Brief Summary
This video outlines five realistic AI business models for 2025: AI consulting, AI agencies, AI services, AI software, and AI education. It discusses the pros and cons of each, recommends the best starting point based on individual circumstances, and provides strategies for scaling and transitioning between models to achieve growth.
- AI consulting is quick to start but has low revenue potential.
- AI agencies offer more value capture but require sales and management skills.
- AI services provide scalability and higher profit margins by selling outcomes.
- AI software has the highest scalability and profitability but demands technical expertise.
- AI education is best pursued after gaining experience in other AI business models.
Intro
The video introduces five AI business models that are realistic to start in 2025, moving beyond clickbaity names to focus on practical concepts. Ben, who has experience running all five models, will discuss the pros and cons of each, help viewers determine the best starting point for their situation, and explain how to transition between models for growth. The models include AI consulting, AI agencies, AI services, AI software, and AI education.
The 5 AI Business Models
The first business model is AI consulting, which involves providing AI guidance to businesses or individuals. This is a good starting point for those learning AI, as it helps others plan projects and debug solutions. The second model is an AI agency or AI automation agency, similar to a traditional development or IT agency. It focuses on designing and building custom automation solutions for businesses, often with added training and consulting.
The third model is AI services, comparable to traditional marketing or lead generation agencies. Unlike AI agencies that sell custom solutions, AI services sell outcomes, results, and KPIs, using AI to automate and enhance their delivery. The fourth model is AI software, which involves selling a self-serve software tool to businesses, similar to a SaaS model. An example is an AI BDR software that companies can use to generate more leads themselves.
The final model is AI education, which addresses the global need for AI knowledge. This involves creating courses, communities, or programs to deliver AI skills and guidance.
Pros & Cons + Which one to choose
AI consulting is easy to start with basic automation skills, offering quick revenue, but it caps out at a low monthly revenue (around $3-5k) due to hourly pricing. Starting an AI agency is recommended because the required skills are similar to consulting, but it allows for capturing more value by delivering and building solutions. Offering AI consulting as a lead magnet for an AI automation agency can also be effective.
AI agencies can charge on a project or retainer basis, but the market is still unfamiliar with these services, and sales can take a few months to pick up. Scaling an AI agency requires hiring and managing more people, leading to profit margins similar to traditional service agencies (20-30%). Despite the challenges, an AI agency is a great starting point because it requires minimal initial skills and provides a ramp to more scalable business models.
AI services require domain expertise to deliver results, making sales easier and faster. Automating service delivery with AI allows for handling more clients with a smaller team, leading to higher revenue caps and profit margins compared to AI agencies. AI software businesses require technical and go-to-market expertise, making it a slower route to revenue. However, the scalability and profitability are the highest, as the same software can be sold to thousands of businesses.
AI education requires technical or domain expertise and scalable acquisition channels. It is best pursued after gaining experience in other AI business models to provide credible guidance.
How to Scale an AI Business
To scale any AI business model, scaling acquisition is essential. For AI consulting, scaling revenue involves working with bigger clients, charging higher prices, and potentially scaling a team. However, transitioning to an AI agency is a better strategy to capture more value by providing actual solutions. To scale an AI agency, focus on working with bigger clients, adding consulting and training to the value proposition, and improving hiring, management, and SOPs.
An alternative to scaling an AI agency is transitioning to an AI service business, which is more scalable. This requires building domain expertise in a specific area to deliver outcomes. Another route is transitioning to an AI software business by niching down on a specific industry and focusing on productizable projects. To scale an AI service business, provide better outcomes, automate processes, and improve hiring and management.
Transitioning from an AI service to an AI software business involves automating service delivery and productizing it into a self-serve product. Scaling an AI software business requires building a better product, achieving product-market fit, increasing pricing, and scaling acquisition. Launching an AI education business can complement the software business by educating users on how to use the tool effectively.
