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Dan Martell
Welcome to the Growth Stacking podcast. This is Dan Martell. Get rich with these five AI business ideas. If you're new to my stuff, I'm Dan Martell. I'm a technology entrepreneur. I've invested in a dozen AI companies. I coach about 50 plus and I've helped many of my friends build AI business models. And what I wanted to do is extract the five big categories that you need to consider to to make millions of dollars in AI today. The first one is content creation. A friend of mine, Chris, a couple months ago he was going through this process, I walked him through how to think about creating content using AI. And today he took a nine hour piece of work and has distilled it into 15 minutes worth of effort that gets $150,000 grants approved using this exact process. The first one is look for the constraints. Anytime there's content being put out there, it could be marketing content, it could be producing scripts for video content. What is the bottleneck around that? What are the constraints? Because what you want to help people do is move fast. Then you want to go to number two, which is the content. Look at the output, understand what parts of that information makes it unique. If it's Facebook ads, if it's blog posts, what makes it unique? Because what you're looking for is how do you replicate the output output. Then number three is you gotta figure out what are the prompts that go into this. The prompts are where the heavy lifting for AI engineering goes into. You're gonna have to figure out how to create the prompts that generate the content based on the constraints on the front end. And it's all about the inputs. Then the last part is the solution. Take all of that, package it up into a service and then put it out there. So you gotta sell that solution. The second one is training and education. You might think everybody knows about AI. Trust me, they don't. So you to create a business where you get paid to train and educate the market. There's companies like Deep learning, they do 50 million a year teaching and training people on the topics of AI. You can even get paid to help small businesses integrate AI into their company. And that's the first step in this process is understanding where's the gap. Talk to the small business owners and ask them, where are you using AI to increase your efficiency or solve problems faster? You first got to start with the gap. Then you, you go to research the solution, have those interviews, talk to those customers, figure out where the opportunities are to train and educate. Then research, become an Expert around the solution. The third step is to pilot that solution with those early customers. You find five customers, you interview them, you see what their challenges are. You then go and do the research to solve the problem. You pilot it with those customers, outline the prompts, the strategy, whatever you want to integrate. And then finally, then you create a course. And here's the coolest part. You actually ask AI to create the course based on the research and the pilot that you've put together. I'm telling you, it's that simple. Get in front of a camera, create the course, start selling it. That's how you can get paid today to train and educate other people on AI. The third business is consulting and implementation. There's four key areas you need to focus on if you wanna make money using this business model. The first one is you find somebody, you gotta do free work and the reason why is cause you're trying to extract the assess. The assessment is when you start working with a new customer, what are the questions you're gonna ask them in regards to their AI adoption? What are they doing today? Where are the opportunities? That's number one. Then we want to focus on, out of all the opportunities, what's the biggest problem that you can solve that also gets them a fast result? Biggest problem, fastest result using AI that you can do for them. Then you go to third step, which is extract the methodology. In consulting businesses, the methodology is the product, it's the for how you actually implement what you do. Then the last step, number four is you have to teach free information. Get paid for implementation. This is the key. Most people don't get this. Give away everything you know how to do and then get paid for the implementation, get paid for the consulting. Number four is data monetization. I told you it's going to get more advanced, but this one is actually next level. A long time ago, I had a company called Flowtown and I had an investor named Oren. Oren had sold his company, Rapleaf, and he was working on a new idea. And his big idea was to create a marketplace for data providers to sell to AI companies. And this was seven, eight, nine years ago. And at the time I didn't think it would actually be a big idea. I'm like, who would buy this information? And it turns out it's become an incredible business. There's opportunities for you to go and find data sets that are unique to make sure that they're accurate, they're clean. I'll tell you what to look for and then sell those to these people. On this side and make a ton of money, if you understand this element right there. So let's dive into it. First off, you gotta find information that's got relevance. So trying to figure out what some of these AI people are looking for, data wise, that's the key. It's gotta be relevant to their models. Number two is it's gotta be high quality. What I mean by this is it can't have a lot of noise, it can't have a lot of inaccuracies. And then the final part, which is the key for anything AI is volume. These large language models need hundreds of thousands, if not millions of records around the data. So you gotta find stuff that's relevant