How to Automate Customer Interactions

Small teams can’t do customer service 24/7. AI can. And it already is. You can’t reply to every ticket. But your customers still expect answers. All day. All night. All channels. Big brands figured this out. Take Target as an example: - AI handles returns, FAQs, and tracking. - It cuts wait times and costs. - Customers stay happy. Teams stay sane. This works because Gen AI bots don’t sleep. ☑︎ They handle the repeat questions. ☑︎ Route the tricky ones. ☑︎ Respond in seconds, not shifts. You can do this too. No DevOps. No bloated budget. Small e-comm shop? Use ChatGPT via ChatNode. Train it on what you already have: - FAQs - Tracking info - Return policy - Internal guides The bot learns fast. Launches faster. Plugs into your CRM. Runs 24/7. And it doesn’t burn out. You stop replying at 10PM. AI answers while you sleep. ☑︎ Customers get support. ☑︎ You get your time back. ☑︎ Focus shifts to growth, not inbox triage. Start small. Scale smart. - Connect ChatNode - Upload key docs - Go live. Improve as you grow. The automation edge is already here. If you’re not using it, your competitors are. Found this helpful? Follow Arturo and repost.

🌶️ 🙅♂️ Non-spicy take: 100% of product teams need Continuous Customer Discovery. Everyone in product knows this problem: • You want to talk to customers. • So, you spend an hour emailing 100 of them. • If you're lucky, maybe 2-3 book a call, 3-4 days away. • After no-shows, a week later, you might have spoken to 1-2 customers. Enter: Continuous Customer Discovery. How we do it at Superhuman: • New customers who send 30+ emails receive a Typeform with questions including NPS • If the customer is NPS 8+, we ask: "Do you want a sneak peek into new features and a say in our product development? As a thank you, you'll receive a $25 gift card!" • If yes, customers can book a 30min call via Calendly with someone from our Product & Design team • Zapier notifies a Slack channel for every booked call • We use Tremendous to streamline gift cards • We share learnings in Slack and tag teammates on interesting topics The result? Customers show up on PM and Designer calendars every week without anyone lifting a finger 😮💨 The impact is palpable. PMs and Designers are: 💡 Closer to customers → no more guessing what to build based on customer support emails, Twitter, or worse, the internal echo chamber ⚡️ Energized and confident → imagine designing something one morning and within hours getting candid feedback from real, paying customers! 💜 Building meaningful relationships → each call is an opportunity to delight You obviously don't need every step. The core is just automating inviting customers to a call. But a few nice details from our system: • Filtering on *new* customers means we only ask customers once, and we ask when they are highest intent. • Filtering on *high NPS* customers means we are more likely to have deep product conversations, versus, say, firefighting support issues. • Zapier lets the admin (me) easily monitor system imbalances, otherwise is just fun. • Trivial to adjust. Reaching more customers, extending the rotation, adjusting availability — all just a few clicks. • Learnings channel is incredibly low friction, building a culture of constant learning. The wild part is that it only took a few hours to create, but years to set up. I'd wager that 95%+ of product teams don't have this, but 100% of them should. Credit to Oji Udezue who describes this here [https://lnkd.in/gxprJi9t], and Lauren who helped set it up at Calendly and Superhuman 👏 #startups #productmanagement #growth

While everyone's talking about AI replacing human agents, something more interesting is happening: technology and humans are forming a powerful partnership that's transforming customer experience. AI isn't stealing your agents' jobs – it's making them superheroes. At Callzilla - The Quality-First Contact Center, we've been implementing Agent Assist tools that give agents real-time support during customer interactions. The results speak for themselves: • Agent gets asked an impossible question? AI whispers the answer • Customer mentions an uncommon tech issue? Relevant articles appear automatically • Agent struggling to categorize the call? AI suggests the perfect reason code • About to make a mistake? AI catches it before it happens This creates a 'best of both worlds' scenario where technology handles routine tasks while agents focus on what humans do best: • empathy • genuine connection • creative problem-solving When to Automate vs. When to Humanize: • Let AI Handle: Repetitive tasks, basic info lookups, initial problem identification • Keep It Human: Complex problems, emotional situations, VIP customers who expect the red carpet treatment Pro tip: Give customers choice. Instead of forcing one path, ask: "We can have an agent available in 5 minutes, or you can chat with our AI assistant now who handles most issues. What works better for you?" Your tech should be: • Serving up answers faster than expected • Reducing agent cognitive load, not adding to it • Supporting natural conversation, not rigid scripts • Suggesting solutions, not just documenting problems AI doesn't replace your agents – it creates 'super agents' who resolve issues faster, with less effort, and greater accuracy. It's not about choosing between humans OR technology. It's about humans AND technology working together. The companies seeing the best results have figured out this perfect pairing – and their customers can't get enough. What's your experience with human-AI partnerships in CX?

Today, we're publishing the AI Agent Engine! 🙌 It's a distillation of our learnings from many successful deployments of AI agents at enterprises. Ultimately, this is what's required for a successful implementation of AI agents. Of course, this is specific to our space (customer service & experience), but the themes will carry over to any vertical. 1. First, you have the "AI agent", defined as a software system that can autonomously do the work of a human agent, such as looking up data, taking actions, making complex decisions, and writing personalized responses. This is the holy grail that everyone wants to get to. 2. Around it, the rest of the engine is designed to reinforce the AI agent and allow it to continuously improve. This starts with a mechanism (i.e. "Routing") that determines when the conversation should be escalated to a human in the loop. This is key because it allows you to roll out your AI agent incrementally. 3. Next, you have the AI tooling for your human agent to use that automates away mundane tasks, like drafting an answer, finding relevant information, polishing tone, etc. We call this "Agent Assist", and it's akin to a copilot. 4. Then, the conversations all feed into a central data platform, our "Admin Dashboard", that allows the leaders of the team to use LLMs to analyze the conversations. This will surface themes, trends, and anomalies in the data easily. It'll also identify gaps in your knowledge, for example, and proactively tell you how to fix them. 5. Finally, you need a way for human staff to "QA" the conversations to constantly give feedback. We've built this directly into the product. These components form the AI Agent Engine, a helpful framework for thinking about AI implementations. The full post written by Bihan Jiang, Kaylee George, and Cynthia Chen is below! 👇

With 4,000 stars on GitHub, this YC-backed startup is making waves with an open-source framework that automates operational workflows with LLM-powered agents. Superagent empowers developers to enhance their applications with robust LLM-powered AI assistants. Imagine a customer support workflow. With Superagent, an agent, could access various data sources like FAQs, product manuals, and customer data in databases to provide accurate and contextually relevant responses. The memory feature ensures the conversation context is maintained, enhancing customer experience. For inquiries requiring more sophisticated handling, the workflow feature can route the conversation to human agents (using the "hand-off" feature) or escalate it through a sequence of increasingly sophisticated AI agents. This system can significantly reduce response times, improve customer satisfaction, and decrease operational costs. Highlights: (1) Ingest various data sources, including PDFs, CSVs, Airtable, and YouTube videos (2) Execute different actions, from searching on Bing, to generating speech from text, calling a custom function, or hitting a Zappier endpoint (3) Features different generative models such as OpenAI’s GPT, Mixtral, or Stable Diffusion (4) Integrates with known vector bases such as Pinecone, Weaviate, and Supabase (5) Supports Langfuse and LangSmith for LLM observability (cost, latency, etc.) It is fully open-source and has Python + Node/Typescript SDKs. Superagent GitHub repo https://lnkd.in/gKrMq-sQ I recently wrote about the rise of autonomous agents and how packages like Superagent facilitate such a change https://lnkd.in/gNsKaeA4