How OpenAI is using AI internally
The one thing you need to know in AI today | AI Ready CMO
There’s something almost comical about the current state of AI news: OpenAI quietly published detailed case studies on how they actually use their own technology internally, and basically no one noticed. These dropped about a week ago with minimal fanfare. No press release storm, no business influencer threads, no keynote spotlight. Just a few blog posts tucked into their site (under the “API” tag, for whatever reason), while everyone was busy dissecting whatever Sam Altman tweeted that morning.
Which is a shame, because this is arguably the most valuable thing OpenAI has published for practitioners in months. Forget the benchmarks and the demos—this is the recipe book from the restaurant kitchen. How does the AI company run on AI? What actually works when the cameras are off and the quarterly targets are looming?
The GTM article says OpenAI’s sales team now exchanges 22 messages per week with their AI assistant. Not “tried it once and gave up” numbers—22 messages per week, per rep, so daily habit territory. On average, they see a 20% productivity lift, roughly one extra day per week to actually talk to customers instead of hunting for context across seventeen tabs. But here’s the part that matters: they built it withtheir top reps, not for them. The best salespeople shaped what “great” looks like, and that expertise got encoded into the system.
The inbound sales assistant story follows a similar pattern but solves a different problem: what do you do when thousands of qualified leads flood in and you can only talk to a fraction? Most companies would hire frantically or let opportunities rot. OpenAI built a system that responds in minutes, in the prospect’s language (literally—if you write in Japanese, you get Japanese back), with 98% accuracy. They started at 60%, got there through tight feedback loops with reps, and unlocked millions in ARR from a channel that was previously a dead end. The lesson isn’t “build a chatbot.” It’s “build a learning system where your best people continuously improve the machine.”
Even the support piece is worth studying. They’ve repositioned support reps as “systems thinkers”—people who flag patterns, propose classifiers, and prototype automations. Every conversation becomes potential training data. Every rep is effectively a product designer. It’s an entirely different mental model from “clear the ticket queue faster,” and it only works because they built tight eval loops where quality is measurable and improvements compound.
Huge kudos to OpenAI; they are dogfooding their own product, learning in public (sort of), and treating internal operations as a design problem, not just an efficiency problem. If you’re building AI into sales, marketing, or support, this is your field guide. Not because you should copy their tools, but because you should steal their process: start with your best people, build tight feedback loops, measure obsessively, and treat AI like infrastructure that gets better with use. That’s the unlock.
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