How to Engineer Delight Into AI Products: The Complete Playbook from Spotify & Google PM Nesrine Changuel
Product Growth Podcast
Life inside Google: misconceptions about PM work
Nesrine dispels myths about rigid processes at Google, describing product work as navigating chaos with strict safety and inclusiveness guardrails.
Today’s Episode
Why do some AI products feel like magic while others feel like work?
You shipped. It works. Your metrics show “success.”
But users aren’t coming back. They’re not telling friends. And next quarter, they’ll switch to the competitor with a better model.
Nesrine Changuel built Spotify Wrapped and ran Google’s Delight Team. Today, she’s giving you the complete playbook:
The 4-step Delight Model to engineer emotional connection (not just satisfaction)
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Check out the conversation on Apple, Spotify and YouTube.
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Key Takeaways
01 | Understand the 3 Types of Delight
Low Delight solves functional needs only. Surface Delight adds emotion without function (confetti, animations). Deep Delight combines both - solving problems while creating emotional connection. ChatGPT and Cursor win because they nail Deep Delight. Most PMs only ship Low Delight.
02 | Follow the 50-40-10 Rule
Allocate 50% of your roadmap to Low Delight (core functionality), 40% to Deep Delight (differentiation), and 10% to Surface Delight (brand personality). Deep delight drives 2x retention, 2x referrals, and 2x revenue versus satisfied users. This is your competitive moat.
03 | Start with Motivational Segmentation
Stop segmenting by demographics. Identify WHY users actually use your product. Map functional motivators (search, get inspired) AND emotional motivators (feel less lonely, feel proud). Your users aren't all using your product for the same reason.
04 | Use the Delight Grid
Create a grid with functional motivators on vertical axis and emotional on horizontal. Place every feature idea on it. Only functional = Low Delight. Only emotional = Surface Delight. At the intersection = Deep Delight. Can't map it? Don't build it.
05 | Apply the Humanization Technique
Ask: "If my product was a human, how would the experience be better?" Google Meet compared to being in the same room, not Zoom. Dyson compares to hiring a human cleaner, not competitors. This creates features like hand raise and emoji reactions.
06 | Validate with the Delight Checklist
Before shipping, ask: Does it bring value to business AND user? Is it inclusive? Is it familiar? Is it continuous? Is it measurable? Google Meet held back filters until they worked on ALL skin tones. This prevents Apple's breakup message disaster.
07 | Study Deep Delight Examples
Gmail Smart Compose reduces stress while helping you write. Google Meet's AI translation uses YOUR voice and emotion. Spotify's Discover Weekly personalizes while creating belonging. Chrome's Inactive Tabs improves performance while respecting user relationships. Function + emotion together.
08 | Test for Corner Cases Obsessively
Apple's AI summarized a breakup as "no longer in relationship, wants belongings." WhatsApp told a grieving person to "ask John to resend" a photo of her deceased brother. AI progresses fast functionally, but emotional needs get ignored. Corner cases destroy reputations.
09 | Learn from ChatGPT's Win
ChatGPT has 800M users not because of accuracy. People pay subscriptions because they feel less lonely. The emotional need - companionship for solo founders and remote workers - drives retention. Deep delight = personalization that improves over time and remembers context.
10 | Start Delight Early, Not Later
Don't say "let me ship functionality first, add delight later." You're building brand perception from day one. Users forgive functional gaps if the experience delights. They won't forgive boring products that work. Engineer delight from the start.
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Where to Find Nesrine Changuel
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Related Content
Podcasts:
* How to Use Google’s Latest AI Tools
* What it means to be Design-Led
* If you only have 2 hrs, this is how to become an AI PM
Newsletters:
* How to Build AI Products Right
* How to Land a $300K+ AI Product Manager Job
* How to Become an AI Product Manager with No Experience
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