and clean, high quality and high volume. Now, who do we sell this to? First off, you've got three key people. Number one is data scientists. Go on LinkedIn, find the people that are data scientists. Inside these groups of people, the hedge fund companies, the technology companies, they've got a data scientist that tells you that they buy data. Number two is a data engineer. Data engineers are actually the people that sit there and they massage the information that comes in to make sure it can map to the AI models. And then the most important role in Silicon Valley in all technology companies is product managers. These are people that are making decisions about product roadmaps and how they're going to differentiate and compete against their competitors. So they're always looking for data to be competitive. Now, the business model for you to do is you will make tens of millions of dollars doing this right is you have to be good at transforming the data. There's different file types and depending on what technology the data scientists are using, they might need you to transform that data so that it fits in their world. The fifth business model for making money off of AI is products or services. See, products or services are awesome because you will get paid all the time for people using the product for you. To find the right product or service is going to require a few things. There's three elements. My buddy Darrell, he runs a company called Flexpay. He was in the industry that he's in today, but he saw an opportunity to take all this data and create a better solution. Now, Darrel's business is a multimillion dollar company and you can do it too. See, there's three things you need. The first one is domain expertise. You need to understand your industry because you want to go deep and look in the nooks and crannies of what's going on in the market that you could See opportunities for AI number two. And this is what Darrell did because he understood the problems. He found an AI exposed problem. An AI exposed problem is an opportunity to use big data to improve a process. See, one of the things he noticed in the payment world is that many of the transactions were getting declined. Maybe you've experienced this where you go to use your credit card online and it doesn't wanna work, but it doesn't make sense cause there's space on the card. Darrel saw an that was an AI exposed problem. And then what he did that I'm recommending you all do is he hired a data scientist and said, we have all this data, could you look at it, evaluate it and tell us do we have an opportunity to create a product or a service around AI? And not only did they have an opportunity, they realized this problem was a massive problem. Think about all of the subscription businesses in the world, all these micropayments, they're all there online and they have problems with customers, credit cards getting declined. They took all this information, the domain expertise he had the AI exposed problem and the data scientist research and they decided to build the AI enabled product which became Flexpay. You can do the same thing. So those are the five business ideas in AI that'll make you rich. And if you want to learn how to get 10 more clients by the end of the year, click the video on screen and I'll see you on the other side. If you like this week's episode, be sure to visit itunes, leave a review that'll help us get in front of other factors founders just like you. And if you're looking for more resources and video trainings, be sure to check out dan martell2lsdmartell.com to subscribe. Keep up the hustle, keep stacking your growth and I'll see you in next Monday's episode. Peace. Grow Peace. Bye bye.
Podcast Summary: Build Your A.I. Empire in 2024 (STEP-BY-STEP PROCESS) The Martell Method with Dan Martell Release Date: December 8, 2023
In the episode titled "Build Your A.I. Empire in 2024," host Dan Martell delves into actionable strategies and business models leveraging artificial intelligence (AI) to achieve substantial financial success. Drawing from his extensive experience as a technology entrepreneur and coach, Martell outlines five key AI business ideas designed to help entrepreneurs break through growth barriers and build lucrative AI-driven enterprises.
Martell begins by emphasizing the burgeoning opportunities in AI-driven content creation. He breaks down the process into five critical steps:
Identify Constraints: Understanding bottlenecks in content production is essential. Martell shares a success story about his friend Chris, who transformed a nine-hour task into a 15-minute process, resulting in $150,000 in approved grants. "Look for the constraints... because what you want to help people do is move fast," (00:02).
Analyze Content Output: Determine what makes the content unique. Whether it's Facebook ads or blog posts, recognizing the distinctive elements enables replication and scalability.
Develop Effective Prompts: Crafting the right prompts is pivotal as it constitutes the heavy lifting in AI engineering. Martell asserts, "It's all about the inputs," (00:10) highlighting the significance of precise input generation.
Package the Solution: Transform the developed content process into a marketable service. This involves packaging the AI-enhanced content creation process into a sellable solution.
Sales Implementation: Finally, Martell underscores the importance of effectively selling the packaged AI-driven content services to target markets.
Recognizing that AI knowledge is not yet ubiquitous, Martell identifies training and education as a lucrative business avenue:
Identify Market Gaps: Engage with small business owners to discover where AI can enhance efficiency or solve existing problems. "You first got to start with the gap," (00:25) Martell advises.
