7 AI Skills 99% of People Don't Know, But Should

You're probably learning AI in the wrong direction. And if you've tried to keep up but somehow still feel more behind the more you learn, this video is for you.

Listen, most people learn AI from the outside in. You know, tools first, prompts next, features after that. And it feels productive until it's not because it doesn't compound. And the people who feel behind, they're often times the first ones to notice that something about this does not add up. That is the part that I see most people misreading. Because if this feels confusing, it's usually because you're early, not because you're behind.

Look, I started my AI journey back in 2018. It wasn't a buzz word back then, and I was often the one who was usually told that I was too early. That is why in this video, I'm going to give you a map. Seven AI skills that I believe 99% of people don't know, although they should. And I'm not going to go through them as tips, but as layers so that you stop chasing tools and you start building advantage that actually builds and compounds for you.

So, let's start with something that almost nobody explains. AI is not hard because it's complex. Sure, it's not very easy most of the time, but I think it's hard because it has an order. And most people start at the wrong end of it. And when you hear people say, "Oh, AI is inconsistent. It does not really save time. Oh, I tried it and it didn't work." Well, most of the time, AI is not the problem. The sequences, there are a few foundational layers that almost everyone skips. And when you skip them, everything else feels random, of course.

So, I'm going to show you the full structure in a moment, but first I want to explain why learning AI from the outside in is what creates, in my opinion, so much frustration in the first place. So, look, most people do this. They see a new AI tool and they think maybe this is the one and then they go and try it. They get a few outputs. Some are good, some are not so good. They feel the hit of novelty and then a week later they are overwhelmed again. Why you might wonder? Well, because tools don't stack unless you build the structure that makes them stack. Makes sense, right? And I think this is the enemy. Mistaking exposure to more tools for progress. Trying more tools, writing more prompts, generating more output, that's movement. Sure, you're not standing still, but movement is not the same as momentum. Momentum only happens when each step makes the next one easier. And that is a huge mistake that I see so many people making with AI. They stay busy and they're experimenting and they're trying all of these new tools and they're producing all this output but nothing makes sense. Nothing compounds. Nothing adds up because they are building in the wrong order.

So let's change that. Okay. There is an inside out sequence and each layer answers a frustration that you've probably also felt. I know I have. So why don't we start with layer number one. Okay. So let me ask you this. Why do some people get huge results with fewer tools? This is the first skill and I call it AI leverage thinking. This is not about how to use AI. This is about where should AI even be used because the biggest mistake that people make is delegating the wrong work. I actually learned it from an investing training because in investing the biggest wins don't come from being right all the time. Actually, they come from asymmetric riskreward. Basically, we're talking about situations where the downside is limited, but the upside is disproportionately bigger, larger, however you want to call it. And that is where capital builds itself. That's where it compounds. And once I saw that, I realized that the exact same logic applies to AI. Most people delegate based on what's easy. But ease, I think, is irrelevant in this situation. What they need to think about is what happens if this goes wrong and what happens if it works really, really well. And when you map work across those two dimensions, risk and reward, you get a very different picture. Most people keep AI in the safe, low-risk, low-reward type of corner, right? Summaries, formatting, busy work and it feels productive but it doesn't add up. The real advantage and leverage is one quadrant over the low-risk high impact and this is where AI should explore things like generating options and synthesizing research or making mistakes really really cheap because the upside then it's massive that is asymmetric reward or leverage and based on what I'm seeing this is where most people don't put AI even though it's the safest place to do so. On the other side, you have the high-risk type of work. And here's the rule. AI should inform high-risk decisions. It should never own them. Responsibility doesn't scale. Accountability does not automate. Leverage does not come from letting AI do more of the wrong things. It comes from putting AI where the downside is kept and the upside compounds. The upside is asymmetric.

