I’ve been noticing AI showing up in more and more early-stage products, and I understand why. It can help speed up development, make products feel more personal, and help you learn faster.
But when I first explored it, I assumed you needed huge datasets, deep technical skills, and a big budget. That belief kept me from starting for a while. The truth? You can begin much smaller than you might think.
Here’s what’s worked for me and what I wish I’d known earlier:
1. Start with one meaningful problem.
When I looked at my own product ideas, I realized the best place to start was where people were getting stuck or frustrated. I asked myself: Could prediction, personalization, or automation make this part easier? Often, a small AI chatbot, predictive insight, or tailored suggestion is enough to learn a lot.
2. Lean on what’s already been built.
I used to think I had to create everything from scratch. In reality, pre-trained models, APIs, and no-code tools can get you moving for a fraction of the cost. Sometimes, a few hundred dollars is all it takes to try an idea.
3. Test before you invest heavily.
One of my biggest wins came from simulating an AI feature manually (“Wizard of Oz” testing). Users got value, I gathered insights, and I knew what to prioritize, all before writing a single line of AI code.
4. Keep your language simple.
I found that using the Goal → Input → Output framework made conversations with technical partners so much easier. No jargon, just clarity.
What I keep reminding myself:
You don’t have to launch perfect AI. You just have to launch something useful enough to learn from. AI is a tool, not the destination.
If you’re exploring this path too, I’d love to hear: What’s one small AI experiment you’d like to try in your product?
