How I'll get AI to build a profitable business | Dabble.AI #2
Discovering autonomous business models: A macro strategy
In this post I'll outline a marco strategy I'm developing in an attempt to get AI to plan, build, launch, and run a profitable business. The goal is to discover autonomous business models. The strategy is the high-level approach I'm working on to achieve the objectives I’ve defined for the Dabble.AI project.
My objectives are:
Use AI tools to plan, build, launch, and run a profitable business.
Spend as little human time as possible to achieve my first objective.
Document the entire process and share the it with the world.
The Macro Strategy
Here are the high-level steps I have defined.
Choose a business model
Decide what to sell
Define the target audience
Develop a go-to-market strategy
Create the product
Launch the product
Test to determine product-market fit
Automate or eliminate manual processes
Scale and maintain profitability
Repeat the process with another product
Step 1: Choose a business model
The first step will be to choose a general business model. The focus of this project is on starting online businesses. So I'll start by choosing one of the following general online business models.
E-commerce: Selling goods online through individual websites or platforms like Amazon, eBay, etc. This model includes both B2C (business-to-consumer) and B2B (business-to-business) approaches.
Subscription Services: This model can include any service that charges a recurring fee, like SaaS services (Software as a Service), online magazines, or even subscription-based food delivery services.
Freemium Model: Offering basic services for free while charging for premium features. This model is common in software services, mobile apps, and online games.
Affiliate Marketing: Earning commissions by promoting other people's (or company's) products. This includes bloggers, YouTubers, and social media influencers who link to third-party products.
Online Marketplaces: Platforms where third-party sellers can list and sell products, like Etsy for handmade goods, or Airbnb for accommodation rentals.
Online Education and Courses: Offering educational content, courses, or training programs online. This can range from platforms like Coursera or Udemy to individual experts selling courses on their websites.
Content Monetization: This includes bloggers, podcasters, and video creators generating revenue through ads, sponsorships, memberships, or donations (e.g., using platforms like Patreon).
Digital Advertising: Selling advertising space on websites or social media platforms. This includes both direct ad sales and programmatic advertising.
Dropshipping: Selling products without holding inventory, where the retailer transfers customer orders to a third-party supplier who then ships the product directly to the customer.
Crowdfunding: Funding a project or venture by raising small amounts of money from a large number of people, typically via the internet. Platforms like Kickstarter and Indiegogo are popular for this.
Consulting and Coaching: Offering expert advice or coaching services in a specific field, often conducted entirely online via video calls, email, or webinars.
Cloud Services: Beyond APIs, this includes data storage, cloud computing, and various IT services provided over the internet.
Software as a Service (SaaS): Offering software applications over the internet, typically on a subscription basis. This includes platforms like Salesforce, Slack, and Zoom.
Artificial Intelligence as a Service (AIaaS): Offering AI services over the internet, typically on a subscription basis. This includes platforms like Google Cloud AI, Microsoft Azure AI, and Amazon AI.
Platform as a Service (PaaS): Offering a platform for developing and deploying software applications over the internet, typically on a subscription basis. This includes platforms like Heroku, AWS Elastic Beanstalk, and Google App Engine.
Infrastructure as a Service (IaaS): Offering cloud-based services for computing, storage, networking, and security over the internet, typically on a subscription basis. This includes platforms like AWS, Microsoft Azure, and Google Cloud.
Blockchain as a Service (BaaS): Offering blockchain services over the internet, typically on a subscription basis. This includes platforms like Microsoft Azure Blockchain, IBM Blockchain Platform, and Amazon Managed Blockchain.
Internet of Things as a Service (IoTaaS): Offering IoT services over the internet, typically on a subscription basis. This includes platforms like Microsoft Azure IoT, AWS IoT, and Google Cloud IoT.
Robotic Process Automation as a Service (RPAaaS): Offering RPA services over the internet, typically on a subscription basis. This includes platforms like Microsoft Power Automate, AWS RoboMaker, and Google Cloud RPA.
Business Process as a Service (BPaaS): Offering business process services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365, AWS Connect, and Google Cloud Contact Center AI.
Human Resources as a Service (HRaaS): Offering HR services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365 Human Resources, AWS WorkDocs, and Google Cloud Talent Solution.
