Confluent GTM AI program built from zero to measured scale.
I built the program end to end and hired the team that delivered it: governance, frameworks, systems, context layers, RBAC, agent patterns, adoption, enablement, production handoff, and value measurement.
The mandate
Turn scattered GTM AI experimentation into a governed operating system that revenue teams could trust, adopt, and run. Experiments were spreading faster than shared ownership, context, review, and measurement.
What I owned
- Hired and led the program team
- Built governance, frameworks, context layers, and RBAC
- Defined agent patterns and the production path
- Created adoption, enablement, inspection, and measurement systems
One decision path
The point was not automation at every step. It was a path that could be inspected, challenged, and inherited.
Messy signal
A rep flags deal risk in a call note, forecast comment, or handoff.
Context gate
The system checks account, stage, owner, freshness, and source before it reasons.
Permission gate
RBAC decides what the agent may read, write, recommend, or escalate.
Human decision
The accountable operator reviews the evidence and makes the call.
Measured outcome
The action is logged against adoption, quality, time returned, and value.
Documented outcome
$12M annual value122 production agents. 190+ agents and automations across environments, including 20 multi-agent workforces.
Have a similar problem?
Bring the workflow, consequence, and sponsor. We will decide if this is the right kind of work.