GTM AI Council

A governed AI system for GTM decisions.

It turns public expert evidence and company inputs into evaluated recommendations with citations, assumptions, risks, and approval points.

The important part is not that the system keeps moving. The important part is that it knows when to stop.

What is it?

A bounded workflow graph, not an autonomous agent.

It retrieves, routes, evaluates, repairs, and pauses before spending or publishing.

Operating model

01InspectRepository state and prior work.
02RetrievePublic transcripts only when approved.
03ReasonRoute evidence into specialist skills.
04EvaluateCheck routing, cards, coverage, and artifacts.
05StopRepair, approval, or complete.
OutputWhat the system has to preserve
Executive memoRecommendation, decision logic, counterargument, and action.
EvidenceSupporting creators, knowledge cards, source URLs, and timestamps.
RisksUnsupported claims, weak assumptions, missing data, and governance concerns.
ApprovalHuman checkpoints before paid retrieval and publishing.

What comes out?

An executive artifact with receipts.

Facts, assumptions, unknowns, risks, and approval points are kept separate.

How it works

GTM makes AI harder because the problem rarely belongs to one function.

One business system.
ProductWhat can honestly be said.
PositioningWhy it matters.
SalesThe live conversation.
SuccessThe promise tested.
OperationsThe workflow underneath.
RevOpsThe fields where reality leaks.
Sales sees one version. Marketing sees another. RevOps sees the mess in the fields.

Why I built it

Most companies think the AI project starts with the model.

I used to think the main challenge was getting AI to produce better answers. I do not think that anymore.

The harder problem is deciding what deserves an answer in the first place.

What changed

01FindWeak evidence or weak logic.
02PreserveKeep the prior version.
03RepairChange the failing part.
04RetestResume only after the gate passes.

What failed?

The first routing system looked good until I tested it properly.

It was wrong more often than I was comfortable with. Later, a quality gate passed the average while six new RevOps cards failed individually.

Current evidence

EvidenceCurrent record
Completed runrun-20260802-213208-bcbf32
Sources46
Chunks723
Knowledge cards46
Final knowledge-card score4.45 / 5
AI-Enabled Revenue System artifact score4.3 / 5
Advisor Marketing Partners assessment score4.12 / 5
Provider requests in completed run6
Unsupported claims in evaluated artifact0

What remains unproven

The Council has not been deployed across an enterprise team. It does not prove commercial impact or productivity gains. It still needs richer human gold-label evaluation and a production privacy model before handling sensitive company data at scale.