The biggest mistake in scaling is hiring a VA to do a task that a Python script or a Make.com scenario could do for $20/month. Humans are for strategy and empathy. AI is for repetition and speed. If you’re hiring a person to copy-paste data, you aren't a CEO—you’re a supervisor for a slow robot. Drop a comment: What’s one task you’re still doing manually that you know an automation could kill today?

It’s tempting to jump straight into make..com, Zapier, or building an AI Agent the second we have a new idea. We want the "robot" to do the work so we don't have to. But I’ve learned the hard way: Automating a broken process just makes a bigger mess, faster. 📉 Before I touch a single line of code or build a workflow, I follow a simple 3-step rule: 1. Do it manually 5 times: This helps me find the "hidden" steps I didn't realize were there. 2. Document the "Why": Why am I doing this step? Is it actually necessary for the result? 3. Simplify first: If a human can do it in 3 steps instead of 10, the AI will be 10x more reliable. AI is an incredible multiplier, but it only multiplies what you give it. Give it a solid, manual SOP (Standard Operating Procedure), and it will work wonders. Give it chaos, and it will give you a headache. Take the time to "clean the pipes" manually first. Your future self (and your API budget) will thank you! Question for the group: What’s one task you tried to automate too early, only to realize the manual process was actually the problem?

I’m seeing way more success building "Agentic Workflows" (where the AI actually executes 5-10 steps in a row) rather than just a simple "Q&A" bot. The $10k+ deals right now aren't in "cool tech"—they're in building "virtual coworkers" that handle the boring stuff from start to finish. Question: What’s the most complex "multi-step" workflow you’ve built recently that actually stayed stable? I’m curious where the breaking points are for you guys.

Most people in the AI space are selling glorified wrappers. They build a bot, hand over the API key, and hope for the best. At Webbility, we’ve realized that the bot is only 20% of the value. The real money—and the real retention—is in the Data Infrastructure. Here is the 3-step framework we use for our AI builds: 1. The Validation Layer: Before the AI even "speaks," we run the raw data through a logic gate to ensure it’s high-signal. If the data is trash, the AI output will be trash. 2. The RAG Architecture: We aren't just "feeding it PDFs." We’re building dynamic Knowledge Bases that update in real-time based on client activity. 3. The Human-in-the-Loop (HITL) Trigger: We build "Panic Buttons" into our automations. If the AI confidence score drops below 85%, it pings a human instead of hallucinating a wrong answer to a client. The Result? Systems that don't just "chat," but actually handle fulfillment, lead qualification, and data entry without babysitting. Question for the builders: Are you guys focusing more on the interface (the bot) or the infrastructure (the backend logic) right now?

Today no post No knowledge No story Just One Question: “If AI could Automate one thing, what would it be?”