If you call everything “agentic,” you’ll never get ROI from AI.

2026-01-01

If you call everything “agentic,” you’ll never get ROI from AI. Right now, the word is being used for anything that touches an LLM:

Chatbots
Zapier flows
RAG systems And that confusion is exactly why many “AI projects” stall after the demo. Also: don’t believe the hype in Instagram Reels. Teenagers aren’t building real businesses with AI in 90 seconds and “automating onboarding” between a margarita break. Demos are easy. Reliable systems in production are not. Here’s the mental model I use to audit what a company is actually building. What is NOT an AI agent? 1) LLMs (ChatGPT, Claude, etc.) You ask, they answer. They don’t decide, run tools, track goals, or own outcomes. They’re interfaces, not workers. 2) Automations (Zapier, Make, n8n, RPA) If X, do Y, then Z. They follow scripts, break on edge cases, and never change the plan. They’re assembly lines, not decision-makers. 3) RAG systems They retrieve information and pass it to an LLM. Great for support, docs, and knowledge access. But they don’t plan, act, or own a workflow. They’re memory, not autonomy. So what IS an AI agent? An AI agent is a digital worker that does four things: 1) Has a goal (a business outcome, not a prompt) 2) Can plan (sequence steps, choose tools, request missing data) 3) Can act (call APIs, update systems, trigger workflows) 4) Can learn (use feedback and history to improve over time) That’s the difference between automation and autonomy. Clients don’t pay for “AI.” They pay for outcomes: revenue, cost reduction, throughput, speed. So the next time you hear “agentic,” ask: Which of the four boxes does it actually check? If you’re building or selling automations, comment “AUTOMATIONS” and I’ll send you the link to a community I like. hashtag # ai hashtag # agents hashtag # automation hashtag # product hashtag # saas hashtag # goToMarket