Engineering Go-to-Market Strategies
Cloudflare uses three AI agents to create weekly GTM summaries, demonstrating practical AI implementation in marketing workflows.
Four reps handled 10,000 inbound leads daily before an enterprise product shipped nine months later. A cookie banner library received most inbound leads from coding agents since April, prompting documentation changes. An internal GTM assistant answered one million questions for 6,000 sellers, with one in five trying it within two weeks.
What engineering go to market looks like, from ten teams: - Four reps and 10,000 inbound leads a day. Nine months later, when the enterprise product finally shipped, prospects said "you never got back to me." Automate the reply before you hire the team - A cookie banner library's number one source of inbound since April is coding agents recommending it. Coding agents never read the website for a library, they read node_modules, so the docs now ship inside the package - An internal GTM assistant has answered a million questions for 6,000 sellers. Two weeks after launch, one in five had tried it. The rule: 50 questions at 95% beats 100 at 70% - Ask an assistant to compare code intelligence tools and Sourcegraph comes up 65% of the time. Describe the pain it solves instead: zero mentions - Cloudflare's weekly GTM summary is written by three agents: one drafts from the data, one checks the draft against the data, one fixes the tone. The team read every run for two to three months first Watch the AI in GTM playlist: youtube.com/watch?v=wdTRsf… 💬 0 🔄 0 ❤️ 3 👀 565 📊 1 ⚡
