OpenAI launches a series called "OpenAI on OpenAI" that explains, with real examples, how the company uses its own models to solve internal problems and scale everyday work. This is a look from the inside at how AI stops being an experiment and becomes operational infrastructure. (openai.com)
What is "OpenAI on OpenAI"?
It's a series of articles documenting internal solutions built with OpenAI technology, designed so other companies can learn repeatable patterns. The piece was published on September 29, 2025 and is presented by Giancarlo "GC" Lionetti, Chief Commercial Officer. (openai.com)
The central idea is simple: treat AI as a practice that elevates human experience, not as a magical black box. Why does that matter? Because transforming workflows means defining what "doing it well" looks like and then iterating quickly—in weeks, not quarters. (openai.com)
Concrete examples they share
OpenAI puts several practical cases on the table. Each one solves a real work problem and shows how the company integrates models with existing systems. Among the examples are:
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GTM Assistant: a Slack-based tool that centralizes account context and expert knowledge to improve meeting prep, research, and product questions. Clear impact on sales productivity. (openai.com)
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DocuGPT: an agent that turns contracts into structured, searchable data, aimed at speeding financial reviews and bringing consistency to the process at scale. (openai.com)
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Research Assistant: a solution that transforms millions of support tickets into conversational insights, letting you spot trends and prioritize actions in minutes instead of weeks. (openai.com)
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Support Agent: an operating model that combines AI agents, continuous evaluations, and dynamic knowledge loops. The result: every interaction feeds the system, raising quality and shifting the human agent role toward system builder. (openai.com)
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Inbound Sales Assistant: automates personalized replies to leads, answers product and compliance questions, and routes qualified prospects to reps with all the necessary context. It converts missed opportunities into revenue. (openai.com)
"AI encodes that expertise and distributes it across teams." That sentence in the article sums up the bet: AI captures and distributes human expertise to multiply its effect. (openai.com)
What will you learn if you follow this approach?
First, you don't need to reinvent the wheel. Look at processes that already work and ask yourself: what part of the knowledge do we repeat over and over? That's where the gain is. Second, useful projects start small and focus on high impact: sales, support, contract review. Third, practical implementation demands clear metrics and constant feedback cycles.
In practice, that means combining agent architectures, continuous evaluations, and sources of truth (for example, internal knowledge bases) so the system improves with each interaction. It's not just putting ChatGPT in front of a form; it's designing the whole flow.
How to start if you want to apply this in your company
- Define a concrete, repeatable task where knowledge is the key variable. Sales, support, or document review are usually good candidates.
- Map the current flow: who does what, what data they use, and what goal you measure. Without that map there's no verifiable improvement.
- Prototype quickly with an assistant or agent that accesses internal sources. Iterate with real users and simple metrics: time per task, resolution rate, perceived quality.
- Design loops so interactions serve as training and continuous evaluation data.
If you want technical resources and more examples, OpenAI invites you to connect at DevDay on October 6, where they promise technical content and materials for developers. (openai.com)
Final reflection
This series is useful because it brings the conversation down from the cloud to the workbench. It's not just about model capabilities, but about how to organize teams, flows, and metrics so AI actually delivers value. Can you imagine your area with the knowledge of your best employees available to anyone on your team in seconds? That's the practical horizon they describe here.
If you work in product, sales, or support, the key question is: what experience can you turn today into a reproducible, measurable service with AI?
