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.

The private equity landscape has fundamentally shifted. With interest rates elevated and valuations at historic highs, the era of financial engineering as the primary value creation lever is over. Today's outperformance comes from execution capabilities—specifically, the ability to transform portfolio companies into high-performance growth platforms through operational excellence.

Yet most PE firms are still approaching marketing and digital transformation with outdated frameworks. They're optimizing for performance marketing metrics when they should be implementing Investment Marketing—treating every marketing dollar as capital allocation with measurable enterprise value impact.

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.

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

“The law of floatation was not discovered by contemplating
the sinking of things, but by contemplating the floating of things which floated naturally, and then intelligently asking why they did so.” (Thomas Troward)

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.