Essays on what happens when AI meets revenue strategy and why the leaders who evolve now will be the ones still standing when the dust settles.
How sales teams, sales leaders, and the seller's daily motion are being reshaped by AI. The people side of the transformation.
The seller is not going away. The seller is evolving. Agents handle the volume. Humans handle the judgment. A blueprint for what the CRO's hiring math, comp design, and org structure actually look like when AI absorbs the operational overhead.
Revenue architecture requires two things rarely found in the same person: frontline credibility and systems thinking. The companies that find both will pull away from everyone who doesn't. A profile of the leader built for what's next.
AI companies are hitting $100M ARR in 18 months. The GTM motion that got them there will not keep them there. Speed to revenue and durability of revenue are built by different systems. Here is what changes when you design for usage instead of seats.
The instrumentation layer. How RevOps, Sales Ops, Enablement, and GTM strategy operate as a connected system. And where AI changes each one.
Closed deals mean nothing without usage. AI companies that design comp, build GTM around outcomes, and invest in implementation will own the results. A revenue architecture for tying seller incentives to the metric that actually predicts renewal.
Sales ops is being restructured by AI. Not automated away, but fundamentally reoriented. Where human judgment still wins, where agents take over, and how the ops function evolves in between.
Enablement has always been reactive. Built on slides, scattered in Notion, lost in Slack. What it actually deserves is an agent that surfaces the right context at the right moment in the deal.
The hardest part of AI transformation is not the technology. It is the change management. How to restructure a GTM org for AI, sequence the transition, and avoid the failures that kill most deployments before they compound.
The technical layer. How to actually build, structure, and deploy AI agents for revenue teams.
From knowledge structure to prompt design to deployment. Most organizations get this wrong and spend months on repeatable asks and optimizing prompts. The real investment is the knowledge architecture underneath. This is the blueprint.
The .md file is not documentation. It is the cognitive architecture. Most organizations get this wrong. They spend cycles tuning prompts and chasing hallucinations instead of investing in the structured knowledge that makes the agent reliable. This is how to write the brain.
Real implementations. What was built, what broke, and what the results mean for operational leverage.
A real account of replacing a quality review process that scaled with headcount into one that scales with intelligence. What broke, what was built, and what it means for how we think about operational leverage.
The macro view. Strategic positions on where AI meets enterprise revenue. The arguments that frame everything else.
Capability is what your system can do. Reliability is what your system does at 2am when conditions shift. Most AI evaluation only measures the first one. The gap between passing a benchmark and surviving production is where millions in litigation, brand erosion, and real human safety live.
Sales executes. GTM decides. RevOps enforces. In AI-native companies, RevOps becomes the control surface for capital efficiency. The thesis for why the CRO who instruments RevOps for AI compounds advantages that take years to replicate.
The AGI race dominates the headlines. But the real leverage for revenue operators is vertical AI. Purpose-built, context-aware, and deployable now. Why smaller and specific wins.