Thinking on GTM systems, revenue operations, and agentic execution.
The GTM Attention Crisis
Pipeline is downstream of attention, not intent. GTM has become an industry of producing signals while ignoring the scarce resource that turns signals into money: human attention, applied with judgment.
Why Your Forecast Is Wrong Every Week
Forecast accuracy is not a data problem. It is a decision architecture problem. If everyone is working from different inputs with no shared logic, no amount of tooling fixes the variance.
What Makes an AI Agent Actually Trustworthy
The bar for trust in GTM is not "does it work?" It is "can I explain what it did and why, reverse it if needed, and sleep at night?" Most agents fail this test. Here is the design pattern that passes it.
The Signal-to-Action Pipeline
A useful GTM system does one thing well: it converts signals into actions that humans trust. That implies a pipeline with clear stages: capture, normalize, rank, route, compress, act, and learn.
Designing for Decision Flow, Not Data Flow
Most GTM stacks are designed to expand. More sources, more enrichment, more automation. The result is a machine that generates work about work. The right design principle inverts this entirely.
Weekly thinking on AI and revenue operations.
One piece of thinking per week. No filler. Patterns from live engagements, frameworks that work in practice, and early reads on what is changing in B2B GTM.
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