I Sold AI Automations for 749 Days: What Should I Have Known on Day 1?

TL;DR

A profitable AI automation agency sells measurable business outcomes, not impressive technology. After selling AI automations for 749 days and reaching over $170,000 a month, the speaker recommends mastering one platform for 90 full days, solving defined customer problems, and evaluating each system by its cost, deliverables, results, and return on investment. Read on for practical guidance on fulfillment, pricing, platform selection, and scalable delivery.

Transcript

i've sold a automations for 749 days and now i make over 170 grand a month here is literally everything that i wish that i knew on day one when i got started i've made basically every mistake under the sun and my hope is by doing so you guys aren't going to have to so i'm going to deliver this blackboard style in the way that i normally do let's st... Read More

Key Insights

• AI automation is a business model governed by the same core functions as other businesses: lead generation, sales, fulfillment, and retention. Its main distinction is the fulfillment process, which typically uses automated systems to solve a particular operational or commercial problem.
• Business value is more important than the technical sophistication of an automation. Clients care about the results produced, the cost of obtaining those results, and the resulting return on investment, regardless of whether fulfillment uses automation, human effort, or both.
• One platform is enough to begin delivering automation services effectively. The recommended approach is to commit 90 full days to mastering a single platform or solution, which can improve delivery speed, efficiency, consistency, and the client's experience.
• Client payments are tied to agreed deliverables and created value, not to the provider's effort. Taking a complicated route or spending extra hours on implementation does not automatically justify higher compensation when the original agreement and business outcome remain unchanged.
• Applied AI systems use technologies that other people have already developed. The agency's role is not necessarily to build or train an artificial intelligence model, but to incorporate available tools into systems that produce a demonstrable return for a business.
• A solution can only be sold when it addresses a defined problem. Searching for real business problems first, then selecting suitable templates or existing tools, is more commercially useful than choosing an interesting technology and later trying to invent a use case.
• Technology-focused agencies tend to produce complex solutions that require more delivery time while appearing less valuable to clients. Technical perfection and academically interesting systems can slow growth because clients judge the outcome rather than the sophistication of the implementation.
• A business-focused agency prioritizes simple solutions, real-world application, clear deliverables, client results, and return on investment. This approach supports faster growth and greater scalability because fulfillment is easier to repeat and the commercial value is easier to communicate.

Questions & Answers

Q: What should you know before starting an AI automation agency?

AI automation is a business governed by the same fundamentals as other businesses: lead generation, sales, fulfillment, and retention. Its distinctive element is fulfillment, where systems are built to solve particular business problems. Success therefore depends on business value and customer results more than technical sophistication.

Q: What are the four core functions of an AI automation business?

The four functions are lead generation, sales, fulfillment, and retention. Lead generation attracts prospects, sales converts them, fulfillment delivers the promised outcome, and retention keeps customers engaged. In an AI automation agency, fulfillment typically means building systems that address specific business problems.

Q: Why should an AI automation agency sell outcomes instead of technology?

Clients care primarily about results, delivery costs, and return on investment. They may not care whether the work is completed through an automated system, an offshore agency, human labor, or a combination of methods. Technical details matter commercially only when they clarify the value created for the business.

Q: How should return on investment shape an AI automation offer?

The offer should connect its cost to the financial result the client expects. The speaker illustrates this with questions such as how much a client will make after spending $1,000 or whether a $10,000 system can produce more than $80,000. The relevant threshold ultimately depends on the client's required return.

Q: How long should a beginner focus on one automation platform?

A beginner should pre-commit 90 full days, or three whole months, to mastering one platform or solution. Using that platform for later builds can improve speed, efficiency, consistency, and the client experience. The goal is to become capable enough to focus on business value instead of continually chasing new tools.

Q: Do you need to learn multiple AI automation platforms?

No; the speaker argues that learning one platform is sufficient to handle the fulfillment function. Platforms such as n8n, Make, Zapier, Lindy, Cursor, and VS Code have different advantages, but they broadly enable users to build systems that solve customer problems. Constantly switching tools can distract from mastering delivery.

Q: Does technical complexity justify charging more for an automation?

Technical complexity or extra effort does not automatically create more client value. Compensation should reflect the agreement, promised deliverables, results, costs, and return on investment. Taking an unnecessarily complicated implementation path does not by itself justify a higher price.

Q: How should an agency choose which automation to build?

Start with a defined business problem, then choose an existing tool, template, or platform that can solve it. Evaluate the expected deliverable, result, cost, and return before adding technical complexity. Simple, repeatable systems are easier to deliver and scale when they reliably create client value.

Summary & Key Takeaways

• AI automation follows the same basic structure as other businesses: lead generation attracts prospects, sales converts them, fulfillment delivers the promised outcome, and retention keeps customers engaged. The distinctive element is fulfillment, which usually involves building systems that address specific business problems. Established lessons about sales, marketing, and retention still apply.

• Clients primarily evaluate results, costs, deliverables, and return on investment. They generally do not care whether an outcome comes from a sophisticated automation, human labor, an offshore agency, or a blended approach. Technical explanations can therefore distract from the commercial case, especially when they do not clarify the financial value being created.

• A practical agency should master one automation or programming platform, apply existing technologies to real business problems, and favor simple solutions that work. Chasing every new tool, developing unnecessarily complex systems, or pursuing technical perfection increases delivery time and limits scale. A business-focused approach instead prioritizes repeatable fulfillment and demonstrable client results.