When I first started with n8n, I learnt that the easiest way to build complex workflows is by starting with tiny experiments. One of those experiments: 👉 Take an email list, automatically find the LinkedIn profiles, and update the sheet. The workflow: - Pull emails from Google Sheets - Extract name & company - Search for their LinkedIn profile - Write the link back into the sheet Simple, but powerful. 🙌 What excites me is how these little automations stack up. This one can plug into bigger systems for enrichment, scoring, and CRM updates. Start small → scale big. 🚀

Most LinkedIn outreach fails because it feels irrelevant. Buyers today don’t want another templated pitch. - 61% of B2B buyers prefer a rep-free buying experience (Gartner). - 73% avoid suppliers who send irrelevant outreach. - Cold emails get ~5% replies vs LinkedIn DMs at 10%+ (Expandi, 2025). The signal is clear: prospects want context-aware, value-driven conversations. That’s exactly what AI agents deliver. They: ✅ Scan profiles & posts → highlight what really matters ✅ Analyze context → spot job changes, funding news, or pain points ✅ Draft relevant, timely messages & follow-ups Even LinkedIn itself is investing in AI: its Account Prioritization Engine boosted renewal bookings by 8% in internal tests. This isn’t about replacing humans. It’s about freeing you from repetitive research so you can focus on the conversations that matter. I’m currently building a LinkedIn lead gen agent that does this end-to-end - monitoring activity, detecting intent, and generating outreach messages you can actually use. 👉 What’s your biggest challenge in LinkedIn outreach - finding prospects, personalizing, or following up?

Tired of answering the same customer questions again and again? Here’s a simple automation I’m planning to set up with n8n + AI that (if it works) could cut response time by 80%. 👇 1. Capture Queries -> Hook up email, live chat, or CRM tickets to n8n triggers. 2. Classify Requests -> Let AI auto-tag each query → billing, product, or tech. 3. Search Knowledge Base -> Embed your FAQs/docs into a vector DB so answers can be pulled instantly. 4. Generate Answers -> Claude/OpenAI drafts a response that feels human, not templated. 5. Human-in-the-Loop -> If the issue is complex -> route to a support agent. 6. Close the Loop -> Log the ticket, track metrics, and keep improving the workflow. 🔥 The potential results (based on similar setups): - 80% faster replies - 4x more queries per agent - Higher CSAT scores ⚡ Here’s the fun part: I don’t have this automation fully live yet. I’m building it step by step and want to see if AI can really handle 80% of support queries without customers noticing. 👉 If it works, I’ll share the exact workflow template here so you can copy it for your own support system. Would you want me to drop updates (and maybe the workflow) inside this community once it’s ready?