Every week people upload a file to chat GPT and type the same word, summarize. They get a tiny report, they nod, and then they change nothing. The summary was never the problem. The question was. OpenAI just published the real prompts their finance team runs every single week. And their team almost never asks what the data says. They ask where the next dollar should go. I'm Dylan. I run an AI consultancy. And I've typed summarize for years before this made me stop. So I'm going to show you the questions their whole workflow is built on and how to run it in files that you likely already have right now. So, let's get into it. But the key thing here is these 16 workflows are sure being applied to finance, but many of the things you can learn from these can be applied to really any other team that relies on data to make decisions. And that's almost every single team inside of a company. Customer support, marketing, operations, HR, etc. So from these 16 workflows, I want to show you the things that actually matter. And the first thing here is the primary use case that they wanted to flaunt through those 16 workflows. Their finance team was in marketing. They struggled to make decisions quickly. It wasn't in the issue that they didn't have data and they didn't have money to spend, but it was figuring out where that money was best suited to be spent. And if this were the week where they're spending the money to publish ads in regards to selling a product, by the time they came to an answer for making a decision, that week already passed. So the data and the decision was outdated. So they needed a faster way to make decisions. Because if you've done any type of marketing or spending on anything in regards to advertising your services or products, you know that there's a form of diminishing returns. So if the vertical axis here is return and the horizontal axis here is money spent over time, you can see the first dollar that's spent on this channel. So this could be Reddit, Instagram, YouTube, doing some sort of ads. You realize that the return here is much higher. So you put in $1 and maybe you get back three. But over time as you spend more and you scale, you realize that one specific channel, you put in $1 and you likely get $1 out. So the return on that is very low. And the first primary key we can learn from this example that they talked about in multiple workflows that they shared is that the question that they asked when utilizing AI in their workflows wasn't what does this data say? Cuz that's just a summary ask. Instead, they were saying, "Where should the next dollar go?" And that's the key here. Is they're not simply asking the AI to summarize the data, but instead to take the data itself and help them make decisions based on it quickly. And by asking this type of question to the AI, you fundamentally have the AI think and do different things. And it's not to say that summaries are a bad thing. Summaries are okay. I think it's a good place to start. But fundamentally, when you ask an AI to summarize something, it's simply shrinking down the data and distilling it into something that's digestible for you. But it's retroactive. It's on the previous month, the previous week. It's something that's already happened. But oftentimes, when we're asking an AI to summarize something for us, we're really trying to make a decision based on that summary. And that's often where comparison comes into play. When we're asking the AI, based on this data set, this is where we've been spending our time, either our time or money or whatever else, should we shift it over here or vice versa? Quick pause. If you're enjoying this, you're going to enjoy two other things. First off, below is a 30-day AI insight series, completely free. You'll get 30 insights in your inbox as to how you can apply AI to your business and your work. The second thing is if you'd like to work with me, below are a series of offerings to see if there's a good fit for the two of us. Now, let's get back into the video. And there are three key questions that the finance team kept on asking through these different workflows they were sharing. So, the first one is what everybody's already doing. So, we're simply asking the AI, "What happened?" So, this is a summary ask. But remember, this is retroactive, and this is just the base. This is where you should start. But quickly after that, you want to climb the ladder of value. So, asking more valuable questions, getting more valuable answers back from the AI. The next one here is asking, "Where am I getting less back?" So, this goes back to the diminishing returns point that I made previously. A common example people like to talk about is pizza. So, if I give you one slice of pizza and you're really hungry, you'll probably really enjoy that piece of pizza. But if I give you five or 10 slices, and after that 10th piece of pizza that you've eaten, it's likely not going to be as good as the first piece that you've e

So, everybody's telling you the same way to make money with AI in 2026, which is to start an AI agency, find clients, and then sell them AI automations. But the opportunity to build a profitable AI agency is shifting. The real opportunity for most people right now is to become the AI person. And right now, this is the closest thing to a job that can't be replaced. I actually used to be an AI person at one of the biggest banks in the world just a few years ago. Now, that position has seen a huge increase in demand over the last couple of years, and it will keep growing. So, in this video, I'll break down what the AI person actually is, why every business is going to be desperate for one, and the exact road map for you to become one. So, let's begin. All right. So, what actually is the AI person? Right now, there's two types of people using AI at work. The first type opens up chatbt or co-pilot, asks a couple questions, gets an answer, and that's pretty much where it ends. While the second type knows how to take AI and actually build something with it. Whether that's an agent that runs the support inbox or a workflow that writes their weekly report or a system that cleans up the data before anyone touches it. The AI person is the second type of person. You're the go-to inside a business for anything AI, the one who finds the problems worth automating and then builds the fix. And you can do this one of two ways. You can do it as your own thing, going business to business and selling it as a service or in-house where you become the AI person inside of a company. And that seed is worth going after because it's the fastest growing, best paid ground in the entire job market right now. And I mean that with actual numbers. PWC tracks over a billion job ads every single year. And in their 2026 report, workers with AI skills are getting paid a 62% premium over people doing the exact same job without those AI skills. And a couple of years ago, that premium was only 25%, but it more than doubled. And it's still going up. The roles at the very top, pay a lot more than that. The forward deployed engineer, which is basically the AI person who's heavily focused on building, went from around 640 job postings to over 5,000 in a single year. And Palanteer pays them about 210 grand at the median. and OpenAI and Enthropic are also hiring the same role right now. And Chief AI officer, a title that barely existed 3 years ago, is paying a median of 1.6 million at the companies that are actually disclosing this kind of stuff. Those postings were up 478% in one year. The number of job titles that even mention AI have tripled since 2022 and almost 2/3 of them are outside of tech. So, healthcare, marketing, logistics, management. So, being the AI person isn't just some niche tech job. It's turning into a requirement in every company and every department. And the earlier that you can become the AI person, the faster you can move up. So that's the value to you. But the reason that every business is about to need this person comes down to one single gap. Almost every company already knows that AI matters. You know, they've got the budget and they're under real pressure to use it so that they don't fall behind their [music] competitors. But the problem is almost none of them have pulled it off successfully. I'm sure you guys have all heard that MIT study. They ran a study on enterprise AI products in 2025 and they found that 95% of company AI pilots delivered no measurable return at all. And then McKenzie found basically the same thing. 88% of companies say that they're using AI somewhere, but only 7% of companies have actually scaled it across the business. So, what we're seeing is the budget's there, the pressure is there, the results are not there. Every one of these companies needs someone who can walk in and turn the AI spend into an actual result. And right now, that seat is pretty empty. And real quick, before we jump into the actual road map, I want to let you know that I have a full guide on how to price AI workflows in my free school community, which you can access for completely free using the link in the description. I also published a video alongside that guide where I go over all these different scenarios and methods, which I will tag right up here if you want to check that out. Now, whether you want to sell your services to businesses or you want to be the in-house AI person, understanding what I talk about in that video is really important because it's all about proving the value that these systems will create and justifying the expense, which is how if you're the in-house AI person, you're able to go ask for, you know, bigger budgets and more resources for your projects that you want to take on. So, anyways, let's get back to the video. Here is the actual road map to becoming the in-house AI person. And I broke it into three phases. So phase one is just to position yourself. You start as a builder and that just means you'r