How to Start an AI Business in 2025 | Full Beginner Blueprint

Key Concepts

AI business models, AI automation agency (general vs. niched), AI productized services, service as a software, AI SaaS, AI education, prompt engineering, AI workflow automation, AI agents, multi-agent systems, marketing & client acquisition, personal branding.

AI Business Models

1. AI Automation Agency

• Definition: Offers automation services to increase efficiency, reduce costs, or solve specific business needs. Requires a high degree of customization per client.
• General AI Automation Agency:
  • Widely focused, implementing various automation solutions for diverse industries.
  • Cons: Requires starting from scratch for each project, difficulty understanding diverse company needs, high project scoping time, high service pricing, and marketing challenges due to a lack of specific focus.
  • Example: Henry at for a.
• Niched AI Automation Agency (Fractional AI Model):
  • Focuses on automation solutions for a specific industry (e.g., real estate, recruitment) or department (e.g., sales, marketing), or a combination of both.
  • Pros: Leverages domain expertise, clearer target market, easier marketing, reusable automations, and positions the agency as a partner.
  • Key: Position yourself as a partner instead of a builder.
  • Pricing: Subscription-based (retainer) model is preferred over project-based pricing.
  • Example: Brandon at agent automation.com (real estate), Alex at sales automated. (sales).
  • Objections & Solutions: Address concerns about monthly fees and project timelines with a portfolio of example automations, timelines, a cancel-anytime policy, and a money-back guarantee.
  • Scaling: Can be scaled by hiring more people or transitioning to a more scalable model like AI productized services.

2. AI Productized Services

• Definition: Delivers a specific, repeatable solution to a well-defined business problem, enhanced and systemized with AI and automation.
• Ideal for: Existing agencies, consultants, freelancers, or professionals with specific domain expertise.
• Key: Automating and enhancing the backend service delivery with AI.
• Pros: Enhanced value, scalability, and premium pricing.
• Example: Brooks at go pipeline formula.com (lead generation for SaaS founders), T (cold email lead generation for m&a companies).
• Challenge: Ensuring consistent results across different clients while maintaining minimum customization.
• Pricing: Implementation fee plus a revenue share and a monthly retainer.

3. Service as a Software

• Definition: A plug-and-play, out-of-the-box automation system that solves a specific business need, requiring minimal customization and support.
• Ideal for: Infinite scaling.
• Key: Requires a highly optimized and productized service that can be completely self-served.
• Difference from Traditional SaaS: Traditional SaaS provides tools, while Service as a Software handles almost the entire use case and process.
• Example: SEO content generation platform on air table, Yonas with voice agent solutions for hotels (aura AI).
• Pricing: Implementation price with usage cost or subscription fees.

4. AI SaaS (Software as a Service)

• Definition: Traditional software as a service model, but with AI integrated to provide more value at a lower cost.
• Key: Building AI SaaS for very specific niches that were previously underserved.
• Pros: Easier and cheaper to launch an MVP with no-code/low-code tools and AI coding agents.
• Example: Anonymous member with a meta ads marketing agency for small business owners in Germany.
• Challenge: Increased competition due to the democratization of tech, shifting the competitive edge towards deliverability and distribution.
• Recommendation: Start as an agency or productized service first to discover a pressing real-world problem and refine the solution before turning it into a SaaS product.

5. AI Education

• Definition: Providing AI reskilling and upskilling to individuals and businesses.
• Opportunity: Massive demand for retraining and upscaling to meet the demands of an AI-powered economy.
• Two main areas: B2B (helping companies rescale their workforce) and B2C (helping individuals stay competitive).

Things to Avoid

• Creating a SAS product too early: Most AI automations require customization, good products don't sell themselves, and you learn what a good product is by working on and implementing it into companies.
• Pay-per-project model: Companies need lots of automations, and the pay-per-project model adds friction to the process.
• Charging by the hour: Clients are paying for outcomes, not hours.
• Consulting without implementation: You can get more leverage and value by taking charge of the actual implementation.

