🏆
Red Dot Award Winner
Redefining Bookings
Through Conversational AI
Redefining Bookings
Through Conversational AI
Redefining Bookings
Through Conversational AI
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Problem
Statement
Problem
Statement
Problem Statement
Frequent travelers weren't getting a frequent-traveler experience
Frequent travelers weren't getting a frequent-traveler experience
Air India's booking experience required frequent travelers to repeat many of the same steps for journeys they booked regularly. Routes, seats, fare preferences and payment choices often followed recognizable patterns, but the experience did not make enough use of that familiarity.
The opportunity was to turn a repetitive booking workflow into a more intelligent, personalized action—without compromising user control, pricing transparency, or airline rules.
Air India's booking experience required frequent travelers to repeat many of the same steps for journeys they booked regularly. Routes, seats, fare preferences and payment choices often followed recognizable patterns, but the experience did not make enough use of that familiarity.
The opportunity was to turn a repetitive booking workflow into a more intelligent, personalized action—without compromising user control, pricing transparency, or airline rules.
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What was Broken?
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What was Broken?
Experience
Not Built for
Frequent Travelers
Experience
Not Built for
Frequent Travelers
Experience Not Built for
Frequent Travelers
The real user problem was that Air India’s loyalty users, who are frequent travellers, still had to go through a lengthy and repetitive booking process every time they wanted to fly. They had to enter travel details, navigate multiple screens, compare options, select seats, and provide passenger information—even when their preferences and needs were predictable. This created unnecessary time, effort, and cognitive load for users who expected a faster, more personalised experience from a premium loyalty programme.
The real user problem was that Air India’s loyalty users, who are frequent travellers, still had to go through a lengthy and repetitive booking process every time they wanted to fly. They had to enter travel details, navigate multiple screens, compare options, select seats, and provide passenger information—even when their preferences and needs were predictable. This created unnecessary time, effort, and cognitive load for users who expected a faster, more personalised experience from a premium loyalty programme.
The real user problem was that Air India’s loyalty users, who are frequent travellers, still had to go through a lengthy and repetitive booking process every time they wanted to fly. They had to enter travel details, navigate multiple screens, compare options, select seats, and provide passenger information—even when their preferences and needs were predictable. This created unnecessary time, effort, and cognitive load for users who expected a faster, more personalised experience from a premium loyalty programme.
Repetition — frequent travelers repeatedly entered information for familiar journeys.
Repetition — frequent travelers repeatedly entered information for familiar journeys.
Limited preference memory — recurring choices were not surfaced intelligently enough.
Limited preference memory — recurring choices were not surfaced intelligently enough.
High cognitive load — loyal customers experienced much of the same process as first-time bookers.
High cognitive load — loyal customers experienced much of the same process as first-time bookers.
Lost time and engagement — unnecessary friction created opportunities for drop-off and weaker loyalty.
Lost time and engagement — unnecessary friction created opportunities for drop-off and weaker loyalty.
My Leadership
& Role
My Leadership
& Role
My Leadership & Role
I led the UX strategy and product experience design for Ez Booking, working across concept, interaction design, design-system extension, and cross-functional alignment.
I led the UX strategy and product experience design for Ez Booking, working across concept, interaction design, design-system extension, and cross-functional alignment.
I owned
I owned
Product experience strategy
Product experience strategy
End-to-end booking UX and interaction design
End-to-end booking UX and interaction design
AI-driven personalization patterns
AI-driven personalization patterns
Trust and transparency patterns
Trust and transparency patterns
Design-system extensions
Design-system extensions
Experience governance
Experience governance
I influenced
I influenced
Product direction and prioritization
Product direction and prioritization
AI experience principles
AI experience principles
Loyalty experience strategy
Loyalty experience strategy
Collaboration between Product, Data Science, Engineering and Design
Collaboration between Product, Data Science, Engineering and Design
Experience decisions across the booking ecosystem
Experience decisions across the booking ecosystem
Research
& Discovery
Research
& Discovery
Research & Discovery
Behavioral analysis of loyalty-member bookings revealed repeated patterns in routes and booking preferences. Customer interviews and NPS feedback reinforced the need for faster repeat booking, preference memory, and reduced form-filling. Competitive benchmarking helped identify an opportunity to make repeat booking more integrated and useful.
