tech. They are confused about where to use it. That’s the real challenge. So we created a simple matrix to help teams make better AI decisions. It’s built on just two questions: 1. What’s the risk if AI gets this wrong 2. How complex is the task When you map support work using this lens, things get clearer: - Use AI fully for low risk, repetitive tasks like tagging, triaging, or summarising. - Use AI as a helper for pattern based tasks like routing, recommending actions, or drafting replies. - Keep humans in control for high risk, complex issues like escalations, complaints, or anything tied to revenue.   And here’s the other mindset shift: Don’t think of support AI as one giant bot. Think of it as a system of specialised agents: 🔹 Analyzers – Understand queries, profiles, logs 🔹 Orchestrators – Manage workflows, routing 🔹 Reasoners – Diagnose problems 🔹 Recommenders – Suggest next steps 🔹 Responders – Write or send replies Each agent plays a specific role, just like your support team does. Done right, AI doesn’t replace humans. It supports them, speeds them up, and helps them focus where it matters most. This approach is also being recognised by the front-runners in the space. At a recent ServiceNow event I attended, many speakers echoed the same thought: AI is not one size fits all. It must be tailored to each organisation’s structure, systems, and bandwidth.   Let’s stop using AI for the sake of it. Let’s start using it where it actually makes a difference.   If you are building or evaluating AI for support and want to walk through the matrix, Feel free to drop me a message.  Always happy to exchange notes.

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

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

Most CS teams do a great job when there’s a named CSM working one-on-one with a customer. That’s not the issue. The issue is the long tail. Thousands of smaller accounts—below a certain threshold—where we just don’t have the human resources to give them the attention they deserve. We’ve tried webinars, drip programs, reactive support… but it hasn’t really solved the problem. That’s where AI gets interesting. With agentic AI, we can finally scale how we prioritize and how we act. → AI agents can run in the background, scan every account, and flag which ones are most at risk. → Then, they go a step further, generating personalized content, emails, even video messages that can be sent automatically. → What used to take hours per account now takes minutes. That gives us the ability to actually touch EVERY customer…not just the top ones. We couldn’t do this before. We didn’t have the time, the tools, or the scale. I know some people worry that AI will replace the human relationship. But I don’t see it that way. I see it as a way to extend those relationships. To reach customers we’ve never had the bandwidth to support. To catch risks before they spiral. To show up for more people…without losing what makes CS work. The real risk in Customer Success isn’t just churn. It’s being invisible. #CustomerSuccess #AIinCS #AgenticAI #RetentionStrategy #Leadership