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 feedback is useless.

Not because people lack good intentions. Not because organisations don't invest in training. But because we've been taught to give feedback in ways that trigger defensiveness, focus on personality rather than behaviour, and leave people with nowhere to go.

A colleague recently shared notes from a roundtable in Paris bringing together VCs, Operating Partners, and key players from the French startup ecosystem. The conversation, by all accounts, was sophisticated. Concrete ROI metrics. Honest acknowledgments that "capital alone isn't enough." Thoughtful discussion of AI's impact on productivity and talent.

Reading through the summary, I was struck not by what was discussed, but by what wasn't.

One question was conspicuously absent:

If I had to start a VC or Operating Partner function from scratch today, knowing what I know and accounting for the three-year trajectory, what would I fundamentally do differently?

Instead, the discussion centred on incremental improvements to existing models. Adding GPT wrappers to knowledge bases. Thinking about AI for network matching. Debating batch formats versus continuous intake.

This isn't a criticism of that particular conversation. It's representative of where most of the ecosystem stands: mature enough for sophisticated execution discussions, not yet ready for uncomfortable structural questions.

This article is an attempt to ask those questions. Consider it food for thought.

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.

A follow-up to "Corporate Innovation in the Age of AI: Navigating the Hype, the Hypertail, and the Hard Limits"

In my previous piece, I explored how corporate innovation leaders face four key scenarios in the age of AI: the "hypertail" overload of point solutions, the slow burn of transformation, regulatory compliance pressures, and talent bottlenecks.
While these strategic frameworks help navigate the landscape, they miss a more fundamental truth that's becoming increasingly apparent in boardrooms and innovation labs alike.

The real bottleneck isn't AI adoption—it's organisational readiness.

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?

The private equity industry has awakened to a harsh reality: financial engineering alone no longer creates value1. With elevated interest rates and historic valuations, the firms that will outperform over the next decade are those that can systematically transform portfolio companies into high-performance growth platforms1.

Yet there's a critical gap between recognizing this need and executing it effectively. Most PE firms are still trapped in what we call the Heroics Trap—relying on exceptional individual efforts, one-off initiatives, and unsustainable growth spurts rather than building the systematic engines that create repeatable, scalable value.

Whether you're a founder preparing for your next raise, a scaleup leader trying to crack sustainable growth, or a PE firm evaluating an acquisition—there's a good chance you're measuring the wrong things.

CAC. CLV. ROAS. Conversion rates. Retention.

These metrics feel rigorous. Investors ask for them. Boards track them. They fit neatly into financial models and pitch decks.

But they're built on a flawed assumption: that brands grow primarily through customer loyalty and retention.

They don't.

The AI-Native Paradox presents significant challenges for startup founders and corporate innovators in today's rapidly evolving technological landscape. However, I find that Wardley Mapping offers a powerful strategic framework to navigate these challenges by providing situational awareness and enabling more informed decision-making (it is a kind of spatial "Where to play? How to win?" imho).

Last week, I explored how the “AI-Native Paradox” is less about a specific industry and more about the phenomenon of AI enabling a surge of new players, each leveraging advanced tools to carve out novel niches and challenge incumbents. Nowhere is this more visible than in the Martech and AdTech ecosystems.