Behavioral analysis of loyalty-member bookings revealed repeated patterns in routes and booking preferences. Customer interviews and NPS feedback reinforced the need for faster repeat booking, preference memory, and reduced form-filling. Competitive benchmarking helped identify an opportunity to make repeat booking more integrated and useful.
Behavioral analysis of loyalty-member bookings revealed repeated patterns in routes and booking preferences. Customer interviews and NPS feedback reinforced the need for faster repeat booking, preference memory, and reduced form-filling. Competitive benchmarking helped identify an opportunity to make repeat booking more integrated and useful.
Key Insight
Frequent travelers don't need another booking journey. They need the product to remember the journey they've already chosen.
Key Insight
Frequent travelers don't need another booking journey. They need the product to remember the journey they've already chosen.
Key Insight
Frequent travelers don't need another booking journey. They need the product to remember the journey they've already chosen.
Key Insight
Frequent travelers don't need another booking journey. They need the product to remember the journey they've already chosen.
AI-powered one-tap booking experience, designed to allow frequent travelers to complete bookings in seconds using intelligent preference memory, predictive recommendations, and instant checkout.
AI-powered one-tap booking experience, designed to allow frequent travelers to complete bookings in seconds using intelligent preference memory, predictive recommendations, and instant checkout.
From booking flow to
Intelligent action
From booking flow to
Intelligent action
From booking flow to
Intelligent action
The traditional experience asked travelers to progress through the same sequence every time. The new model shifted the product from asking users to remember everything toward helping the product remember useful patterns.
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What We Built
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What We Built
EzBookingintroducedanewbookingparadigmforfrequentflyers.
EzBookingintroducedanewbookingparadigmforfrequentflyers.
EzBookingintroducedanewbookingparadigmforfrequentflyers.
eZ Booking solved the loyalty user's biggest booking pain—friction—by replacing a complex, multi-screen airline booking process with a personalised, conversational “tell us what you need” experience.
eZ Booking solved the loyalty user's biggest booking pain—friction—by replacing a complex, multi-screen airline booking process with a personalised, conversational “tell us what you need” experience.
Feature Highlights
Predictive route recommendations
Predictive route recommendations
One-tap checkout
One-tap checkout
Smart notifications for price and schedule changes
Smart notifications for price and schedule changes
Trust and transparency layers for AI-driven booking
Trust and transparency layers for AI-driven booking
Preference memory for seats, meals, fare class, and payment
Preference memory for seats, meals, fare class, and payment
Preference memory for seats, meals, fare class, and payment
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Business Impact
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Business Impact
EzBookingdeliveredmeasurableimprovementsinloyaltyengagementandconversion.
EzBookingdeliveredmeasurableimprovementsinloyaltyengagementandconversion.
EzBookingdeliveredmeasurableimprovementsinloyaltyengagementandconversion.
⏱
Booking time reduced 90% (4 min → 25–30 sec)
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Booking time reduced 90% (4 min → 25–30 sec)
📈
Repeat bookings increased 42%
📈
Repeat bookings increased 42%
💎
CLV increased 2.3×ease
💎
CLV increased 2.3×ease
💰
₹280 crore annual revenue potential
💰
₹280 crore annual revenue potential
🏆
Red Dot Design Award 2024
🏆
Red Dot Design Award 2024
DD
Personalized booking suggestions
Key Learnings
AI-powered experiences must be transparent and trust-driven. Speed alone is not enough-users must feel in control of automated decisions, especially in high-value purchases like airline tickets.
" Ez Booking positioned me as a driver of AI-led product innovation within Air India’s digital ecosystem and demonstrated my ability to design intelligent, scalable experiences for high-value customer segments"
Key Learnings
AI-powered experiences must be transparent and trust-driven. Speed alone is not enough-users must feel in control of automated decisions, especially in high-value purchases like airline tickets.
" Ez Booking positioned me as a driver of AI-led product innovation within Air India’s digital ecosystem and demonstrated my ability to design intelligent, scalable experiences for high-value customer segments"
Key Learnings
AI-powered experiences must be transparent and trust-driven. Speed alone is not enough-users must feel in control of automated decisions, especially in high-value purchases like airline tickets.
" Ez Booking positioned me as a driver of AI-led product innovation within Air India’s digital ecosystem and demonstrated my ability to design intelligent, scalable experiences for high-value customer segments"
From Legacy Platform to High-Conversion Digital Engine
From Legacy Platform to High-Conversion Digital Engine
View Casestudy
View Casestudy