You’re Doing AI Automation Wrong (Here’s How to Fix It)

If you want to make money with AI automation, whether that's selling services to businesses or implementing systems into your own, I'm going to show you exactly how. Because the truth is, if you're struggling to take advantage of the AI space, you're probably just approaching it with a suboptimal strategy. I know this because I've been on both sides. I've sold AI workflows for thousands as a beginner freelancer. And I've scaled my own agency, True Horizon, to $2.5 million this year by building AI systems that make the company run smoother. So, in this video, I'll break down four non-obvious lessons. And these are the same lessons that all the people monetizing AI understand that you may not. So I don't want to waste any time. Let's get right into the video. The first lesson is AI automation isn't actually about automation. It's about leverage. Now that may sound just like a wording change, but it changes everything about how you approach the AI space. When people first heard the term AI automation, they thought it meant one thing. We're going to build agents and replace every human. And to be fair, that idea exploded back in 2022. Remember tools like AutoGPT? Everyone was hyping up these giant end-to-end agents that were supposed to do every step of your workflow for you. And on social media, it kind of looked that way. You'd see these demos of an AI agent booking meetings, sending emails, building websites, all with zero human input. But in the real world, those types of systems did not hold up. They broke. They bottlenecked. And half the time, they created more problems than they solved. So businesses learned pretty quickly full automation is not the goal. And that was the turning point. The people who stuck around started to realize that AI automation was never really about automation. It's about leverage. And here's what I mean by leverage. Rather than focusing on just building for the sake of building, you need to learn and implement the golden AI ratio. So here's how that looks. Maybe you automate the first 60% of a process, the boring and repetitive stuff. Then you use AI to assist a human with the next 30%, things that need judgment or context. And the last 10% you can leave that fully manual because sometimes a human just does it better. That mix is what actually makes businesses faster, leaner, and more profitable. So the real question isn't how do I automate everything. It's where does AI give me the most leverage and what ratio of AI to human do I need to maximize that. Once again, this applies to you whether you're selling AI or just implementing it into your own business. So, stop thinking about how to automate with AI and start thinking about how to leverage it. Your only job is to think about that ratio and applying it to every problem that you come across. So, let's take something simple like following up with leads. If you're a business owner, you probably want the system to make your life easier. If you're someone selling AI services, this is exactly the kind of process that you would build for a client. So the first 60% the boring repetitive stuff like logging data into a CRM or scheduling calls, that's where you can fully automate and you may not even need AI there. The next 30% that's where you need a little bit more judgment like writing personalized outreach messages. That's where you use AI to help out. Let the AI draft it and then a human can tweak it and send it off. And then you can leave that last 10% as a human manual process because when it comes to closing the deal or adding a personal touch, no AI is beating a real person. But once again, you could use the automation and the AI to help with other things in that process up front, like research. And that's leverage. You'll notice that this applies everywhere. In finance, you might automate 60% of reporting, have AI generate draft summaries for the next 30% and still need a human sign off for the last 10. In marketing, you can automate scheduling posts, let AI draft, copy, or captions, and then keep the final creative touch human so it feels authentic. Even in operations, say onboarding new hires, AI can handle the paperwork, prep the training docs, and then leave the actual culture building to a human. The ratio is flexible, but the principle stays the same. Stop thinking about full automation and start thinking about where AI gives you the most leverage. Because once again, the most important part is communicating the value that a system adds. And if a system automates 70% of a 10-hour process, you're still saving 7 hours every single time that that process happens. That's huge value, and that's leverage. All right, moving on to lesson number two, which is that most people in AI fail because they're trying to do too much. When you're new, your instinct is to go wide. Learn every new shiny tool. Build every type of workflow. Say yes to every client. On the surface, that feels like the right move because you think you're going to have more skills, more reach, and so in turn, more chances to win. But the truth is, doing too much actually kills your leverage. Take tools for example. Most people hop from one to the next. End it in one week, lovable the next. Then onto some new agent that's going viral that month. What happens is they end up knowing a little bit about tons of different things, but not enough to actually solve real problems. So for me, I went the opposite way. I picked up and it early and I stuck with it. I wasn't chasing every new release. I was still paying attention to what was going on in the market, but I went deep with the tool that I chose. And that's what gave me an edge because I wasn't the guy who knows some AI tools. I became the end guy. That depth built authority. And that authority brought me more clients. I'm sure you've all heard the saying, you'd rather go an inch wide and a mile deep than a mile wide and an inch deep. What you're trying to find is diamonds. And then once you're that deep, you can start to br it out and you'll find more and more rather than starting all the way up at the surface level where there's not going to be any diamonds anyways, no matter how wide you go. So anyways, all of that time that I saved in just learning one tool was spent learning about real business problems because you only need so much knowledge about building workflows. Once you've got a good base, you can spend most of your time on the application of what you can build. So rather than focusing on learning a new tool every single week, I focused on learning about business owners and their specific problems. And because I was doing that, I was able to land clients while people with the same amount of experience kept hopping from tool to tool. And it's not just about tools. Distractions in general is what causes people to fail when they're always hopping around. So the same thing happens with clients. Most beginners think, "If I work with anyone, that's way more people. That's a bigger market, so I'm going to make more money." But that actually spreads you too thin. One week it's gyms, then it's ecom brands, then it's real estate, then it's dental clinics. Where does it stop? You never build depth in one niche. So, you stay a generalist. The people who win, they pick one lane. They solve one specific type of problem, and they double down on it because now they know that exact pain point and problem and solution better than anyone else. And then there's marketing. People think they need to be on every platform, but all they do is waste their time and burn energy. The messaging gets diluted. The content looks like noise. The ones who cut through, pick one platform, go deep, and only expand once they've built real authority. Those are the ones who win. So the takeaway here is simple. Stop trying to do everything. Pick one tool, one type of client, one main platform, and go deep. Because in AI, leverage comes from depth, not from doing too much. And that's where you find diamonds, too, when you go deep. All right, moving on to the third lesson, which is that complexity kills and simplicity scales. I know it's a mouthful, but hear me out. When people first get into AI, they love building these big complex systems. 