Introducing a new series about how OpenAI runs on its own technology.
Chief Commercial Officer, Giancarlo “GC” Lionetti, kicks off our series about how OpenAI is building its own solutions on our technology.
AI has moved beyond an experiment. It now operates as infrastructure for work, shifting from pilots to systems that shape daily decisions. While our models improve in speed, cost, and capability, adoption rarely moves in a straight line. Deployments often outpace the change needed for organizations to leverage this technology.
Inside OpenAI we see the same tension. Running our business on AI means facing the questions every customer asks: where to start, how to align new tools with existing workflows, how to measure progress as the ground shifts. When I meet customers, the question they all ask me is, “How does OpenAI use OpenAI?”
Our approach is to treat AI as a practice that elevates craft.
Every company depends on expertise. The salesperson who builds trust, the support lead who solves the hardest problem, the engineer who finds order in complexity. AI encodes that expertise and distributes it across teams, scaling the impact of each discipline.
This is how we build. Our GTM, product, and engineering teams study their everyday workflows, define what good looks like and deliver changes in weeks instead of quarters. We decided to focus on a few high-leverage systems with outsized impact. Each team tests them in live deployments, building the same muscles our customers do.
Today we’re launching OpenAI on OpenAI, a series that shows how we use AI inside our business. Each story covers a real problem and the solution we built. Our goal is to share patterns companies can adapt.
We start with a few examples:
- GTM Assistant: a Slack-based tool that centralizes account context and expert knowledge. It streamlines research, meeting prep, and product Q&A, boosting sales productivity and improving outcomes.
- DocuGPT: an agent that converts contracts into structured, searchable data. Finance teams use it for faster, more consistent review at scale.
- Research Assistant: a system that turns millions of support tickets into conversational insights. Teams surface trends and act on customer feedback in minutes, not weeks.
- Support Agent: an operating model built on AI agents, continuous evals, and dynamic knowledge loops. It turns every interaction into training data, raises quality, and positions reps as system builders rather than ticket handlers.
- Inbound Sales Assistant: a system that personalizes responses for every lead, answers product and compliance questions instantly, and routes qualified prospects to reps with full context. It turns missed opportunities into revenue.
A preview of the future of work
Every company has craft. AI scales it. The future belongs to organizations where employees capture their expertise and distribute it across the company. The companies that marry craft and code will set the frontier.
If you’d like to learn more, we’d love to connect. Join us at DevDay on October 6, with technical resources to follow soon after.
