So everyone is playing with AI right now. Yeah, but almost no one is actually making real money from it. Why is that? Because they're selling the technology itself. They're not selling the actual outcome. Right. Welcome to the Deep Dive. I'm glad you're here with us. Today we're unpacking a really specific framework. We're looking at how to transition from just from staying busy with AI tools to actually profiting from them. Exactly. We've got a lot of ground to cover for
you today. We're going to explain why prompt packs are a complete dead end. Yeah, they really are. And then we'll show you how to structure your inputs using the MAPS framework. Plus, we'll get into the rule of R for automation. And we also need to talk about the warning signs. You know, what happens when you scale up with AI agents. But I think we have to start with the biggest trap in the AI space right now. Oh, absolutely. Which is this... this deep obsession with the
tools themselves. It's everywhere. I mean, people are obsessed with the shiny new toys, and they completely miss the business value. It's like they're hypnotized by the tech. They really are. You see everyone trying to sell these like massive prompt packs or basic chat GPT tutorials. Even automation templates kind of fall into this trap. I mean, technically there is a market for those things. Sure. Yeah. But the ceiling is brutally
low. Like competition is just overwhelming because everyone has access to the exact same information. The real money always sits one level deeper than the tool itself. Right. It's about finding a broken process. Precisely, because business owners don't care about language models. They have a costly process, right? Say they're losing warm leads because their follow -ups are terrible. They're bleeding money. They just want the bleeding to stop. Exactly. Whoever fixes that specific
outcome gets paid. And they can get paid well, regardless of the tools they use. The tool should be a... Totally invisible. Yeah. I like to think of it's like stacking Lego blocks of data. The client doesn't care about the plastic bricks. They just want to buy the finished castle. That is a perfect analogy. Let's look at IG Group from our source material. Massive global financial services company. And finance means heavy compliance.
Incredible compliance, yeah. They had strict regulatory requirements and they needed multi -language content fast. They were paying insanely high agency costs just to keep up. So what did they do? They deployed Claude. quietly across their teams. And the results were wild. The analytics team saved 70 hours a week. Marketing saw triple -digit speed -to -market improvements. Wow. Yeah. Full ROI in under three months. But here's the kicker. Claude was just the invisible engine.
They didn't sell AI to their customers. They just delivered better financial services. So looking at that, what are the three conditions for spotting a real opportunity? You're looking for a task taking skilled people a long time. The final output has to be very high stakes, and the business must already be spending money to solve it. Look for manual high stakes tasks where businesses already spend money. Exactly.
Right. So once you identify the right high stakes problem, you actually have to get the AI to do the work. And if your output is generic, you know, the model isn't broken. Your prompt is. Yeah, your prompt is definitely broken. Vague prompts just give you vague answers. Which brings us to the MAPS framework. Let's break this down for you. M is for mission. You have to give the AI the actual business goal, right? Right. Not just the task. So instead of just saying, find
me leads, which is terrible. Because it has no context. Exactly. You say, I need 30 customers to hit my $15 ,000 revenue goal. That's the mission. It grounds the AI. Beat. Then A is for ask. Make one specific request. Right. Don't say, help me with sales. Say, give me a list of 40 U .S. leads with name, company, email, and LinkedIn. Precision is key here. It is. P is for parameters. This is the background context. Your ideal customer,
your constraints. And honestly, a massive time saver here is using voice input to just dictate those parameters. You can just talk it out. I have to admit something here. I still wrestle with prompt drift myself when I rush and skip the parameters. It's really easy to get lazy. Oh, we all do it. But skipping parameters guarantees garbage output. The context is what makes it work. Yeah. And the last one is S shape. Tell the AI exactly what the output should look like,
like format it as a table. Right. And I have to ask you, why is shape the step everyone seems to forget? Skipping it means wasting 20 minutes reformatting a useless wall of text. It really does. It's such an unforced error. Absolutely. We're going to take a quick break here. Don't go anywhere. Insert a clear break here for the mid -roll sponsor read. All right, we're back. So MAPS can use you perfect single outputs. But for tasks that happen every single week, running
manual prompts just, it isn't enough. You need automation. But only if it passes the test. Right, the rule of R. We use tools like Zapier or Make for this, which is basically digital glue that automatically moves data. between your different apps. That's exactly what it is. So the first R is repetitive. Does the task happen regularly? Like manually checking ConvertKit every Monday. Yeah, and then pasting those email stats into Notion, that's highly repetitive. The second
R is rule -based. Meaning the logic is perfectly predictable. Right. Say a Typeform lead comes in, it goes straight to your HubSpot CRM. A CRM is a digital filing cabinet tracking all your customer relationships. Right, so it hits HubSpot, then triggers a ConvertKit email. The logic never changes. And the final R is return. Will it save you at least one hour a week? Because if yes, the setup effort pays back in a month. Which is huge. So what's the danger of ignoring the
return rule? You'll spend days building a machine nobody needs. It's a trap. Spending 60 hours to automate something that saves two minutes is a bad trade. An incredibly bad trade. So what happens when a problem is too complex for a single prompt or, you know, rigid automation? That's when you graduate to AI agents, systems that can actually make decisions mid -task. They adjust on the fly. Exactly. Look at Klarna. They built
an OpenAI customer service agent. In its first month, it handled 2 .3 million conversations. Whoa. Imagine scaling to 2 .3 million queries in a single month. It's mind -blowing. It cut resolution time from 11 minutes to 2 minutes, and repeat inquiries dropped by 25%. That saved them a ton of money, right? Service costs per transaction fell from 32 cents to 19 cents. But there is a real warning here. Klarna eventually had to rehire human agents. Prioritizing cost
over quality really backfired on them. Right, because agents can hallucinate. You need humans in the loop to review drafts. How do we prevent these long agent workflows from hallucinating or going off track? Well, you have to engineer the process carefully. Build checkpoints where a human or second agent reviews the work mid -process. That's exactly it. A second agent checking the first agent is a game changer. It's a fascinating architecture. So you have this toolkit now. Claude
for Thinking with MAPS. Zapier for scheduled tasks with the rule of R, and agents for complex workflows. The final piece is knowing how to charge for this superpower. Yeah, and this is where people mess up. You have to become the orchestrator. You know which tool fits which problem. But there's a big pricing mistake happening. Massive mistake. When AI makes delivery faster, people instinctively drop their prices. But a lead gen service worth $5 ,000 before AI is still
worth $5 ,000 after... Because the outcome hasn't changed. Exactly. Klarna didn't pass all their 40 % savings back to the customer. They kept the margin. You anchor your price to the client's outcome, not the hours you spent running, Claude. Let the technology stay invisible. Totally invisible. Why is it so hard for creators to keep their prices high when AI does the heavy lifting? Because of guilt. Honestly, it feels like cheating. They wrongly tie their self -worth to hours worked
instead of the final results. Exactly that. You have to break that mindset. Let's synthesize this. To actually win with AI, stop selling your knowledge of the tools. Find expensive problems. Master your inputs with MAPS. Filter your busy work through the rule of R. Use agents for big workflows, but keep humans in the loop. And above all, price for the outcome. I want to leave you
with a final thought to mull over. If the ultimate goal is to let the AI technology remain completely invisible to the client, What happens in five years when every single business has the exact same invisible AI capabilities? What becomes your ultimate differentiator then? That's the real question. Thank you for joining this Deem Dive. Look at your own workloads today and just find one task that passes the rule of R. Start there. OUTRO music.