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AI Matching Strategy 
Global consumer platform

AI Matching Strategy
Global consumer platform

A global consumer platform was exploring a new AI-driven approach to matching users.

I led foundational research to define how users interpret and respond to AI-driven matching experiences, informing product direction during early development. This work identified critical gaps between user expectations and system capabilities, helping the team align the product experience with evolving AI functionality.

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Details have been generalized to protect confidential product and business information.

Challenge

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The team was developing a fundamentally new interaction model while underlying AI capabilities were still evolving.

  • Users had high expectations for AI-driven experiences

  • Multiple product directions were being explored in parallel

  • The experience communicated capabilities that were not yet fully supported

  • Misalignment between product promise and system behavior risked eroding user trust

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How do users interpret AI-driven matching—and how does that align with what the system actually does?

Key Activities

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  • Led foundational mental model research to understand how users interpret AI-driven matching experiences

  • Identified gaps between product positioning and actual system capabilities

  • Recommended a phased approach to align user experience with evolving AI functionality

  • Conducted follow-up research to evaluate naming, positioning, and explanatory language

  • Partnered closely with design to refine concepts and test variations

Methodology

  • Mental model study (n=8 IDIs, U.S.) exploring interpretation of the experience without explanation

  • Follow-up AI-moderated study (n=50, U.S.) to evaluate positioning and language

  • Comparative testing of product framing and terminology

  • Mixed-method synthesis connecting qualitative insights to product decisions

Screenshot 2026-03-27 at 5.42.32 PM.png

Users form strong assumptions about how AI works—even without explanation.

Key Tradeoffs

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  • Expectation vs. Capability → Aligning user expectations with evolving AI systems

  • Speed vs. Clarity → Supporting rapid iteration without undermining trust

  • Innovation vs. Comprehension → Introducing a new paradigm while maintaining usability

Screenshot 2026-03-27 at 5.42.40 PM.png

Clear positioning is critical to aligning expectations with system capability. Misalignment erodes trust. 

Impact

  • Informed product and UX strategy to better align user expectations with system capabilities

  • Identified risks to user trust related to product positioning and communication

  • Guided adjustments to product language and experience to support a phased rollout

  • Helped the team balance innovation with clarity in communicating AI-driven functionality

© 2026 by Celine Pering. 

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