AI & Invisible Experiences
Jessa’s work in AI focuses on designing decision systems, behavioral signals, and invisible interactions—places where users experience outcomes rather than interfaces.
Rather than treating AI as a feature, this work centers on:
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How people are matched, supported, or guided
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How algorithms reflect organizational values
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How invisible systems earn trust at scale
These projects explore how design leadership shapes AI-driven experiences responsibly, especially in environments with high complexity, sensitive data, and organizational risk.
Context
Walmart lacked a scalable way for employees to find meaningful mentorship across a massive, global organization. Traditional mentor programs were constrained by hierarchy, geography, and visibility—often pairing people based on proximity rather than compatibility.
The opportunity was not simply to build a chatbot, but to design a system that could leverage Walmart’s rich HR data—role tenure, geography, experience level—while centering the human question underneath it all: How do people actually want to be mentored?
Leadership Challenge
This work required navigating both technical and organizational ambiguity. There was no existing model for algorithmic mentorship inside the company, no shared definition of what “successful” mentorship looked like, and no precedent for using matching logic in this way.
The challenge was to design an experience that felt personal and trustworthy, while introducing predictive matching logic that had never been attempted by the team—or the organization—before.
What Jessa Led
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Led discovery to understand mentorship preferences, work styles, and motivations across diverse employee groups
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Defined mentorship signals beyond role and seniority, including learning goals, communication styles, and desired mentor dynamics
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Partnered with engineering and data science to design and iterate on a custom matching algorithm
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Shaped the conversational experience into a familiar, low-friction interaction—often described internally as “Tinder for mentors”
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Established feedback loops to improve match quality and algorithmic confidence over time
Outcome
The platform enabled employees to connect with mentors anywhere in the organization, regardless of role, geography, or reporting structure. Match volume increased, prediction accuracy improved, and the program demonstrated measurable success in connecting people who would not otherwise have crossed paths.
The product was later highlighted by Walmart’s Chief Inclusion Officer and publicly announced at 2018 SXSW, marking a first-of-its-kind mentorship system within the company and a foundational example of human-centered algorithmic design at enterprise scale.