You’re Not Behind on AI. You’re Just Missing This

0:00 If you feel behind with AI, give me 20 minutes and by the end of this video, you will know how to get more useful work out of it than probably 99% of people because you could spend the next

0:10 year trying to learn every single model and test hundreds of tools and save another thousand prompts and still get answers like this. Define your audience, [music] create an offer, post content,

0:23 network, send outreach. I mean, it sounds reasonable, but it doesn't know what you can sell and who you can help, what resources you have, or whether any of that fits around your life. The model

0:38 is not the missing piece for you. The way you guide it is. And for that, you only need five moves. I call them the five C's. These five moves work in claude, they work in Gemini, they work

0:50 in chat GPT, any other capable AI tool because the model is interchangeable, but the method is not. So over the next 20 minutes or so, we are going to rebuild this answer together. And with

1:04 every move, you'll get [music] one simple framework and one prompt that you can use immediately. And by the fourth, you'll see how AI can even reject part of the plan that it created itself.

1:15 Starting with the first C which [music] is clarity. I mean look at our first request again. Get clients is a wish. It needs a clear finish line. So the first C needs to be clarity. And for that I

1:29 use three things. Result, reality and rules. The result is what I want to have at the end. The reality is where I'm starting from [music] and the rules are the limits that the answer has to

1:43 respect. So, instead of simply asking for a client plan, I could say, "I want five qualified client conversations within 30 days. [music] I'm starting without an audience, an email list, and

1:54 an ad budget, or even a chosen service. I have 5 hours per week, and the plan has to tell me what I could offer, who I should approach, and what to do each week. It's the same job." And that's

2:07 only the first pass. That was clarity. Now it knows we are not trying to build a global brand or collect 10,000 followers. We need five conversations. We have 30 days and the whole thing has

2:18 to work within the 5 hours a week that we have given it. That removes entire categories of bad advice before we have wasted any time executing any of it. Now here's the question that I want you to

2:30 start asking whenever AI gives you a generic answer. [music] What does done look like? Not help me with marketing. What should marketing produce? Not help me hire, but who needs

2:42 to be hired and by when and what would make them the right person for your team or organization. Not make this better. Better for whom according to what standard? Most people get bad AI output

2:56 because they have clarity and destination problem. So result, reality, and rules. We have given it an address, but it is still writing this plan for a stranger. [music]

3:07 Fixing that is the biggest jump in this entire video and that is [music] coming next. But one thing before we get there, result reality and rules is also how I would approach learning AI itself. What

3:18 you want to be able to do, where you're starting from, and the time that you actually have available. Now, I've been working with AI since 2017, and Data Camp is what I point people to when they

3:29 want a structured path. I've partnered with them on this video because scattered material leaves you guessing at what you're missing and data cam can give you the full map and you practice

3:40 it in short browser exercises with instant feedback and nothing to install. It only takes 10 minutes at a time which is the part that turns information into capability. [music] I suggest you start

3:51 with their AI fundamentals course. There is no coding. It takes around 10 hours and it covers what this video assumes [music] you sort of already know. how large language models work, where they

4:03 break, and how to get real output from anything like claude or chat GPT or C-pilot. It also ends with a real exam and a certification that you can put on your LinkedIn or your resume or your

4:15 website. And then when you're ready to put AI to work across your business, their AI business fundamentals track is the natural next step. The same no code approach applied to using AI in real

4:25 decisions and in real workflows. I think it's the difference between knowing the tools and running your business with them. So, start the first chapter of AI fundamentals for free today. Both links

4:36 are going to be in the description. And thank you so much data cam for partnering with us on today's video. Now, back to the plan that is still written for a [music] stranger. Move

4:44 number two. Context is everything that AI needs to know to do this particular job. Well, some people give it almost nothing and other people upload 25 files and assume more must be better. But both

4:58 can miss the [music] point because it does not need every fact you have ever collected. Useful context is not all the information that you have. It's the information that can change the [music]

5:08 answer. So for this job, I wanted to understand four things. What I know, what I've done, what I already have, and what I will not do. Your answers might include things like skills or work

