Most B2B proof-of-concept projects fail not because the technology doesn't work, but because no one validated whether the client was willing to bear the internal cost of solving the problem they just discovered.

Your POC worked perfectly. The technology performed. The data confirmed your hypothesis. The client nodded along in the final presentation.

And then nothing happened.

If this sounds familiar, you're not alone. After working with dozens of B2B startups navigating enterprise sales cycles, I've observed a pattern so consistent it deserves a name: the Successful Failure.

The POC technically succeeds. The commercial outcome fails. And founders are left wondering what went wrong.

Here's what went wrong: you validated the wrong thing.

Most businesses chase growth the hard way. They obsess over customer loyalty, lifetime value, and retention while ignoring the fundamental laws that actually drive sustainable expansion.

The result? Wasted budgets, stalled growth, and missed opportunities.

My previous article, "The AI-Native Paradox," explored how AI has created new challenges for both VCs and founders. But there's a deeper issue we need to address: the metrics we use to measure success are broken.

The same forces that make AI startups hard to evaluate and differentiate have also made traditional software metrics useless. ARR growth rates, churn calculations, and unit economics—the foundation of SaaS investing—don't work anymore.

This isn't just about tweaking formulas. We're witnessing a complete metrics revolution that demands new frameworks for measuring AI startup success. As we've explored in our work on corporate innovation in the AI age, what new metrics or evaluation frameworks are needed to assess the real potential of AI-native startups and solutions?