Marketing as a Service (MaaS): Offering marketing services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365 Marketing, AWS Pinpoint, and Google Cloud Marketing Platform.
Sales as a Service (SaaS): Offering sales services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365 Sales, AWS Marketplace, and Google Cloud Sales Platform.
Finance as a Service (FaaS): Offering finance services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365 Finance, AWS Financial Services, and Google Cloud Financial Services.
Accounting as a Service (AaaS): Offering accounting services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365 Finance, AWS Financial Services, and Google Cloud Financial Services.
Legal as a Service (LaaS): Offering legal services over the internet, typically on a subscription basis. This includes platforms like Microsoft Dynamics 365 Legal, AWS Legal Services, and Google Cloud Legal Services.
Some of these models overlap with each other. For example, SaaS, AIaaS, PaaS, IaaS, BaaS, IoTaaS, RPAaaS, BPaaS, HRaaS, MaaS, SaaS, FaaS, AaaS, and LaaS are all examples of cloud services. However, I'm going to treat them as separate models for the purposes of this project.
Generating a more specific business model
After choosing a general business model, I'll use it along with other criteria to generate a list of specific business models that I could potentially pursue.
I'll engineer a prompt to guide AI to generate business model options based on certain criteria that I'll define. From the generated list, I'll then choose one of the business models to refine and test.
I'll be choosing the business model based on additional criteria that will be used to rank the options. The criteria will be largely based on the following questions:
Can the product be created using AI?
Could the product be created in under 90 days?
Is the product something that can be sold online?
Is the product something that can be sold as a subscription?
Is there evidence that there is a market for the product?
Is there evidence that people are willing to pay for the product?
Could the market support a new $1M+ business?
Is there a lot of competition?
Is there a clear path to profitability?
Is the market likely to grow in the future?
Are there significant barriers to entry?
Is there a clear path to determining product-market fit?
Could product-market fit be evaluated in under 6 months?
Can the model be fully automated and scaled using AI?
Could the model be replicated with other products?
Answers to some of the questions will eliminate certain business models. For example, if the product can't be created using AI, then it's not a good fit for this project. Or, if the product can't be created in under 90 days, then it's not a good fit for this project.
The answers to other questions will be used to rank the remaining options. For example, if there is a lot of competition, that doesn't necessarily disqualify the model, but it would rank lower than others that have less competition.
The ranking and scoring algorithm will initially be based on my own intuition and experience. However, I will be using AI to help me evaluate the ideas and improve the ranking algorithm over time.
I'll also use other proven tools and methodologies to help me evaluate different models. For example, I'll be using the Business Model Canvas and the Lean Startup methodology. These frameworks will also be used to help guide the AI training or prompting that I'll be using to improve the ranking and evaluation process.
Step 2: Decide what to sell
The specific business model won't necessarily define exactly what will be sold. For example, if I choose the e-commerce model, and I decide to sell data sets, then I'll need to decide on what specific data sets to sell. Deciding on the specific product will largely be based on the following questions:
Could the product or an MVP (minimum viable product) (MVP) be created in under 90 days?
Could the product be created using AI?
Could I generate revenue from the product within 90 days?
Could I decide to scale or bail within 6 months?
Step 3: Define the target audience
Ideally, I'll be able to adapt the product for multiple audiences. However, I'll need to start with a specific audience to target. This decision will largely be based on the following questions:
Is there a specific audience that I'm already a part of?
Is there a specific audience that I'm already familiar with?
Is there a clear path to reaching the audience?
How much would it cost to reach the audience?
What is the size of the audience?
Is the audience likely to grow in the future?
How quickly could I test the product with the audience?
What do I expect the customer acquisition cost (CAC) to be?
What do I expect the lifetime value (LTV) of the customer to be?
What do I expect the customer retention rate to be?
What do I expect customer retention cost to be?
What do I expect the customer churn rate to be?
How confident am I in my answers to the above questions?
To be continued…
I’ll pick up in later posts to discuss steps 4 - 10. But in the next post, I’ll detail how I’m generating the business model to align with my values, interests, areas of knowledge, and my key objectives. I’ll discuss the AI tools I’m using and provide examples of the prompts I’ve come up with. So stay tuned!