Where does AI create useful work in a revenue org?
I wanted a decision base I could trust, so I built one. It covers the GTM roles, the RevOps functions underneath them, the tools they use and the controls an agent needs before it touches company data.
Each card has a stable ID, dated sources and an honesty label for analysis or hypothesis.
A claim carries its own receipt.
Inside the source library, each claim is tiered, dated and linked. Vendor numbers keep the vendor label. Anything I worked out myself is marked as analysis. The links below are a small public sample.
Original evidence
Official documentation, filings, research papers, legislation and first-party datasets.
Named experience
Credible operator accounts, technical writeups and direct interviews with clear provenance.
Use with care
Consultancy synthesis, vendor benchmarks and secondary reporting that helps frame a question.
The database runs past RevOps.
Revenue decisions depend on the teams around them. The research follows those handoffs instead of stopping at the sales org chart.
Role packs
Week-in-the-life, KPIs, AI use cases, tooling and sources for the people doing the work.
RevOps function packs
The operating spine: planning, forecasting, systems, insights, deal desk and incentives.
Cross-cutting cards
Agent engineering, AI governance and payments evidence, kept separate from the role research.
Training modules
Practical AI use, change management, enterprise controls and paths for leaders and teams.
Six decisions worth funding first.
This is my analysis of impact, buildability and evidence quality, last checked on 12 August 2026. Two are build specs. Four are buy evaluations.
CRM hygiene agent
Start with a read-only scan. The scan earns the right to build the agent.
Expansion signals
Account-grain product telemetry is the gate. Human owners run every play.
Trust centre
Vanta when compliance automation matters; Conveyor for the narrower use case.
Questionnaire auto-fill
SiftHub leads the current evaluation. Test it on a real SIG and CAIQ set.
AI forecast
Gong Forecast when Gong already sits in the stack. Fix the data layer first.
Enrichment
Clay Growth with named providers inside it, checked on real coverage and unit cost.
Public work comes out of a private repository.
The research contains company-shaped questions and a private opportunity backlog. I publish the method, the patterns and the tools without publishing internal context.
That gives the work somewhere solid to come from. It also stops a directionally interesting vendor number turning into a confident LinkedIn fact by the time it reaches the feed.
- working browser tools
- operator writeups on LinkedIn
- practical guides and templates
- sourced findings with caveats