Glean shifts product metrics from feature volume to organizational intelligence

Emrecan Dogan, Chief Product Officer at Glean, manages a product organization where the correlation between shipping volume and value creation has weakened in the AI era (SC Moatti from Products That Count). Instead of tracking feature output, Glean uses its own AI platform to monitor organizational intelligence in real time, replacing manual status reporting and preparing for one-on-ones through automated progress tracking (SC Moatti from Products That Count). This approach treats the product organization as a laboratory for systems thinking, where fixing a bottleneck moves the constraint elsewhere rather than eliminating it.

Glean maintains enterprise predictability alongside frontier-speed shipping by decentralizing decision-making, where team members take autonomy rather than waiting for it to be granted. This shift mirrors broader trends in AI-native product management, where the primary constraint moves from engineering capacity to the quality of human judgment and the design of work processes (Ops Forward). As Paula Goldman notes, effective AI adoption requires moving from individual output metrics to managing how work happens between people, with teams acting as a unified organism rather than a collection of efficient individuals (Untangled with Charley Johnson Your…).

IKEA successfully retrained 8,500 workers after a chatbot replaced their previous roles, resulting in the company’s fastest growth in remote sales (Hands-on Agile by Stefan Wolpers AI…). Peec AI updated descriptions on 15 of 60 YouTube videos, which led to a 359% increase in daily AI retrievals compared to 17% for unchanged content (TLDR Marketing).