Okay, that single shift explains why some people get outsized results with fewer tools. Leverage thinking chooses the battlefield. Okay, so if you don't choose the right battlefield, every other AI skill becomes useless. Now, even when people pick the right task, AI still disappoints. Why? Well, let's move to the second layer. Okay, our second AI skill. So, think about this. Why does AI feel smart one moment and useless the next? Well, I call this skill decision framing. Let me explain. Once AI is in the right place, low risk, high impact, there is a step that most people entirely skip. They start delegating before they're clear on what they actually want. And that's where things go sideways. And probably that's also the reason why the AI slop has become a thing because AI is incredibly good at helping you move faster. But it's terrible at deciding where you should be going. So if you don't know the outcome that you're aiming for, AI will happily generate options, but none of them is going to feel right because it's not right because you never define the decision. This is what decision framing actually means in practice. Okay? Before you ask AI to do anything, you need clarity on three things. What outcome are you trying to move towards? What would make that decision a bad decision? What would actually change your mind? Until those three things are clear, delegating is premature. You are not giving AI a decision. You are giving it more confusion to work with. And of course, more confusion in, more confusion out. Okay? And I think this is also where most people misunderstand AI because even the best AI systems feel noisy and inconsistent. You cannot treat it like a worker. First of all, AI needs to be treated as a thinking partner, as a coach, someone that you use to clarify things, what you actually want, what trade-offs you're willing to accept, and also what success even looks like in that situation. And once that part is clear, then delegation becomes obvious because without that clarity, even the best AI systems are going to be disappointing. Okay. And that is why one of the first things that I recommend is building a personal coach style GPT. Not to do stuff for you, not to execute anything, but to help you frame decisions properly before you move on. And by the way, let me know in the comments down below if you would like me to show you how to make one of these GPTs, because I'd be more than happy to. I have my own, multiple, and I'd be happy to show you how to make one, too. Because once decisions are framed, then AI can start doing real work. Okay, leverage thinking decides where AI belongs. Decision framing decides how well it can help.

Okay, so you picked the right task, right? You framed it correctly. You know your outcome. Why does it still not save you time? Okay, well that brings us to the third layer, the third skill. And the question that many people ask themselves here is why does AI still feel like more work? And I think that's because most people treat AI like a one-off helper. They open a chat, they ask, they get something useful or maybe not, and then they start from zero again next time. That is assistance. By no means can that be called leverage because leverage comes from your ability to say, "Run this repeatedly. Do this for me at scale." A workflow is basically that, the same kind of thinking applied in the same order to the same kind of problem again and again and again. And most people never do this. They think every single task is unique. But when you look closely, most work is not. It's just variations of the same decisions made repeatedly. And that is where AI actually ends up saving you time. but by removing the redeciding. Okay. So you decide once what the input looks like, what questions need to be answered, what a good output includes and where human judgment needs to step in and then AI runs that path every time. Workflow design which is our third skill is the skill of turning a task into a system. And usually it has three to five steps. Input, transform, check, output, reuse. Simple, right? The key though is that AI only saves time when it replaces loops, not tasks. So if your process is messy, AI gets you more mess faster. Without workflows, AI just gives you speed. With workflows, it gives you consistency and speed. And consistency is what gives you that advantage that that uh competitive advantage that makes you unique in the market, whether you are competing as an individual or as a company.

And if you've been watching this and thinking, great, I know what to learn, but how do I actually learn it? How do I get the reps in without spending 2 years trying to piece it all together from YouTube videos? Well, that is the right question. And honestly, it's exactly the gap that most people never close. Look, self-learning is great, but a structured system is faster. You can piece this together from YouTube and documentation, but here's the reality. The market has moved faster than most people realize. We are not in the generative AI era anymore. We are in the agentic AI era and the gap between knowing about it and building it is massive. If we zoom out, AI has moved in three waves. The first wave was predictive recommendations, spam filters, forecasting. You can think of Netflix and Amazon as really good examples here. The second wave was generative. Think Chad GPT writing text and code snippets and summaries. And that's where most people are still stuck. But the third wave, the one that actually matters now is agentic AI. AI that doesn't just suggest, but it executes. It runs workflows, calls tools, updates systems, and makes decisions. And honestly, I think the world doesn't need more prompt engineers. It needs people who can understand and architect agentic systems. And that is why I'm partnering with SimplyLearn for this video, the applied agentic AI systems design and impact program. This is a program by SimplyLearn in partnership with Microsoft, leveraging Microsoft's cuttingedge AI expertise. This is not a beginner course, however. is for people who already understand the basics and want to actually build real systems. Over 10 weeks, you are not going to only learn theory, but you are working with the same stack that companies are hiring for right now. Microsoft autogen, Azure AI foundry for enterprisegrade agents, Langchain and Crew AI for open source orchestration, rag which is retrieval augmented generation so that your agents can work with your own data. You're going to learn multi-agent systems, MCP, the frameworks shaping cutting edge agentic products. You also earn a joint completion certificate from Microsoft and SimplyLearn, which let's be honest is a serious trust signal for your LinkedIn and your resume. So, if you're serious about building competence under everything we've discussed in this video, not just chasing trends, but really understanding the mechanics, I'm going to link the program details below.

