Scaling Our Impact with AI: Research Planning Co-Thinkers

Focus

  • Internal UX Research Enablement

  • AI Integration

Client: Fortune 500 retailer

Role: Strategist, Educator, and Builder

Impact: Scaled research consultations, improved planning quality, and democratized access to evidence-based practices

With AI-powered co-thinkers, we gave teams faster, smarter ways to plan studies and sparked a new way of working: one that saves time, builds confidence, and keeps humans where they matter most.

The Challenge

My Role

Our centralized UXR team of 4 supports a growing community of 40+ researchers across the org.

Office hours were a go-to for coaching — helping teams clarify questions, tighten goals, and think out loud. But demand outpaced our capacity, and those high-value dialogues didn’t scale.

We needed a way to deliver the thinking and structure of office hours — without the human bottleneck.

Designed and led the AI-driven research enablement strategy

  • Created a custom GPT tool rooted in team standards and best practices

  • Expanded the assistant into a living system with real-time academic updates

  • Scaled impact by turning consultations into reusable co-thinking frameworks

  • Enabled 40+ researchers to plan better, faster, and with greater confidence

Phase 1: Building the Research Planning Assistant GPT

I created a custom GPT trained on our internal research standards and templates.


Its purpose?


Simulate the exact conversation we’d have during an intake session and output a first-draft research plan — structured, aligned, and ready for review in office hours.

✅ Benefits:

  • Reduced prep time for teams

  • Higher-quality research plans

  • Clearer alignment with internal best practices

  • Freed our human team for more strategic engagements

Phase 2:
Expanding into AI-Human Interaction

Inspired by Stanford’s “UI/UX Design for AI Products” course and growing internal demand for AI-infused tools, I applied the same co-thinker concept to AI research itself.

I built a second GPT focused on Human-AI interaction — but didn’t stop there.

To keep it current, I:

  • Wrote a custom Python script

  • Scraped an open academic repository for the latest HAI research

  • Set up monthly updates to feed the GPT’s knowledge base

Result: A thinking partner that draws from cutting-edge research to guide AI feature design and exploration — without requiring everyone to read a dozen papers a month.

Outcomes

  • Scaled consultation: More people got the help they needed — when they needed it

  • Increased research maturity: First drafts came in stronger, faster

  • Democratized best practice: Tools were accessible across levels and teams

  • Freed up team capacity: We focused where the human layer mattered most

Next Steps

We’re extending the co-thinker model to new frontiers:

  • First-Pass Critique Framework
    We’re building a feedback engine to give early-stage concept critiques — helping teams catch low-hanging UX issues before involving real participants. It’s not a replacement for users, but a smart filter to refine what should be tested.

  • Simulated Omnichannel Environments
    We’re experimenting with AI-driven simulations to prototype and evaluate complex, cross-channel experiences. Think call centers, web, mobile, and retail interactions — stitched together into a researchable environment before full deployment.