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.
The SaaS + AI GTM Playbook
In my earlier post, How to Monetize Your AI Roadmap, I laid out the ladder: Capability → Feature → Product → Digital Labor. This post is the GTM companion. As you climb that ladder, the go-to-market has to change.
If you treat AI as “just another feature launch,” you will ship plenty of capability and stall on revenue. This playbook is how to avoid that.
PMI in the AI Era: How to Make AI Acquisitions Actually Work
AI tuck-ins only create value when the acquired capability plugs into the core data model, workflows, and decisioning engine of the parent platform. Most fail not because the technology is weak, but because the PMI playbook was built for the cloud era, not the AI era. This post breaks down how AI PMI differs fundamentally from cloud PMI a decade ago. Done right, AI PMI turns small acquisitions into platform-level transformations, and translate to material enterprise value.
Why AI Value Creation and Value Capture Are Diverging
Are you building "Surface Area" AI or "System" AI? I analyze the 2025 State of AI reports from both Battery Ventures and McKinsey to show why most integration strategies fail and how the right M&A playbook can move you from “Pilot Purgatory” to durable enterprise value.