AI Innovation!?
The failure is not in the technology. It is not in the strategy. It is in the classification: the pre-strategic decision about what kind of problem this is.

Every corporate AI initiative has two identities. What the organisation approved, and what the initiative actually requires. The gap between these two is the misclassification, and it cascades through every downstream decision.

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?