Pricing your AI product: Lessons from 400+ companies and 50 unicorns

For the first time there's cost dynamics to actually navigate. So you need to think about monetization from day one. But there's also a more critical reason, which is value capture.

"Asset test" refers to Ramanujam's term for a simple validation exercise to test if your pricing strategy is clear enough.

The asset test that you probably should go back on Monday morning and do is take some of your early prospects or customers and ask them to articulate the pricing strategy back to you.

To stop churn, you need to attract customers who won't leave. That sounds counterintuitive, but that's the best way to actually stop churn.

You need to be able to create needs rather than just discover them.

Ramanujam earlier described a two-by-two framework with quadrants based on market share versus wallet share, where outcome-based pricing sits in the top-right quadrant.

About 5% of companies are probably in a true outcome-based pricing model. If you want to win in AI, figure out a way to get to that quadrant.

If you have options on the table, let's say if you have a good, better, best, if you're a hundred K product, a 200K and a 300K option, then you're not just talking price, you're talking value.

20% of what you build drives 80% of the willingness to pay. But the irony is that that 20% is the easiest thing to build often.

The winners in AI will need to master monetization from day one. If you're bringing a lot of value to the table and you start at training your customers to expect $20 a month and you anchored yourself on a low price point, you're in trouble.

The right way to think about an ROI model is to actually co-create it with your customers from day one, which means agree and validate on the assumptions and the inputs.