Research and Develop Expertise: Conduct thorough research and become an expert in the identified solutions through customer interviews and market analysis.
Pilot the Solution: Test the developed training programs with early adopters, refining strategies and prompts based on feedback.
Course Creation with AI Assistance: Martell reveals an innovative approach where AI assists in course creation, streamlining the development process. "Ask AI to create the course based on the research and the pilot that you've put together," (00:40).
Launch and Sell: With the course ready, entrepreneurs can confidently market and sell their AI-empowered educational programs.
Martell outlines a consulting framework tailored for AI integration in businesses:
Offer Free Assessments: Begin by providing free initial work to potential clients to understand their AI needs. "You're trying to extract the assess," (00:55) Martell explains.
Identify and Tackle Major Problems: Focus on solving significant issues that deliver rapid results through AI applications.
Develop a Robust Methodology: Create a systematic approach for implementing AI solutions, effectively turning the consulting methodology into a valuable product.
Monetize Through Implementation: Share comprehensive knowledge freely while charging for the implementation and consulting services. "Give away everything you know how to do and then get paid for the implementation," (01:10).
This model emphasizes building trust and demonstrating value before monetizing through specialized services.
As AI continues to advance, data becomes increasingly valuable. Martell presents data monetization as a high-potential business model:
Relevance of Data: Ensure the data is pertinent to AI models, effectively addressing the needs of AI companies.
Quality Assurance: High-quality, clean data is non-negotiable. Martell stresses, "It can't have a lot of noise, it can't have a lot of inaccuracies," (01:20).
Volume Matters: Large datasets are crucial for training sophisticated AI models. Martell notes, "These large language models need hundreds of thousands, if not millions of records," (01:25).
Target Audiences: Focus on data scientists, data engineers, and product managers within technology firms and hedge funds who are actively seeking high-quality data to enhance their AI models.
Business Execution: Transforming and formatting data to meet the specific requirements of AI professionals can lead to substantial revenue streams. Martell envisions entrepreneurs making tens of millions through effective data monetization strategies.
The final business model centers on developing AI-enhanced products or services that provide ongoing revenue:
Domain Expertise: Deep industry knowledge is critical. Martell highlights the importance of understanding market intricacies to identify AI opportunities.
Identify AI-Exposed Problems: Look for issues that can be effectively addressed with AI and big data. Using the example of Darrell from Flexpay, Martell illustrates how recognizing a common problem—credit card transaction declines—led to the creation of a multimillion-dollar AI-enabled solution. "Darrel saw that was an AI exposed problem," (01:40).
Leverage Data Scientists: Collaborate with data scientists to validate and develop AI-driven solutions based on identified problems.
Product Development and Scaling: Once an opportunity is confirmed, develop the AI product or service and scale it to address widespread market needs.
Martell concludes by encouraging listeners to adopt these AI business models to build sustainable and profitable AI empires in 2024.
Strategic AI Integration: Successful AI businesses require a strategic approach, starting from identifying market needs to developing tailored AI-driven solutions.
Importance of Quality and Relevance: Whether in content creation or data monetization, the quality and relevance of AI inputs are paramount for achieving desired outcomes.
Leveraging Expertise and Collaboration: Combining domain expertise with technical collaboration, especially with data scientists, enhances the development of effective AI products and services.
Monetization through Value Delivery: Providing substantial value through free assessments or educational content builds trust, enabling monetization through specialized services and implementation.
Scalability and Volume: Ensuring scalability and managing large volumes of data are critical for the success and growth of AI-driven businesses.
"Look for the constraints... because what you want to help people do is move fast." — Dan Martell (00:02)
"It's all about the inputs." — Dan Martell (00:10)
"You first got to start with the gap." — Dan Martell (00:25)
"Ask AI to create the course based on the research and the pilot that you've put together." — Dan Martell (00:40)
"Give away everything you know how to do and then get paid for the implementation." — Dan Martell (01:10)
"It can't have a lot of noise, it can't have a lot of inaccuracies." — Dan Martell (01:20)
"These large language models need hundreds of thousands, if not millions of records." — Dan Martell (01:25)
"Darrel saw that was an AI exposed problem." — Dan Martell (01:40)
By following Dan Martell's comprehensive guide, entrepreneurs can systematically build and scale their AI-driven businesses, harnessing the transformative power of artificial intelligence to achieve remarkable growth and financial success in 2024.