10 different agents all talking to each other, 15 steps in that workflow, all stacked together like a Jenga tower. And yeah, it can grab attention and it looks impressive. You could post a demo online and people will think it's advanced. But here's the problem. In the real world, those systems will break. And when a business is relying on them, they don't want advanced. They want reliable. So when they're paying you for an AI solution, they don't care how many nodes or API calls it has. They care if it saves time, makes them money, and doesn't break every other day. And that's where I think most people trip up. They think that creating something cool is what makes their system valuable. But value is in how usable the tool is, not the tool itself. That boring, simple workflow that saves a company 100 hours a month will always be worth more than some flashy agent that looks cool but needs to be babysat. For me, whenever I build systems now, I start by asking myself, what's the simplest possible version of this that still delivers the result? That mindset not only makes things easier to manage, but it also makes the systems way more scalable. I also try to remove as much decision-m and AI in a workflow as I can. And I know exactly what you're probably thinking right now, but the answer is no. Clients will not think you're too basic or choose someone who says their system is better because there's more steps. All they really want to hear about is how their problem is going to be solved. And honestly, myself and some other AI automation content creators out there are probably a little bit to blame here. Because if you went to our channels and you looked at the videos that have the most views, I'll bet that they're the automations that are really flashy, really advanced, or super agentic. But in reality, the amount of clients that come to us and say, "Hey, I saw a video that you did and I love that system." or the templates that we're actually repurposing from my YouTube videos to build for a client are always the ones that have less views. They're more robust and probably what the majority of the world would consider boring. But in automation, boring is beautiful. Predictability is your best friend. The actionable takeaway here is stop overbuilding. Don't chase complexity for the sake of looking smart or cool. Build simple, stable systems that create obvious value because that's what businesses actually pay for. Moving on now to the final lesson of the video, which is process over prompts. Most beginners obsess over the wrong thing. They'll spend days tweaking prompts, stacking tools, and building giant workflows that look impressive but don't actually solve problems. And the truth is, prompts and tools are easy to change. The real challenge and the real opportunity is understanding the process that they're meant to actually improve. You could have the smartest workflow in the world, but if it's plugged into a broken process, it's just going to make that process break faster or more. It's like a doctor trying to prescribe medicine without understanding how the patients immune system works or what they're allergic to. A certain medicine may work great for one person, but it could do nothing for this other particular person, or could even make that person worse. AI works in a similar way. If you don't actually understand the business process at its core first, then your system will be misaligned from day one. It might look great in a demo, but in production, it'll crash into all the messy real world details that you didn't account for upfront. And here's one secret that sometimes might hurt my job. But a lot of the times when you actually put in the effort to understand the core problem that a business is facing, you may not even need a custom AI solution. You may just need a better CRM or a cleaner CRM or a SAS product that costs 20 bucks a month. You don't always need to force AI or a custom agent into everything. That's why your job isn't to build, it's to study. Study how the process currently works, what parts are repetitive, what parts require judgment, and where the human touch actually adds value. Then, and only then, design a solution that complements it. For example, in customer support, AI can draft 80% of replies, but the human still needs to handle exceptions and empathy. In sales, AI can automate CRM updates, prep follow-ups, but the human still closes. See the pattern here? AI should fit into the process, not fight against it. kind of like the leverage thing that we talked about earlier. And here's the second big takeaway inside this lesson, which is to stop chasing perfection. Nothing needs to or will be perfect right away. That's why you iterate, test, go back and forth with your client. You don't know what you don't know until something's live and starts getting exposure to more scenarios. That's why we have these terms PC, proof of concept, or MVP, minimum viable product. It means that you want to get something out there, get it working, get it adding value, and then learn how to improve it. Because once it's actually out in the real world, that's when the real learning begins. That's when feedback starts coming in, the feature requests roll out and you'll finally see how the system interacts with its environment. So instead of obsessing over every little detail, especially the wrong ones, focus on the right first step. Understand the process, ship something small and iterate fast. You've probably heard the saying, fail fast. And it's true because when you fail, you learn. And the faster that you learn, the faster that you build something better. So the formula is simple. 20% of your time building, 80% of your time understanding. Because prompts might give you output, but process gives you leverage. And as we know from lesson one of today's video, it's all about leverage. So let's quickly recap all four lessons. The first one is that AI automation isn't actually about automation, it's about leverage. The second one was that depth in one area will get you results, not juggling five different tools. The third one is that simplicity is more appreciated by clients than complexity. And the fourth one is that understanding business processes matters more than the AI solution itself. So I know that that was a lot of information thrown at you guys. So I compiled everything that we just talked about today and the four lessons into a full resource guide. You guys can access this for completely free. All you have to do is join my free school community and the link for that is down in the description and you'll be able to access it there. And if you're looking to connect with over 200 people who are learning to become AI problem solvers and starting businesses around AI automation, then definitely check out my paid community, AI Automation Society Plus. The link for this will also be down in the description. Got a great community of members in there. We do one live call a week and full courses that set you up to identify opportunities for automation, build those automations, and then start selling those automations. So, I'd love to see you guys in those communities. But that's going to do it for today. If you enjoyed the video or you learned something new, please give it a like. It definitely helps me out a ton. And as always, I appreciate you guys making it to the end of the video. I'll see you on the next one.