Chapter 1: What did the speaker learn from spending $2.6 million on software?
I spent $2.6 million on software a couple years ago trying to build out everything that AI can do in a day now. I think most agency owners are nervous right now. They're like, AI sucks. It's not good enough, which is true. However, at this very moment, I do think AI is ready officially now.
So Steven, for those who are not familiar with you and your background, tell everybody real quick a little bit about you and what you've built.
Yeah.
Chapter 2: Why does the speaker believe AI is finally ready for agencies in 2026?
So I'm Steven Pope. I own my Amazon guy. We are a 420 person agency that helps Amazon sellers close to 20 mil in annual revenue. We are currently working on a pivot towards agentic AI. We built out a couple other projects we can talk about today and automating as much as we can and excited to see what AI does for us and how to run with an agency.
That's an interesting question that I think a lot of people have is like, OK, what is AI really going to do and not just the hype? Because you hear about all the hype about like, oh, this is going to revolutionize and all this stuff.
Chapter 3: How can agencies turn their systems into AI products?
But like, how do you actually practically use it?
First, let's set the stage, right? Like AI is like teenage sex. Everybody thinks everybody else is doing it. Nobody really is. And if they are, they're doing it wrong. Right, and so I'm here to say though today that I've changed my opinion in the last 60 days, and I think AI is finally ready. And we've seen Nana Banana was huge for design work.
Claude with programming has come from, like so far it's ridiculous. I spent $2.6 million on software a couple of years ago trying to build out everything that AI can do in a day now. Like literally 2.6 million for a shitty product I didn't launch. that my COO with a background in IT coded an equivalent software in a weekend that we're now creating with confidential.law.
Chapter 4: What roles in agencies are being replaced by AI?
That's confidential with a K. And we're basically taking our agency model and then serving it up to lawyers. And this is like where we're going to pivot next is to create... models that have worked at our agency, replicate them into other jurisdictions, other platforms, other, you know, clientele services. Cause the money's kind of drying up with Amazon sellers.
Truth be told, like we've, you know, we've lost, I don't know, 800,000 sellers or something crazy high.
If you believe the stats, I think Amazon is a hole with their seller community.
Yeah. I think most agency owners are nervous right now. And they're like, AI sucks. It's not good enough, which is true.
Chapter 5: How can AI predict client churn effectively?
However, it's trending vastly different from that at this very moment. I do think AI is ready officially now. I've been shitting all over AI for two years, personally. I think there's an AI singularity that's going to happen in the next two years. where we can't predict anything past that event. And it's a huge black Swan, which is like peacocking its way over here.
Like we know it's coming, but we can't predict it. And it's still highly improbable, but highly impactful. So I think that's where we are today. And I think agency owners, have to be transitioning now, even though it doesn't feel like it's quite ready. But it actually is like officially in 2026, in my opinion.
That's a good point, because, you know, just a couple of years ago, it was like, oh, well, if the hands like that'll tell you if it's an AI picture or not. Now you can't just look at hands because the hands have gotten better. So AI is getting better in general. How else are you using AI, whether in my Amazon guy? And then we'll get into some of your pivots here in a moment.
When ChatGPT originally came out, we fired our copy division.
Chapter 6: What data-driven decisions can agencies make using AI?
We felt like AI could outright faster, better, less errors. That was true then. We were very early on that maneuver. It worked out very well for us. Copywriting was an American-based salary, two years. I think it's been, might be three and a half now actually on that. So that was like the first Gen 1 AI maneuver. Design work is a really recent one. The design staff used to be like 55 employees.
I think we're closer to 30 at this moment, maybe a tad lower. We cut 11 roles who couldn't adopt AI or resisted adopting AI. And we foresee that graphic design roles will not necessarily be about the design elements. It'll be about the ability to use the tools.
So you could take a guy like John Aspinall, who has no design background, and he outmaneuvers designers with better results based on tech adoption. I think that's the future of design. And that's going to put a lot of design schools out of business. And I think that's OK. you know, those jobs will transfer some way, somehow. So I think that's a big one.
Another big one was the programming of websites, programming of tech. And this is where we're seeing most of the layoffs in online right now, whether it's Facebook, Google, et cetera.
Chapter 7: How can agencies build tools using AI prompts?
Seeing a lot of challenges from companies that can now out-program with AI. And it's very specifically usually based in cloud. Cloud is being seen as the enterprise tool. We can spin up a website in two hours that's good enough to go. So like, for example, I come up with the idea that we're going to start selling a CRM billing software, et cetera, to lawyers.
I come up with the idea, I go buy the domain, and hours later, we have a website spun up. email system completely done, and then built the back end the next weekend, a couple days later, and enough that people can sign up, use the tool, and we're off to the races. That's millions of dollars in software dev done in a blink of an eye, really.
So I think that's structurally where we've seen some of the biggest adoptions. I'll give you one other example. This is probably the one that will be most interesting to other agency owners, and that is a churn red alert. So I started taking some free calls for people to give AI advice. And in return, I wanted to know what their use cases were.
And so one of the strongest use cases that came out of that was the need to have an amber alert for when a client's going to churn. So you can take a system like Fireflies, which is kind of like AI Gen 1 software, where it can see in isolation, like, hey, if a client uses a keyword,
call a red flag the problem with that gen one software is it can't tie it back to see trends it can't say this call is different than the last 10 calls and that's where we came in and said oh we could we could do that we tie it all together so we tied the entire client's history together in one boat and we record every single client call if you're not recording client calls you're That's insane.
You need to record everything, record every sales call, every client call. This is data galore to how to run your company, marketing, especially ops, et cetera. And then we were able to figure out like with probability indexes, who's going to churn, who needs a touch base from a higher rep in the company and structurally figure that out.
We also ran analysis for how much churn increases under various scenarios, whether it's market scenarios, you know, a transitioning employee issue like a transfer or an employee resignation. So we have all of that data down pat and we know exactly what happens and we can make educated calls. So if we know we got an account manager that comes to us and says, hey, I want to X my salary or I'm gone.
Well, now I can see, well, if you leave, I'm going to lose money. X percent of revenue. Let me just run the math. Oh, that doesn't make sense. Go ahead and leave. No problem. I've got my intern model built out in a way where I can replace you three deep to a seat. No challenge.
I obviously want to retain talent, but that's kind of an outlandish scenario on purpose to show how you can use that data to make those decisions and then store everything. We have a client portal where we've got all of this information, you know, dashboards galore for clients. I
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Chapter 8: What challenges do agencies face with too many good ideas in AI?
So now it's not a question of bad ideas. It's now a question of too many good ones.