AI for Marketing Agencies: How to Profit $1.5M With 40 Clients

**Jordan Ross** (0:00)
Welcome to How to Build an 8 Figure Agency. My name is Jordan Ross, and I'm your host. And over the course of this journey, we're going to be interviewing 8 Figure Agency owners and going through the systems that I've seen work and we've implemented into our clients' businesses that have helped our clients add a half a billion dollars in annual recurring revenue over the last handful of years. I myself have already hit the 8 figures in annual recurring revenue, and when I got there, I wasn't happy. I was stressed, I was dissatisfied, and I didn't like the people I was working with. So I clicked the restart button and went from 12 million in annual portfolio revenue back down to the mid-seven figure level to a climb back again. And on this journey, I'm going to be documenting my process on how I'm getting backstage figures and bringing you expert industry leaders who will tell you everything they did to get there as well. Thanks for tuning in and enjoy the episode.
More than 50 or 60 percent of the marketing agencies, advertising agencies, SEO agencies that you're competing against are AI-forward or they're working towards being AI-forward. They're building skills, they have their MCP connections, they are working on constraints, they're just stacking. And you're either part of this camp or not and you're falling behind. So I wanted to give you guys an update as what I see as someone that has to be at the helm of this. Like I don't have a choice because we are competing in the space for delivery. And I wanted to chat on the state of AI for marketing agencies and just some insights that I've had. So if you're a listener of the pod, you're coming back here. What up? Thank you for coming and let's get going. So first and foremost, if you're new to the pod and you don't know me, I've been running 8 Figure Agencies since 2019 In 2024, we had serviced over 1,000 agencies as an operations management consulting firm. In 2025, Q1, we started an engineering AI development division.
We were fully rebranded as the world's first AI development and consulting firm in 2025
And ultimately, what ended up happening is the market tech has gone crazy, crazy. This shit's nuts. And I think if you're not staying on top of this, it's part of the reason why I'm chatting in today's. I want to actually tell you what I'm seeing right now. So here's my POV.
If you have not started yet for AI development or just going AI forward, the first thing I would say is just as a business owner or operator, you need to find the time. Now, you actually don't have to be the one studying it. It's actually insanely time-consuming.
I think I'm going on four months or between three to four months of every week I do training with one of the engineers who teaches me on building.
I'm up every night. I'm watching YouTube every single night. I'm consuming so much because I have to be. Well, last year I didn't have to be technical. This year, you could use a terminal, a cod code and you could build. So what I'm trying to learn every week is what is being updated. So let's start with that.
Agents are now leveraging things called harness and loops. I'm going to give you my non-technical perspective, and this is very relevant.
Loops are closed. They might be open or closed. Who knows by my accounts, sorry. But these loops are ways for agents to code for an ongoing basis and develop. So these loops are primarily for development agents, but we are, as marketing engineers, we care about business processes and agents running business processes. These agents are operating now 12 to 15 to 30 hours consistently with no human intervention. It's actually pretty crazy. So this is a new thing.
You did not see videos about this online. This has been happening this year, but you didn't see a lot of conversations around it. So Q2 really came relevant. So we actually had someone on the podcast, David Siffrey, who actually showed me one of his loops. And what that is, is you build an agent that will try to go do a task. And then that agent will go do it and it will pass off to another agent. And then it will give it a handoff notes. Here's what I built. That agent will then try to take the task and do the next thing, and it will deploy it. And then from that deployment, it will see, did we accomplish the task? What can we do better? So in Dave Siffrey's example, if you didn't listen to that podcast, go check it out. Dave shared how he does an end-of-day report, and at the end of every day, his agents will review, like, what's my goal? Did I hit it? How could I get better? And from, how could I get better notes? It leaves notes. So the next time an agent logs in its context, it's like, here's what was built, here's what I did, here's what I need to do better. It will do the next thing the next day. And when you consider where this is going, like this is how you have autonomous AI companies, right? Like you have a state objective. I want to go from a million dollars in EBITDA to five million dollars in EBITDA.