AIRBNB AI PLANNER · CONCEPT 2026
The future of AI-powered trip planning
© Airbnb AI Planner
ROLE
Senior Product Designer
TIMELINE
6 Weeks
TEAM
Inkyung Ryu
SKILLS
Product Strategy
AI Prototyping
Cursor
Claude
PROBLEM
Modern travelers are paralyzed by choice
Modern travelers spend an average of 10-15 hours researching accommodations, often paralyzed by Airbnb's vast inventory and the "paradox of choice." The challenge was to transform this fragmented search experience into a seamless, intelligent flow that anticipates user needs rather than just reacting to filters.
Conversational Search
Mapped how travelers naturally describe a trip in conversation, then designed the assistant to translate that into structured search criteria.
Field Research
Watched real people plan real trips — the tab-juggling, the re-filtering, the mental math — to see where the friction actually lived.
Cross-functional Collaboration
Partnered with engineering to validate that proposed AI interactions were technically feasible within real LLM latency and cost constraints.
Opportunity
An AI layer that plans, not just filters
Designed and prototyped an AI-driven orchestration layer that curates personalized itineraries in real-time. Moved beyond static design by using an AI-assisted development workflow (Cursor, Claude) to build a high-fidelity functional prototype — this allowed testing of complex LLM interactions and dynamic UI components that a standard static prototype couldn't capture.
CORE FLOWS
From open-ended search to a guided plan
The core experience walks a traveler from a broad, conversational starting point to a fully-formed itinerary — surfacing listings, dates, and logistics as the AI narrows in on what fits, rather than asking the user to filter everything by hand.
One Prompt to Start
Travelers describe their trip in plain language and the assistant starts building a plan immediately, no forms or filters required.
A Plan, Not Just Listings
Surfaces a full day-by-day itinerary with flights, stays, and activities already assembled, instead of a list of listings to sort through.
Book Directly in Chat
Reserve flights, stays, and activities from within the same conversation, with a clear review step before anything is confirmed.
RESEARCH
Understanding the paradox of choice
Early research centered on how travelers actually search today — jumping between tabs, saving listings, and re-filtering the same criteria multiple times. That fragmented, repetitive pattern became the throughline for the rest of the project.
Core flows
Start with one open-ended prompt
Travelers start with a single free-form prompt — no filters or forms — and the assistant immediately begins generating a plan.
The assistant proposes a complete itinerary
It surfaces a complete plan — flights, stays, and a day-by-day itinerary with pricing — and keeps the conversation open to swap in different activities or hotels.
See the full day-by-day plan and book
Each day is broken out with timed stops, stays, and activities, all rolled into one price with a single Reserve button to confirm the trip.
research & Iteration
Testing Real Prompts, Not Just Mockups
Because a conversational entry point can't be evaluated as a static screen, I tested four distinct prompt patterns with real travelers to see which one actually reduced hesitation and got people to a usable itinerary fastest.
Strategic directions
Three Bets on How AI Guidance Should Work
The directions that shaped the final planning experience.
Tradeoff: This surfaced during an internal team session (pictured below), where we mapped the traveler journey and curated opportunities and open research questions from it. One idea we explored was a fully autonomous booking flow that could commit to purchases without confirmation — but removing the traveler from that decision wasn't something users trusted at this stage. We chose a lighter-weight solution instead: AI-generated suggestions paired with a clear review step, so travelers could stay in control without doing all the legwork themselves.
Confidence Before Commitment
Show flight and stay options, pricing, and cancellation terms before the plan is finalized, so nothing about booking day comes as a surprise.
Guidance, Not Interrogation
Move preference-gathering into the conversation itself, so travelers don't feel like they're filling out a form before getting useful suggestions.
Fail Gracefully
Design clear fallback paths, like handing off to a plain search view or letting travelers edit the plan directly, for the edge cases the AI can't resolve on its own.
Design decisions
We prioritized a simple, familiar, and clean interface.
Because a conversational planning flow can't be evaluated as a static screen, I tested several prompt-to-itinerary patterns with real users to see which one reduced hesitation and got them to a usable plan fastest.
reflections
What I learned
Testing in the Real World
Testing with real travelers on real trip requests surfaced friction a clickable prototype would never have caught.
Design and Build
Rapid prototyping let me test multiple prompt-response patterns with real travelers before committing engineering resources — collapsing the gap between design and validation.
Clarity Over Convenience
Showing travelers what to expect before they commit to a plan outperformed adding more automation — a reminder that trust is built through clarity, not just speed.
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