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Resume-backed operator case study

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.

From zero to measured scale

The program changed in three moves.

Build

I hired the team and built the operating model, governance, context layers, RBAC, agent patterns, enablement, and production path.

The system connected experimentation to decisions about priority, permissions, review, adoption, and value.

Evidence boundary: resume-backed operator case study. No client testimonial is implied.

The mandate

Turn scattered GTM AI experimentation into a governed operating system that revenue teams could trust, adopt, and run.

What I owned

  • Hired and led the program team
  • Built governance, frameworks, context layers, and RBAC
  • Defined agent patterns and production paths
  • Created adoption, enablement, inspection, and measurement systems

Documented outcome

$12M annual value

Across 190+ agents and automations, including 20 multi-agent workforces.

Decision trace

How I made the work reviewable.

01 / Messy signal

A rep flags a deal risk in a call note, forecast comment, or handoff.

The point is not automation at every step. It is a decision path that can be inspected, challenged, and inherited.

Failure modes

What the program had to survive.

What breaks

The agent sounds confident because the source is old.

Fix: Add freshness checks, source provenance, and a hard stop when the required context is stale.

This is the difference between shipping a demo and shipping something a team can own.

Evidence ledger

What is mine to claim.

01

Personal ownership

Built the Confluent GTM AI program from zero and hired the team that delivered it.

02

Documented scale

$12M annual value across 190+ agents and automations, including 20 multi-agent workforces.

03

Evidence boundary

Resume-backed operator case study; no client testimonial or confidential claim is implied.

04

What I will not claim

I will not manufacture a logo, quote, metric, or before/after story without permissioned source material.

Build journal

The operating choices behind the program.

01

The decision

Do not start with an agent catalog. Start with the expensive workflow and its owner.

02

The thing that broke

AI activity outpaced shared context, permissions, review, enablement, and measurement.

03

The repair

I connected intake, risk, access, human review, adoption, and handoff in one path.

04

The rule

If nobody can explain what changed, who owns it, or how it is measured, it is not ready.

What sat around the agents

The agents were only the visible part.

Layer 01

Governance

Decide what is allowed, who owns the decision, and when it must be reviewed.

The system is only complete when this layer has an owner and a way to inspect it.

Have a similar operating constraint?

Bring the workflow, consequence, and sponsor. We will decide if this is the right kind of work.

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