An AI operating system that converts product signals, buyer intent, and customer behavior into 90-day forward visibility across churn, expansion, and the full customer journey. Designed and proven inside a live GTM organization.
What you're looking at.
The AI GTM Operating System is a framework designed and built inside a live GTM organization. This page shares it completely: the architecture behind it, the results it produced, the workflows running today, and the governance model that sustains it at scale. Every section is grounded in something that was actually built.
AI is not a chatbot bolted onto your funnel. It is a loop that runs continuously across the customer journey: it surfaces the signal before the number moves, orchestrates the right motion, acts through people and agents, and refines from every outcome. That loop is the engine inside the operating system.
Leading-indicator health and engagement scoring surfaces adoption risk and expansion opportunity long before the renewal conversation.
The signal routes work to the right layer: a human, an agent, or a digital experience, matched to account complexity and value.
Intervention happens before the customer goes quiet. Automated where it should be, human where it matters.
Every outcome sharpens the model. The system gets more predictive with each cycle, compounding instead of resetting.
Senior judgment aimed where it changes the outcome: complex accounts, executive relationships, the moments a model can flag but not close.
Agentic workflows across onboarding, adoption, support, and review generation, delivering the motion at a scale headcount never could.
Self-serve and product-led experiences that carry the low-complexity journeys end to end, freeing the other two layers for what they do best.
A score without the data underneath it is just a guess. The real work is the signal architecture: product usage, customer conversations, buyer demand, and renewal context, fused into one model.
The score is the output. The architecture is the point.
Each lens reads High or Low. Eight combinations. One named diagnosis per account.
Renewal falls cleanly from 93% to 48% across the eight profiles. V1's single flag becomes eight actionable diagnoses.
The 0-to-8 scale stays. What changes is the diagnosis. An account at 3/8 used to mean at risk, act. Now it carries a named profile: is product engagement low while no buyer demand exists, or is the CSM relationship the only thread holding? Those are different conversations, different urgencies, different next steps.
A customer who logs in every day but tells their team the product is not working is a churn risk. A customer who barely logs in but calls their CSM a strategic partner will renew. Behavioral signals alone cannot distinguish those two accounts.
Gong transcripts, NPS verbatims, support tickets, and G2 reviews carry the words customers actually use. Combined with product and ROI signals, they turn a number into a diagnosis: not just risk, but why, and what to do next.
This is where the roadmap goes next: renewal-call sentiment, meeting acceptance trends, and multi-threading depth wired directly into the score, so prediction sharpens with every customer interaction.
Activation is the single strongest predictor of renewal. Not quarterly sentiment, not meeting cadence, not NPS. The behavior in the first 30 days determines whether a customer reaches renewal as an advocate or a churn risk. Every late-stage intervention is a failure of early signal.
Group onboarding ran at 35% attendance and was retired. Every customer now gets a named Onboarding Partner who owns the first 30 days end to end. Full activation is defined precisely: custom CTA live, 5+ reviews collected, ROI dashboard connected.
Every agent here went through the same door: a structured intake, a priority triage, an engineering build, a maintained deployment. This is what professional AI operations looks like. Not individuals running scripts from their own computers.
14 of 67 ideas became maintained, production-grade agents. The rest were triaged, deprioritized, or declined. Governance is the filter.
Every submission is scored by impact, complexity, and risk before engineering touches it. Ideas from the field. Accountability from the team.
Across 6 roles with named champions. Time freed from manual work, redirected to the moments that need human judgment.
Scaling AI across a GTM org does not happen through enthusiasm. It happens through a repeatable intake model: one front door, one priority framework, one engineering team that builds and maintains what the field submits. Three phases make it work.
Give every role a structured way to submit ideas, or AI work happens in shadow corners: undocumented, single-owner, invisible until something breaks. Access is a cultural signal that the field is the best source of what to build next.
Before you scale, you need to know what already exists. Most organizations have more automation than they think, and most of it is fragile, single-owner, and undocumented, which is how the same thing gets built six times by six different people.
Not every submission should be built. The governance layer triages by priority and risk, assigns ownership, and rebuilds approved workflows into a tested, documented catalog the whole org can trust.
I speak and write about what it actually takes to get AI adopted inside complex organizations.