N=1 Then Scale
The most credible thing you can say when you’re selling an AI solution is: “I use this. My own business runs on it. I’m the guinea pig, and it works.” That’s the n=1 principle. Before you try to package something for others, make yourself the first test case. Your company, your workflow, your actual problem. Build the solution there. Prove it. Then talk about scaling.
This matters for a few reasons. The obvious one is credibility. How do you sell something you’re not using? You can’t, not with integrity. People can tell the difference between someone who built something in a lab and someone who built something because they desperately needed it themselves. The second reason is more practical: you learn things in the real deployment that you never would have anticipated in theory. The edge cases, the failure modes, the moments where the AI confidently does the wrong thing. You need to discover those on your own company before you’re responsible for someone else’s.
There’s also something clarifying about the framing. Your company on its own is making money. The AI work isn’t the business, it’s gravy. The core is: I’m doing my job better. The secondary benefit is: the things I figured out while doing my job better are now a product I can offer to others who have the same problem. That sequence matters. The tail doesn’t wag the dog. You serve your existing business first, and the new opportunity emerges from that.
What I love about this approach is how it naturally produces niche expertise. If you run a security company and you build an AI system to track staff performance and client satisfaction, you now have something no generalist AI engineer could ever build. You have domain knowledge baked into the solution. You know exactly what field supervisors need at 2am and what clients actually complain about and what data matters. That specificity is the moat. It’s not replicable without going through the same real-world experience you went through.
Key Takeaway
Make your own business the first laboratory: prove the technology works on yourself before you offer it to anyone else, because that’s where both the credibility and the real learning come from.