5:20 history or problems that you've solved or industries that you understand or tools or relationships or time or preferences, boundaries, any of that. [music] And context does not only live

5:31 in the message that you type. It actually lives in six places. First, the files you upload, obviously. Second, the project or the workspace that you are working inside of which holds everything

5:44 that you put there. Context is also related to the apps that you have connected. So it can read your calendar, your documents, your [music] inbox. Also number four, the instructions that you

5:54 set once and then forget about and whatever the tool already remembers about you. So that would be the memory. Now most people only ever use the message box. That is just one of six and

6:05 it is the only one that disappears when you close the tab. So make sure that you take advantage of the other five as well. Now, if you don't know which details are relevant, that's also fine.

6:15 You can ask it to interview you and make it gather the context itself. You could say something like, "Do not revise the plan yet. Ask me one question at a time, but only ask questions that could

6:27 materially change your recommendation. When you have enough, please summarize what you learned and show me any gaps or assumptions that remain." For the demonstration, I gave it a very normal

6:38 profile. I'm good at organizing messy information and managing projects. I have helped teams improve processes. I understand small service businesses. I can use spreadsheets or notion. And

6:50 [music] I have a few professional relationships but no audience. I would also rather work behind the scenes than spend every day making videos. None of that is extraordinary. And that is the

7:00 point. Okay? [music] Your first useful offer often is not a completely new skill. It's something that you can already do. pointed at a problem that somebody will pay to remove. [music] And

7:11 with this context, it can see a plausible service. In our case, helping a small service business organize a messy client process, perhaps onboarding or follow-up. Now, it can see a possible

7:23 buyer and it can also build the skills that this person already has instead of sending them away to become [music] somebody else for 6 months. A generic plan is often just a personal plan

7:33 missing the person. So we run the same job a second time and we get a much better answer. Okay, that was context. This version inherits the outcome and now it knows the person who has to

7:44 execute it which makes the answer much more persuasive. But we need to be careful because that also creates a new risk and [music] that's because persuasive and correct are not the same

7:55 thing. One of the channels in this plan is a poor match for the buyer that we just chose. [music] In a few minutes the AI is going to catch it on its own. Now we have a clear outcome as well as a

8:06 useful context but get clients is not [music] one task. It is a chain of decisions. Okay. What can I credibly sell? Who has that problem? Where can I reach them? What proof do I need? What

8:17 should I say? What happens if nobody replies? Now if I ask for the final answer in one lead, it has to fill in all of those decisions somewhere inside the jump. So the result may look

8:29 beautifully organized but the logic underneath is still going [music] to be loose. So our third C is collaboration. You realize handing it the whole job at once rarely leads to what you expected.

8:40 [music] So you start working through it with it. And for that I use three [music] things. Map, sequence, execute. Map the decisions and tasks inside [music] the

8:51 job. Sequence them putting them in the right order based on what depends on what. and then execute one stage at a time. My instruction here is pretty simple. I say do not create the final

9:01 plan yet. Map every decision required to reach the outcome. For each stage, tell me the question we need to answer, the information required, and the output we need before moving forward. If you lack

9:12 evidence for a decision, just stop and ask. For our client plan, the sequence might be choose the problem and service, define the buyer, find or create one piece of proof, choose the channel,

9:23 build a prospect list, create the message, run outreach, learn from the response and adjust, you can see why the sequence is so critical because if I write outreach before I understand the

9:33 buyer, the message will become generic. Now, if I choose the channel before knowing where the buyer can [music] be reached, I may build it in the wrong place. If I automate before anybody

9:44 responds, I simply send a weak message faster. Prompting asks for an answer. Collaborating builds one. This works for almost any complex job. If you're creating a course, for example, you need

9:55 to validate the problem, define the transformation, design the learning path, and then create the lessons. If you're making a decision, you need to define the criteria, gather the

10:03 evidence, compare the options, test the assumptions, and then decide. Or if you're creating content, you need to choose the audience problem, define the promise, gather the proof, build the