AI Crash Course: Three Fundamental Skills

1. Prompt Engineering

• Definition: Designing effective prompts to guide AI models to generate desired outputs.
• Key: Domain expertise is crucial for injecting knowledge into prompts.
• Difference from Chat GPT: AI systems require consistent and reliable outputs, necessitating good prompt engineering.
• Frameworks:
  • Long Structured Prompting Framework: Used for complex tasks, includes role, objective, context, instructions, examples, variables, and notes sections.
  • Short Structured Prompting Framework: A reduced version of the long framework for simpler tasks, includes role/objective, examples, and variables.
  • Agent Prompting Framework: Used for AI agents with decision-making responsibilities, includes role, objective, SOP (Standard Operating Procedure), context, instructions, sub-agents & tools, examples, and notes.
• Prompting Use Cases:
  • Extracting Data: Short structured framework, cheaper language models.
  • Classification/Categorization: Short structured framework, cheaper language models.
  • Generation: Long structured prompts, best models.
  • Evaluation: Longer structured prompts, better models.
  • Data Transformation: Long structured prompts, better models.
  • Decision-Making (AI Agents): Agent prompting framework, best models, system 2 level thinking models.
• Chain Prompting: Breaking down a larger complex task into smaller subtasks for each language model or AI step.
• Language Models:
  • System 1 (Non-Reasoning): Cheaper mini models (GPT 40 mini) and more expensive models (GPT 4 o, CLA 3.5).
  • System 2 (Reasoning): Cheaper models and most expensive models (gpt1, gpt3).

2. AI Workflow Automation

• Definition: Automating tasks and processes based on logic and predefined rules, connecting different software and databases with APIs.
• Key: Analyzing, transforming, and taking action on data before outputting it into the next software.
• Fundamentals:
  • Analyzing and Mapping Processes: Understanding and mapping out the current process or workflow in a business.
  • APIs and Webhooks: APIs (Application Programming Interfaces) allow different software to share data. Webhooks are event-driven and listen for triggers.
  • Data Types and Transformation: Understanding data types (arrays, JSON) and how to transform them.
  • Database Management: Using no-code tools like air table or Google Sheets to store, retrieve, and update information.
  • Testing and Error Handling: Ensuring the workflow behaves as expected and handling unexpected events.

3. AI Agent Building

• Definition: Systems where large language models (LLMs) take charge of the workflow and decide themselves what to do to accomplish a task.
• Four Fundamental Parts:
  • Large Language Model (LLM): The brain of the agent.
  • Tools: Perform actions in software or include prompt chains.
  • Memory: Remembers past conversations and actions.
  • Knowledge (RAG): Access to a large amount of context.
• Multi-Agent Systems: Using multiple AI agents, each responsible for a single task, to automate more complex workflows.
• When to Use Multi-Agent Systems: Optimize each agent for a specific task, limit tools and sub-agents per agent, and limit manager agent responsibilities to team management.
• AI Agents vs. AI Workflow Automation:
  • AI Agents: Excel with dynamic inputs and open-ended problems, quicker to deploy, more expensive, more prone to errors, require more human oversight, reusable.
  • AI Workflow Automation: Good for predictable workflows, more reliable, fewer errors, cheaper to run, takes more time to set up, requires less human intervention.
• Current Best Use Cases for AI Agents:
  • Customer-Facing AI Agents: Text or voice-based agents for customer service or sales.
  • Internal Process Automation: Still in early stages, workflow automations are generally a better option for well-defined processes.
• AI Coding Agents: AI coding assistants that allow people to build applications and software with no coding background.

Marketing and Client Acquisition

• Key: Building an acquisition channel where you can consistently get clients.
• Focus: Stick to and focus on one acquisition channel in the beginning.
• Main Strategies:
  • Building a Personal Brand:
    • Channels: YouTube, LinkedIn, X, Instagram, Tik Tok, Medium.
    • Content: AI demos, AI workflow/agent giveaways, explainers/diagrams.
  • Cold Email: Using AI personalization to get high reply rates.
  • AI Communities: Being active in communities and reaching out to DMs.
  • Cold Calling: Effective if done right.
  • Event Speaking/Conferences: Great way to find leads.
  • Freelancer Platforms: Fiverr, Upwork, etc.
  • Ads: Instagram, LinkedIn, YouTube, Facebook ads to lead magnets.

Getting Started Today

1. Make a Commitment: Jump into the AI space now to become a top 1% expert.
2. Pick One or Two Automations: Build automations that are useful to yourself.
3. Join Communities: Get help from others who have faced similar issues.
4. Position Yourself: As an automation or AI specialist inside your company.
5. Add AI to Existing Services: For existing clients or as a consultant/agency owner.
6. Build Free Automations: For your network, friends, and colleagues.
7. Share Everything: On a social channel to build your personal brand.
8. Get Your First Client: Jump in and try to make it work, even if you haven't done that specific automation before.

Synthesis/Conclusion

The video provides a comprehensive guide to starting an AI business in 2025, emphasizing the importance of acquiring AI skills and effectively monetizing them. It outlines five key business models, including AI automation agencies, productized services, service as a software, AI SaaS, and AI education. The video also offers a crash course on three fundamental AI skills: prompt engineering, AI workflow automation, and AI agent building. Finally, it stresses the significance of marketing and client acquisition, highlighting personal branding as a particularly effective strategy. The key takeaway is that the time to enter the AI space is now, and by focusing on practical skills, strategic business models, and effective marketing, individuals can position themselves for success in the rapidly evolving AI landscape.