How OpenAI Used Outset to Inform Key Superapp Launch Decisions

Session Details: As OpenAI expands into increasingly complex B2B and enterprise use cases, the challenge is no longer just building powerful AI systems. It is ensuring they are understood, adopted, and correctly positioned in the market. This session explores how Scott Garrison, market research lead at OpenAI, is using structured qualitative research to directly inform both product naming and the development of OpenAI’s emerging “superapp.”

A key enabler of this work is Outset, which allows the team to rapidly recruit participants across diverse B2B audiences and gather feedback at a scale and speed that traditional research methods cannot match. By removing recruitment and coordination bottlenecks, Outset compresses research cycles from months into days and enables faster iteration and more responsive decision-making.

This accelerated feedback loop allows OpenAI to test language, positioning, and early product concepts directly with target users. The result is a tighter connection between customer insight, product naming, and product development, ensuring that both product strategy and messaging are grounded in real-world understanding rather than internal assumption.