10:13 argument, and then write the content. The prompt did not become better or smarter, but the work became visible. The same job with a third pass will get us significantly better results. This

10:26 version inherits the outcome and the context, and now the work is in [music] the right order. And soon we will turn it into a plan that somebody could execute anytime. But first we need to

10:36 find out whether the recommendation is something that we should actually pursue. Now this is the skill that matters more as AI gets better and that is judgment. I call this one critique

10:47 and it is our fourth C. It can produce more options than you can use, more copy than you can publish and more plans than you can execute. Somebody still has to decide what is true, what fits within

10:59 your project and what should be thrown away. [music] So you change its job because it was the creator and now it needs to become the critic. Now three things make a critique real. Switching

11:09 the role, setting the bar and giving it permission to [music] fail you. And the bar is five checks. First, you need to make sure everything's correct. Are the facts right? Second, is it logical?

11:20 Would these steps actually produce the [music] result? Third, you need evidence. What do you know and what did it assume? Next, you need to make sure it fits. Does this suit the [music]

11:31 person, the buyer, the goal? And then if it's doable, can you do it with the hours and the money that you actually have or any other constraints? I would tell it to stop acting as the author of

11:41 the plan and review it with permission to reject its earlier work. I will tell it to separate facts from assumptions to test the workload and tell me what should be removed, changed, or [music]

11:50 tested before I execute anything. The earlier version recommended posting consistently on LinkedIn to attract small local service businesses. And I know it sounds sensible. Content can be

12:01 an excellent client acquisition strategy, [music] but the audit exposes that that doesn't actually work. No audience, a 30-day target, and 5 hours a week, and we have a buyer who may not be

12:12 searching LinkedIn for operational help. Content might become a useful credibility asset, but later it should not carry the weight of finding the first five conversations. Direct access

12:24 fits the situation better. professional relationships, carefully selected local businesses, relevant communities, personal outreach. The first answer chose a popular channel, something that

12:34 usually works in most [music] circumstances, but the review asked whether that was the right channel in this situation, which I think is super important because it tells you where the

12:43 value has moved. [music] Execution used to be the expensive part, you know, knowing how to write the thing, build the thing, find the hours for it. But I think that's no longer the case.

12:53 Everyone can produce now, which [music] means producing is worth close to nothing. The expensive part changed. And I think that's about knowing exactly what you want and being able to look at

13:04 what comes back and see that is not good enough yet. It doesn't come in a subscription. All right? That's the whole reason this move might actually be more important than the other ones.

13:16 That's something that I learned many, many years ago back in my consulting days. We used to always say that you can never assume because assumptions are usually the source of a lot of mistakes.

13:29 And because we've been through a lot with my team back then, that stayed with me because bad work does not always look bad. Sometimes it looks ready. Fluency makes weak ideas sound finished [music]

13:39 even when they're not. You can use AI to create absolutely. But then give it a different role, a standard, [music] and a permission to disagree. Ask what supports that? What did it assume? What

13:50 would make it fail? What contradicts it? And does it fit the actual [music] constraints? When facts matter, verify them against reliable sources. A polished paragraph is not evidence. And

14:01 AI agreeing with you is not proof that either of you is right. Now, let's run the same job the fourth time. We now have a defined outcome, relevant context, and the work in the right

14:12 order, and a recommendation that survived the review. Now, most people copy that answer, close the chat, and rebuild the whole thing from zero next time, which means the value never

14:22 compounds. But the fifth move is about turning good work into a system. Our fifth C is compounding. That does not have to mean complicated automation or five tools connected together. At the

14:35 simplest level, a system means you can reproduce a good result again without reinventing the [music] process. So, I'm going to give you five questions. What starts it? what information goes in,

14:46 what happens and in what order, what must come out, and where does a human review or maybe even decide. For our client plan, the trigger is the start of each working week. [music] The outputs

14:58 are the offer, buyer criteria, prospect list, and last week's results. The steps are research, prioritization, personalization, outreach, and follow-up. The output is a defined

15:09 number of highquality conversations started. Human approval needs to happen before a message is sent [music] and before the offer changes. And then I ask it to create the fourweek checklist,

