The 5-Layer System to Make Your Business Run on AI

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**Marina Mogilko** (1:00)
In five years, the most valuable companies in the world will run on AI as a closed information loop, meaning that all data is inside AI, all calls, emails, meetings, content performance. Because then AI acts faster on it and iterating and decision-making has just become much faster with AI. As a person who lives in Silicon Valley, interviews the best minds in AI, I am trying to implement all of that in the way I run my social media company. I see the system as a few different layers, and we're building towards the very last layer right now. But I'm going to describe everything step-by-step, so you can just copy the system. Honestly, my business has immensely sped up in the past few weeks. The change is amazing and I'm glad we're doing that. By the way, if you want to keep building these systems with me, please subscribe to this channel because I share everything I try myself, what worked, what didn't, the actual numbers, and every week I bring on founders, operators, and AI builders who are actually shipping this stuff, so you get to learn from the source. Now, let's keep going and we'll start with level number one. We're going to get your basics organized. We're going to build a queryable knowledge layer. Without it, nothing is going to work properly, and by adding more agents on top of whatever you have, you're going to just add more chaos. You need a structured data layer. And I'm going to mention this very basic thing. If you're still typing, please stop that, because you're going to get a lot of your time back by switching to voice. I recently had a conversation with Ally Miller. She's basically helping employees at huge corporations start using AI. And one of the things that she said is that the best prompting is complaining to your AI. Imagine you have a problem and instead of prompting a solution, talk to your AI about that problem. And it's so much easier to complain when you're talking. And there are various apps you can use. You can use built-in stuff. The problem is I speak Russian and English and Claude doesn't really understand my Russian, so I use Whisperflow for that. It understands multiple languages and it has very accurate input. So all of your prompting should be done in voice. And when I talk to top founders and builders, most of them talk to their computer these days instead of typing. And when you're talking to your computer, you give it 10 times more context than you'd ever type. We also use Trend for anything I want to capture and process later, like maybe during a podcast, I'm recording this to make a LinkedIn post right after I finish recording, or I'm at a conference and I press record on my Apple Watch and it records the talk. And then I use Trend to process it and create a beautiful post. So once you switch to talking, let's organize your data. This part is super important because tools change all the time. And the most frustrating thing is that, for example, today, you absolutely love cloud and you're building on top of it. You're building agents there and you're uploading all your decisions, all your information to cloud, and it's not stored anywhere else. In a week, you switch to Codex. He's amazing. He's smart. And you're like, oh, I really want to use Codex for my business now. The problem is all your data is in cloud and it's kind of hard to migrate all the tiny decisions. So what we realize is that we need a database where all of our content is stored. We organize that database based on every social media channel that we run. We automatically pull the views, pull the performance, pull the transcripts, tone of voice, branding. Everything is in that database. So if we decide to switch from cloud to Codex, from Codex to Perplexity, from Perplexity to this new Gemini model, we just connect our database. And it could be as easy as Google Drive, can be more complicated systems that you find online. But honestly, it's just so much easier to have your data organized by folders somewhere that is accessible by many different agents that you're going to build later. Apart from everything that I mentioned, like all the artifacts connected with your business, I think it's really important to let AI know what your tone of voice is. What's your business strategy for this year? Like what are your personal goals? Do you have a personal constitution, like decisions that you're trying to make or trying not to make? We also have an NTAI file because we work with a lot of content and we don't want our content to sound like AI. So in addition to thinking about day-to-day documents that you work with, think about this overall strategy and how you can convey your thinking to your AI. Now, once you're set with your level number one, your data is beautifully organized, you selected a database, maybe it's just Google Sheets and Google Drive, but it's somewhere on the cloud, it's ideal because then you can access it from all the devices. Now, layer number two, you're going to build your AI on top of your knowledge base. This is where you're going to teach AI your business so deeply that it stops needing you to re-explain everything. And this is why I said data is so important. I've talked a lot on this channel about cloud and how I use cloud projects. There is something my team is testing right now that goes one level deeper. It is called Cloud Cowork. And here's the main difference. When you use a cloud project in the browser, you upload all the files into the project. So for example, if it's your, I don't know, LinkedIn project, your voice profile, your dossier, your performance data, cloud reads them inside that conversation. It's powerful, but it can only respond to you. It can't actually open your files, edit your documents, run scripts, or take actions on your computer. Now Cloud Cowork is a desktop app. We're testing it now with our YouTube team. The producers have a folder with subfolders for every part of our production process. Titles, thumbnails, scripting, distribution, guest research. Inside each subfolder is an instructions file that tells the AI exactly what to do for that task, step-by-step, what to check, what format to deliver in. The instructions work in layers. The master folder has our overall context. Voice profile, audience, business goals. Each subfolder has its own task instructions that build on top of that context. When an agent picks up a task, it reads the master file first, then the task layer, and then it executes. Whatever prompt my team types, it always passes through the same standard checks before producing output. And the feedback from the team is that results are actually far more accurate on the first try.