TLDR;-) Large organisations don't resist innovation because they're broken. They resist it because they're working. Four mechanisms do the killing: procedural resistance, resource competing, standard dilution, passive waiting. Each is a legitimate organisational function operating in the wrong context. If the immune response is firing, you're probably working on the wrong layer.

Eighty-three per cent of companies rank innovation as a top-three priority. Three per cent are ready to act on it. That is not a typo. BCG's 2024 Most Innovative Companies report calls what remains "zombie innovation systems": organisations going through the motions of innovation without strategic commitment, waiting for certainty that will never arrive.

AI for VC & Founders: The playbook (dealbook?) has changed—and everyone is scrambling to keep up.

Truth is, most startups die.
— 9 out of 10 fail (according to Genome Project)
— 199 out of 200 (according to THNK & Deloitte Fast Ventures)
It’s the elephant in the room.

TL;DR: Most conversations about sustainable performance start from the wrong premise—that the performance standards themselves are neutral. They're not. Before optimising for sustainability, ask: whose definition of "good" am I trying to meet? The answer might explain why it feels so hard.

You're exhausted. Not the kind of tired that sleep fixes—the kind that accumulates despite doing everything right. The productivity systems, the boundary-setting, the rest. You've tried it all.

The advice you get assumes the problem is execution. Work smarter. Delegate more. Manage your energy better.

But here's what that advice never questions: the performance standards themselves.

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.

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?

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.