Why Most AI Roadmaps Are Backwards
In a previous post, How to Monetize Your AI Roadmap, I introduced a simple framework for understanding why most AI initiatives stall before they ever generate real revenue.
This piece picks up where that framework leaves off.
It argues that the root cause of the AI revenue gap is sequencing. Most teams design AI roadmaps starting with features and work backward to monetization. The teams that succeed reverse the order. They start with the outcome they want to own, define the pricing unit that captures its value, and only then design the roadmap.
This post explains why feature-first roadmaps fail, what pricing-first roadmaps look like in practice, and why this distinction increasingly determines product success, valuation, and M&A outcomes in the AI era.
How to Monetize Your AI Roadmap: A Framework
AI adoption is accelerating, at a faster rate than AI revenue. This article introduces a four-level framework for AI monetization, spanning Capability, Feature, Product, and Digital Labor. Using real-world examples from Zoom, GitHub, Intercom, and Klarna, it explains why most AI initiatives stall at the lower levels, how pricing power correlates with workflow integration and economic ownership, and what it takes to move from bundled functionality to outcome-based revenue. The result is a practical guide for product leaders, operators, and boards trying to turn AI investment into durable business impact.