What are Distributed Research Teams?

For a long time, research tools were built around a simple assumption: that research lived within a single team.

That assumption no longer holds.

Today, product managers run discovery interviews. Designers test concepts. Marketers validate messaging. Founders talk directly to customers. Research didn’t spread beyond the role of researchers because of a strategy shift — it spread because teams needed answers faster.

There is no question: many more people now do research.

The harder question is whether the systems they’re using were designed for that reality.

When Access Scales, Quality Is the First Thing at Risk

Making research easier is relatively straightforward.

What’s difficult is making it reliable, consistent, and rigorous once it’s no longer owned by a single group of experts.

As research spreads across roles and teams, the same operational issues tend to surface:

• Inconsistent approaches to interviewing

• Limited visibility into spend

• Review processes that don’t scale

• No shared standard for how research is run

These aren’t failures of intent. They’re failures of tooling and structure.

Most research tools assume quality comes from who is running the study.

But once research scales, quality has to come from systems.

The Opportunity (and Responsibility) of AI-Moderated Research

AI-moderated research is what made this shift possible in the first place — and from day one, it’s something we’ve built with trust, security, and consistency as non-negotiables.

It allows teams outside of traditional research roles to run interviews, gather insights, and learn directly from customers without weeks of setup or constant facilitation.

But unlike static tools, AI systems interact, adapt, and make decisions in real time. As research expands beyond a single team, those interactions need structure to remain consistent across studies, teams, and use cases.

From the beginning, we believed that if AI was going to unlock research for more people, it had to be held to the same standards organizations already rely on — and extended thoughtfully as usage grows.

That belief is what shaped this release.

Building Guardrails Instead of Gatekeepers

We’re releasing a new set of features designed specifically for teams scaling trusted AI-moderated research beyond a single group of experts.

These updates make it possible to scale who can run research — without requiring constant oversight from a small group of specialists.

Starting this week, Outset now supports:

• [NEW] Approval flows based on role and cost, so the right people review the right work at the right time

• [NEW] Custom guide templates, making proven approaches easy to repeat

• Shared AI moderators, allowing teams to apply consistent standards across studies

• AI-Guide Companion, to get users from question to guide more easily, with research best practices built-in

• Global context for AI, keeping interviews consistent across projects and teams

Together, these changes move quality control out of individual projects and into the system itself.

They don’t slow teams down. They remove the need for ad hoc checks, duplicated effort, and manual intervention.

Because once more people do research, quality can’t depend on who happens to be reviewing a study that day.