Ria Shah is Growth Lead at Listen Labs, an AI-first customer research platform building the future of qualitative research at scale. She holds a B.A. in Economics from The Johns Hopkins University, with early research roots in public health and behavioral data, including community-based work in West Baltimore studying the relationship between language, trust, and health outcomes. Before joining Listen Labs, Ria was Co-Founder and CEO of Toni, an AI interviewing platform backed by Pioneer and Daniel Gross that served organizations including the Chicago Bears, Women in Product, and All Raise, and was acquired in 2025. Prior to founding, she spent over four years as a management consultant at CVP, where she became the youngest manager in company history and closed $90M in new growth. Across her career, Ria has been driven by a single through line: the most valuable human insights live in conversation, and the tools to capture them at scale are only now catching up.
Sessions
Jun 24
9:35 AM - 9:55 AM GMT+2
Blue Stage
AI for Qual Research
Decision-Making
Innovation
Research Automation
Touching Grass at Scale: How AI-Moderated Research Put the Voice of the User in Every Room
Headway came to Listen Labs with a hypothesis: therapists would resist AI. The research said otherwise. 80% were already using it. This session breaks down how Headway ran six AI-powered studies, from provider adoption to children's intake flows, and turned unexpected findings into product decisions that moved the needle.
Key Takeaways:
AI moderation can unlock more honest answers -- Clinicians and parents opened up more, not less, in an async, low-pressure format. For sensitive topics, removing the human interviewer reduced social-desirability bias.
Speed at this scale changes how research gets used -- When you can deliver provider insights in under 12 hours, research stops being a "we didn't have time" excuse and becomes a default input to real decisions.
Democratizing research requires guardrails, not just tools -- Researcher craft moves upstream into study design. AI moderation is powerful for exploratory and evaluative work, but standards and review are what keep quality from eroding as more teams run their own studies.
