How to make AI profitable in existing processes?

TL;DR

AI is a tool to improve profitability of existing, validated processes, not a creator of new opportunities. Focus on adding AI to deliverables in high volume, low value contexts to improve margins, and use distribution as the main moat rather to rely on the product alone. Exercise caution with high value, low volume use cases where AI may erode margins.

Transcript

If you're here, you probably want to make money with AI. I've sold several million dollars worth of AI products and services over the course of the last couple years, and I can confidently tell you most approaches on the internet are about as useful as a wet blanket. They're total dog water. So, in this video, I want to cover three ways to actually... Read More

Key Insights

• AI does not create impossibilities; it makes them profitable by speeding up work and improving margins.
• Focus AI on existing profitable processes rather than building new products from scratch.
• High volume, low value opportunities benefit most from AI due to favorable unit economics.
• Low volume, high value deals can suffer from nuance loss and reduced conversion when fully automated.
• Material gains come from increasing output frequency and reducing headcount needs through AI integration.
• Personalization and AI have improved profitability but can saturate as markets learn AI patterns.
• The real competitive advantage shifts toward scalable distribution rather than product differentiation alone.
• Avoid deploying AI in roles where marginal gains do not justify the cost or risk of missed nuance.
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Questions & Answers

Q: What is the core idea behind making money with AI according to the video?

The core idea is to take an existing profitable process and add AI to it to speed up delivery and improve margins, rather than trying to create new AI products. AI is framed as a tool to improve profitability of validated processes, enabling faster output and reduced headcount while preserving or increasing revenue.

Q: Why should you not disrupt existing services with new AI products?

Because the video argues that AI does not fundamentally create new opportunities but makes current ones more profitable. By embedding AI into proven deliverables, you can scale and optimize margins without risking the disruption that comes with entirely new, untested products.

Q: In what type of business does AI work best, according to the talk?

AI works best in high volume, low average order value industries where many repeat, smaller transactions allow the cost savings from automation to compound into significant overall profits.

Q: What is a potential risk of using AI for high value, low volume deals?

The risk is losing a large portion of revenue if AI reduces the conversion rate even slightly, since high value deals rely on nuanced human judgment and personalized handling, and the math of big deals magnifies small percentage drops.

Q: What does the speaker say is the only real moat left for businesses in an AI world?

The speaker argues that distribution is the only real moat left. If AI can generate products quickly, the unique value becomes reach and access to customers, not the product itself.

Q: What example does the speaker use to illustrate AI increasing profitability through better margins?

The speaker cites a copywriting company that scaled to nearly $100,000 per month by embedding AI into the deliverable process, allowing faster content production and enabling a higher delivery rate without proportionally higher headcount.

Q: What cautionary guidance is given about AI deployment in sales processes?

The guidance is to be intelligent about where AI is applied, specifically avoiding fully automating high value, nuanced sales interactions and instead targeting scalable, repetitive, low value steps where AI clearly enhances efficiency and margins.

Q: How does the video describe the impact of AI on personalization tasks?

AI makes deep personalization feasible at scale by autonomously handling research and tailoring outreach to many contacts, such as personalized emails, which used to require extensive manual effort and time, now accomplished much more quickly and cost-effectively.

Summary & Key Takeaways

• AI boosts profitability by enhancing already proven processes, not by creating new markets. The approach is to embed AI into delivery workflows to speed up output and improve margins, as demonstrated by converting a copywriting service into a faster, more scalable operation.

• AI excels in high volume, low average order value scenarios, where the math favors automation over human labor, but it struggles with low volume, high value deals due to potential misses in nuance and conversion rates.

• The only durable moat left is distribution, since in a world where prompts can generate products quickly, the unique value lies in delivery reach and market access rather than the product itself.