GTM AI Program: From Intake to Measured Scale

A governed applied AI program that turned a growing production estate into a measurable operating system for GTM teams.

Evidence type

Documented professional outcome

Role

Senior Manager, Applied AI & GTM Systems

Timeline

Feb 2026 – Present

Team

Distributed GTM systems, AI, data, and application engineering

Leadership layer

Stakeholder Map

Sales, Customer Success, Revenue Operations, and Marketing teams using AI workflows
Business and technology leaders responsible for value, data, and operating risk
Internal builders moving use cases from intake into production

Decision Tradeoffs

Kept local experimentation open while routing scale-worthy work through one accountable path
Balanced production scale with human review, adoption, and measurable business value
Centralized intake and standards without making the core team the only builders

Governance Model

Centralized intake, ROI sizing, agile backlog prioritization, UAT, production promotion, and lifecycle decisions
Established data controls, human-review requirements, health scoring, and iterate-or-sunset governance
Created shared build standards, documentation, and enablement cohorts for internal builders

Rollout Plan

Expanded AI workflows across Sales, Customer Success, Revenue Operations, and Marketing
Scaled work across three platforms rather than one tool-specific program
Connected the program to IBM integration work without publishing internal implementation detail

Adoption Strategy

Created an internal builder program so teams could experiment while scale-worthy work followed shared controls
Measured value, operating usage, quality, and capacity returned alongside technical delivery
Used human review, UAT, and enablement as adoption and quality gates

Architecture evolution

Before

No established GTM AI program or shared production path.
AI use cases needed a common model for ownership, controls, adoption, and value.

After

A governed program connected intake, delivery, monitoring, and ROI measurement.
The program reached 122 agents in the production environment and 20 multi-agent workforces, with a broader governed UAT estate.

The operating problem

Scaling AI across a revenue organization requires more than a collection of experiments. Use cases need accountable owners, permission-aware data, production gates, human review, adoption, and a way to measure value after launch.

The system

The program established one delivery path from intake into production and ongoing measurement. Prioritization, build standards, UAT, human review, health scoring, lifecycle decisions, and builder enablement were treated as one operating system rather than separate compliance steps.

The result

The program scaled to 122 agents in the production environment and 20 multi-agent workforces. The current resume documents $12M in annual value from $1.1M in tooling spend, while the operating model connected intake, governance, enablement, production promotion, and measurement.

Evidence boundary

The figures and program description on this page trace to the downloadable 2026 resume. Internal configurations, proprietary data, and employer-confidential implementation details are intentionally excluded.

GTM AI Program: From Intake to Measured Scale

Documented operating outcomes

$12M

122 production agents

20 workforces