This content was created by Eric Siu. This content was uploaded by Eric Siu. This is a YouTube video titled "The 4 Levels Of AI Marketing..." (published May 7, 2026) in which a single speaker outlines four tiers of AI adoption for marketing: Level 1 — automating existing tasks (reporting, copy, data pulls); Level 2 — using AI as a thinking partner; Level 3 — enabling work that was previously uneconomical (e.g., performance intelligence, large-scale negative keyword mining, site QA, competitor monitoring); and Level 4 — building bespoke, company-specific AI tools where ROI compounds. The speaker references Anthropic’s single growth marketer, examples of practical use cases, and companies like Ramp/Glass building custom institutional AI. The content is instructional/strategic, aimed at marketers and builders considering how to escalate AI integration.
Chapters
This one is from Anthropic's single employee. Remember they had that single growth marketer? So he has mapped out the 4 levels of AI marketing use. So most people sit at level 1, automating what they already do. So level 1, Neil, automate what you already do. So reporting, copy, data pulls, like using it at a pretty rudimentary level. That's level 1. Yeah. Level 2 is you use AI as a thinking partner where it's better than you. Okay? So you might ask about tax situations or tax laws. I might ask about those as well, right? Or I might ask about, you know, certain implications around doing work. Level 3 is do work that was below the ROI threshold before. So an example of that might be, Performance intelligence, which I just mentioned. Like it's too much work to have to say, oh, Neil, how did you? What questions do you ask? How'd you work? How'd you use your token? Say da da da da da. You couldn't do that before. This work is now all doable, right? A lot of these edge case scenarios you can do now. Level four: build custom tools only you would ever build. Okay, so level three is work that never existed before. So stuff nobody did because the manual cost was never worth it. So mining negative keywords across every ad group, checking your full site for broken links daily. Same logic applies to content research, QA, competitor monitoring, and work that existed in theory but nobody had the hours for, which is kind of how we're using our. Level 4, which we just talked about, is where the ROI compounds. So there are hundreds of AI marketing skills and plugins floating around GitHub right now. Most of them work in theory but fall apart in practice because they are built for general use cases, not your case. Your business has specific data, specific workflows, specific edge cases that no generic tool will ever cover. The people building custom tools around their own problems are the ones pulling ahead, which is similar to what Ramp is doing with Glass. They don't want to use any off-the-shelf software. They're building it for their solution. Their single brain version is that we have our own single brain version and then we're working on selling that. But I think every company has their own institutional AI that they need to have.
