Blog — page 14
Notes on
agentic GTM.
Short arguments about how revenue work is changing — and what it takes to run agents responsibly once it has.
Posts, page 14
How to build a post-pilot re-engagement RevPlay
A pilot ending is a trigger. A repeatable motion needs to say who qualifies, what happens next and where a person decides.
ReadHow to give an AI messaging sequence one clear job
“Follow up” describes a genre. A useful messaging framework tells the agent what outcome each step is supposed to create.
ReadManaged RevOps vs. RevOps consulting: who owns the work after the strategy?
The deciding question is who owns reliable daily execution after the recommendations are delivered.
ReadCRM hygiene for AI agents: 8 checks before you automate revenue work
When agents can read and write revenue systems, clean CRM data becomes an operational safety control.
ReadSystem of record vs. system of action: where revenue AI agents belong
Keep the CRM as the governed record; use a managed operating layer to coordinate context, action and recovery.
ReadFrom AI agent pilot to production: a 90-day RevOps rollout plan
Narrow the scope, prove context and controls, rehearse exceptions and assign an operator before widening autonomy.
ReadHow to measure revenue AI agents: 12 metrics beyond activity
Measure the chain from eligible work to trustworthy completion, human control, recovery and commercial outcome.
ReadAI agent governance for RevOps: a practical framework
Permissions, approval thresholds, audit evidence and rollback make agentic GTM scalable without removing human judgment.
ReadRevenue AI agents vs. automation: what to use where
Choose the operating model by ambiguity, data access, action risk, reversibility and required human judgment—not by hype.
ReadSee how the work gets done.
Explore how agents prepare work across your revenue teams, and how people review it, guide it and decide what happens next. Fictional data. Real product.
Explore the demo