Cemre Güngor: How to ship AI features users actually use | Product in Practice

Quick Overview

The most durable pattern for shipping successful AI features involves product managers focusing rigorously on solving customer problems rather than getting overly excited about novel AI solutions, which requires learning through shipping small, iterative versions to validate real-world value over relying on initial demos.

Key Points: The most stable pattern for good product teams remains starting with the end in mind and avoiding getting "too excited about solutions and not problems," a pitfall exacerbated by easy AI prototyping. Product management craft must shift from heavily metric-driven funnels (like at Instagram) to relying more on user understanding and judgment when product surfaces become complex, such as in the Figma canvas or the browser. The key value of an AI browser like the one at the Browser Company is not friction reduction for simple queries, but the ability to bring in and utilize the user's disparate context from various apps and documents. The single best predictor for committing to an AI feature is whether internal teams are actually using it themselves, as demonstrated when the team pivoted a highly-praised feature because internal users were not clicking on it in production. Building AI features requires significant upskilling, including learning prompt engineering, and successful evaluation relies on setting up evals and data sets to hill climb on quality, as non-deterministic features are hard to assess beforehand. The Browser Company found that integrating AI features requires learning by doing; they successfully shipped Gmail/Calendar integrations only after reasoning models improved tool calling capabilities, after an initial attempt failed a year prior. Product managers should "do the boring thing" by focusing on reliable friction points that AI can solve, rather than chasing shiny, stage-demo-worthy features that lack sustained daily utility.

Context: This interview features Cemre Güngor, who has held roles at Facebook, Instagram, Figma, and now drives product at the Browser Company (DIA), discussing how product management must evolve to ship AI features that users actually adopt. The conversation centers on recurring patterns observed across these diverse product environments—from massive consumer social apps to specialized design tools and now an AI-native browser—focusing specifically on the challenges of context management, evaluation, and iterative development in the age of LLMs.