Okay, cool. Now, you have a workflow. So, why does AI still not feel consistent? Okay, why does it reset every single time? Well, once you have clarity and you've designed a workflow, what's the next step? Okay, so there is one reason that things still fall apart and that's because AI doesn't know what matters to you. So, every interaction basically resets the game. You're starting from square one. Okay? And that's where a lot of people get frustrated because they end up saying things like, "Oh, AI keeps forgetting or it changes its mind or I have to explain everything every single time." Trust me, AI is not forgetting. Most likely, you never told it what should stay consistent. Okay? So, context isn't memory in the technical sense. We're not talking about RAM. Context is what you're trying to optimize for, what you care about, and what you consistently say no to. Okay? Also what good looks like for you. When that isn't stable, every output feels random, even if the workflow is solid. Trust me, I've seen this so many times working with clients. And I want you to think about it this way. If you hired a smart human assistant, for example, but you never told them your priorities, your preferences, your standards, or how you make decisions, for example, you wouldn't say, "Oh, this person keeps forgetting." Right? Probably you would say something like, "Oh, I never onboarded them." That is what context engineering really is. This is our fourth layer. Okay, fourth skill. It's kind of like onboarding AI into how you think. Context engineering means that you design the objective, the boundaries, the audience, your preferences, your decision rules, your definitions, your ongoing memory state. And that's also why most people's prompts don't scale because they keep restating the same assumptions, the same preferences, the same standards every single time, which I know is exhausting. I would probably feel the same. But when context is designed once in the right way, then prompts become shorter, clearer, more reliable, and that is what turns AI from a slot machine where you pull the lever and hope for the best into a system.

And this brings us to layer five. Okay. So we have come to the fifth skill in the stack. Okay. And here people think why do great prompts stop working. And that's because prompts don't scale. Let me explain. When it comes to prompting is where people think they're supposed to get good at AI. Okay. The right wording, the magic sentence, and that's exactly where they get stuck because prompts feel powerful. because they're visible. Okay, you type something and then you get a result. But writing better sentences is not what gives you the advantage. It's not what what gives you the scale. Sentences are fragile. You've probably experienced this as well. You can change the wording and then the output changes as well. And that's not leverage. That's just luck. Okay. So, prompt architectures are different. This is our fifth skill. Here we're not talking about what you're asking. We're talking about what always happens first, what must be checked, what decisions get made in what order, and what constraints never change. So in other words, or in plain English, the shape of the conversation. You need to have that clearly mapped before you are ready to get the results you expect from AI. And you can think of it like this. You don't build a company by improvising meetings, right? You design what happens there. Agendas, decision rules, escalation paths, review cycles. Well, basically prompt architectures do the exact same thing. They tell AI before you answer, do this. Always evaluate against these five criteria. If something is unclear, ask this first. Once that structure exists, then the exact wording matters less because I mean a lot less because it's no longer about the actual word. Okay? And that's why most people feel like AI is inconsistent because they keep changing the sentence instead of stabilizing the structure. But when the structure is stable, outputs become predictable. and predictable outputs. Well, that's what makes AI usable in real life, in real work, in real workflows. And this also connects back to context engineering. Okay, context defines what matters. And architectures define how decisions are made. Okay, and then together they basically remove 90% of the randomness that AI many times comes back with. And that means that you can finally trust what comes back. And once you trust the output, you can start thinking with AI, not just pingpong reacting to it.