15:19 [music] the prospect research template, the outreach draft, the follow-up schedule, as well as the questions we used to learn from the response. All inside the

15:29 original limit of 5 hours per week. And now I have something that I can actually run at the beginning of the week. Select the right prospects. [music] Research and draft. Review before sending. Follow

15:39 up. Record the responses and adjust what is not landing. There are inputs, templates, approval points, and feedback loops. And here's the part that a lot of people miss or don't even think about. A

15:51 system you keep is a document. [music] A system the tool can load is an actual system. So, let me give you my four levels. Number one, save the prompt as a template that you reopen and fill in so

16:03 you never start from a blank box. Number two, you put it in a project or a workspace with persistent memory holding your offer, the buyer criteria, the prospect list. So every conversation now

16:13 starts with the model already knowing your business and you stop reexplaining yourself. Number three, you can turn the method into a saved skill. So you can write the steps once and the tool loads

16:24 them automatically whenever that job needs to come up. So the standard is enforced without you having to ask for it. Claude can do that through what we call skills. Gemini calls them gems. And

16:35 then number four, you can also give it a schedule. Let's say Monday morning, it runs without you opening anything. And the draft is waiting when you sit down. You review, you approve, you don't have

16:45 to produce anything. So every layer only works because the one below it exists. [music] If the manual system begins producing results, then I can automate part of it. It could research each

16:55 prospect and prepare a first draft. An automation could update the tracker and schedule follow-ups. An agent could eventually monitor the pipeline and suggest the next action. But the system

17:05 needs to come first. All right? Because automation cannot rescue a process that nobody has proved actually works. Don't save the conversation, [music] save the process. If it works once, keep it. If

17:16 it works repeatedly, systematize it. When it becomes predictable, automate it. Now we can run the same job a fifth time and we can see the impact of compounding. So look, we started here.

17:27 Write me a plan to get clients. We had five words [music] and we got internet average answers. Clarity gave it a finish line using result, reality and roles. Context gave it the four things

17:38 that can change the answer. What you know, what you have done, what you have, and what you will not do. Collaboration made it map the work and execute one stage at a time instead of leaping to

17:48 the end. Critique switched the roles from author to reviewer. Gave it a bar and let it reject its own recommendation. And compounding turned that result into something with a

17:59 trigger, inputs, steps, and output and a point where you approve. Clarity, context, collaboration, critique, compounding. And you can use that in claude, in chat, GPT, in Gemini, in

18:10 Grock, whatever comes next because the model is interchangeable. But the method is not. AI mastery is not knowing everything that AI can do. It is knowing how to turn work that you need done into

18:21 a result that you can use, check and repeat. So no, you do not need to catch up on every tool released over the last 3 years. You pick one capable AI, you give it a real job that you already need

18:33 to do this week. Get clear on the outcome, give it the [music] context, work through it together, critique what comes back, and keep the process so it compounds. Do that a few times and you

18:44 will understand AI far better than anybody who spends every day collecting prompts or watching product launches. You are not behind. But knowing how to work with AI creates the next question,

18:54 which jobs inside a business are actually worth giving it? Because you can choose the wrong ones and you save a few minutes. If you choose the right ones and AI can help you create

19:03 customers, capacity, revenue, save cost. Now, in this video, I show you the five stages from zero to a first customer and the AI workflows that I would use at everyone. Now, before you go, tell me in

19:15 the comments which of the five C's you might have been skipping. In the meantime, before you go and watch the next video, thank you so so much for watching. Like this video if you did. Be

19:23 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 stop feeling like they're behind with AI and

19:32 really get some practical steps to follow. And if you want to do this in practice with a very actionable plan together with thousands of other people who are on the exact same path as you,

19:42 make sure you come and join us here in one of our two communities. You can join the trailblazers if you want to go through challenges and learn across many different types of businesses. Or you

19:53 can join us in the founders if you have a business idea or you want help coming up with one and then you want a 90-day plan to make it work. Thank you again and I'll see you in the next video.