Client Success
Global Insight, Local Nuance—Mixed Methods Research on AI Tools
An enterprise tech client needed to deepen its understanding of global user perceptions around its AI-driven features. With expansion in mind, the company sought to identify adoption barriers, trust issues, and regional expectations across key markets—specifically the U.S., U.K., and Japan.
The goal: use real-world insight to inform product development, go-to-market messaging, and user engagement strategy
Challenge
Segment designed and executed a two-phase international mixed methods study. First, we conducted in-depth interviews with 8 users in each region to explore their attitudes, emotional responses, and experience with AI tools—surfacing regional trust issues, expectations, and language nuance. These findings shaped a targeted quantitative survey deployed to 1,200 respondents across all three markets, measuring AI awareness, perceived value, usability, and trust.
By combining qualitative depth with quantitative validation, we were able to identify both global commonalities and market-specific insight—informing product decisions, messaging strategies, and feature adoption playbooks.
Approach
The research uncovered significant cultural and emotional differences in AI perception across the U.S., U.K., and Japan—insights that would have been missed with a single-method approach. The client used these findings to tailor onboarding flows, refine product messaging, and design AI features that better addressed local user concerns.
The result: higher regional adoption rates, stronger user trust, and improved satisfaction metrics across markets.
Impact
Why our work matters
for Global Insight Leaders
We combine methodologies to deliver rich, decision-ready international insight.
We reduce risk when scaling into global markets—aligning product and message to regional needs.
for Product & Innovation Teams
for Strategy & Expansion Leaders
We help localize AI-driven solutions with human-first, culturally fluent research.
How can we support you?