So let's talk about the next layer. Okay, so your prompts are no longer breaking, but then something else gets exposed. That's the quality of our thinking. And maybe I should have started with this, but this is the point where AI is no longer impressive or looks like magic. And honestly, it starts being dangerous. And that's for a reason that I'm sure many people recognize. That's because it agrees with you too easily. Most people use AI to get answers, faster explanations, and cleaner summaries and nice words. And it's enjoyable. You know, people like it. And it does feel like you're progressing. You're growing, but you're not. It's just comfort. Real leverage starts when you stop asking AI to answer and you start allowing it and using it to pressure test your thinking because bad decisions don't come from missing information. They come from unexamined assumptions. And this is what AI augmented reasoning actually is. This is our sixth skill. And I'm not talking about philosophy here although I enjoy it. Um and it's also not logic for the sake of it. It's not critical thinking as a concept. It's basically forcing ideas to survive contact with reality. And a lot of people are not um comfortable with debate and confrontation. But when used properly, AI can help you tremendously. It can help you surface blind spots that you didn't know you had or compare second order consequences or explore paths that you normally ignore or stress test confidence before it cost you anything. And sure, AI could qualify as smarter than us, but it's not because of that. It's not because of ego. It's just because it critically thinks in a different way than we do. And that's probably also why some people say, "Oh, AI just gives generic answers." Of course it does. Garbage framing in, garbage clarity out, right? But when you bring clear outcomes, defined constraints, stable context, and structured thinking, you can use AI as a true thinking amplifier. Okay? It's no longer just a shortcut. It's no longer just Google prompting. Research changes here too because you also stop collecting information. You're able to interrogate decisions. You can ask things like what would have to be true for this to work or where does this break when I try to scale or what does someone smarter than me see that I'm missing? And that's not simply asking AI. It's about thinking out loud with a mirror that doesn't lie. And that's what builds confidence. It builds certainty. but from knowing that you've actually tested the idea instead of just liking the answer. I hope that makes sense.

But even this does not compound on its own because insight without feedback just evaporates and that brings me to the final layer which so many people miss. I remember back in my corporate days we would talk about this a lot and I think that's the the ultimate compounding layer. Okay. Um, too bad that not enough people build it because it does not feel exciting at first probably. But most people use AI as I said like this slot machine. You prompt, you get an output, you move on and then there's no trace of what worked. There's no correction. There's no accumulation. There's no feedback loop. And a feedback loop could be simple. You look at the result and then you can adjust the system that produced it. Okay? You can provide that feedback of what you like, what you don't like, what worked well, what didn't. And that is the difference between getting lucky once and getting better every time. Okay, without feedback, just like in real life, AI can give you answers. But with feedback, it can build judgment. It learns to know you. And this is why most people feel like they are always starting over because they never tell the system, look, this is what worked, this is what didn't, and this is what to do differently next time. It's literally just like you have a new hire. Without this nothing improves, not the output, not the workflow, not the thinking, nothing, you do not progress. But when feedback is a step that is designed in, then you will see that something so surprising happens. And what I mean by this is that you finally feel like you can start running AI. You know, you're not just hoping to get something right. And that's where people think they need something advanced agents, automation, complex setups. They don't. Those only work after feedback actually exists. Feedback is the upgrade. Everything else is just amplification.

And that's why I believe genuinely that the inside out model makes a huge difference. Leverage, framing, workflows, context, architectures, reasoning, feedback, everything needs to be done at its right time in the right order. And if you skip the early layers, the top ones feel random. But when you build them in order, AI feels less chaotic. So if you've ever felt behind or overwhelmed or like everyone else figured something out that you must have missed, you didn't miss the tools. You were just early enough to realize that the noise was not compounding for you, maybe for others as well. You realized that something wasn't making sense. And I believe that is the difference between using AI and having a real advantage. And if you want to learn AI from the inside out with frameworks and systems and real application, I'm going to make sure that we put the QR code here. Join us in the AI founders. We are not here to play with tools. We are here to build leverage and hopefully we can go on that path together. Thank you so so much for watching. I'm so grateful that you stayed with us till the end. Like this video if you did. Be sure to subscribe if you haven't done so. Share this with anyone in your circle of friends or family or co-workers who you think needs to learn more about the right order to do things in to get the right results from AI. And until next time, I suggest you go ahead and watch this video over here. Thank you again and I'll see you soon. Bye.