OpenAI introduced an internal assistant to analyze large volumes of feedback and support tickets, designed so anyone on the team can ask questions in natural language and get actionable reports in minutes. Can you imagine not always depending on a data scientist to understand why users complain? This is exactly what they describe in their announcement on September 29, 2025. (openai.com)
What the research assistant is
It's not a mysterious tool nor a new dashboard that requires long trainings. It's a practical combination: dashboards and classifiers to spot patterns, plus a conversational engine based on GPT-5 that summarizes, explains, and generates reports in plain language. The advantage? You move from seeing numbers to understanding the why behind those numbers without writing complex queries. (openai.com)
"The magic is that you don't have to predefine your questions, you can just follow your curiosity."
That sentence appears in OpenAI's note and sums it up: you start with a visualization and then keep asking questions, without rigid paths or prior forms. (openai.com)
How it works in practice
Imagine millions of support tickets piled up: complaints, ideas, bug reports. Before, a deep analysis could take weeks and require an expert in SQL and models. With this assistant, OpenAI says the same questions return reports in minutes, with problem size, prevalence, and friction points highlighted. It's speed without losing context. (openai.com)
Inside the company they used it to spot an onboarding flow that was failing after the GPT-5 launch, prioritize fixes, and adjust the product roadmap. It also helped separate two truths: creative image-generation use by marketing, and simultaneous frustrations from rendering delays. Those findings turned into concrete actions. (openai.com)
Impact on teams and your day-to-day
This isn't meant to replace those who do deep analysis. Rather, it frees data scientists from repetitive tasks so they can build better classifiers, automations, and tools. Operational and product teams gain time to talk with customers and make faster decisions. In short: curiosity multiplied by speed. (openai.com)
What it means for your company or project
- Less waiting for insights: better-informed decisions in less time.
- More autonomy for non-technical leaders: you can ask in natural language and get a usable report.
- A shift in the operating model: instead of rationing analysis, any team can iterate with real feedback.
If you work in support, product, or growth, the key question is: what value would you unlock if your questions were answered in minutes instead of weeks?
Practical considerations and risks
This isn't magic without oversight. OpenAI points out the need to verify results and cross-check with internal sources before acting blindly. Trust grows with cycles of asking, checking, and adjusting. Also, integrating these assistants requires good privacy and data governance practices so analyses respect sensitive information.
If you want to read the original note and see the call to talk with their commercial team, here are more details: OpenAI — Empowering teams to unlock insights faster. (openai.com)
To close, think of this as an invitation to democratize curiosity in your organization. What questions would you stop postponing if your team could get actionable answers in minutes?
