AI strategies at NYT and Washington Post — Nieman Lab analysis

This Nieman Lab piece summarizes presentations from the Digiday Publishing Summit, where AI leaders from The New York Times and The Washington Post outlined their distinct approaches to AI adoption. The comparison reveals how two newsrooms with similar resources can arrive at very different AI strategies.

Context

At the Times, Zach Seward’s AI Initiatives team treats research and investigation as “by far the biggest use of our resources and the biggest opportunity right now when it comes to AI in media.” The team works by helping individual reporters use AI on specific projects, then creating repeatable processes that others can adopt. The Times also maintains an open Slack channel where anyone in the newsroom can ask questions and share use cases, building institutional knowledge organically.

At the Post, chief AI officer Sam Han’s team focuses on business applications alongside editorial ones. Their AI-powered paywall adjusts to individual reader usage patterns, producing what Han describes as “a 20% increase” in subscription conversions. The Post’s approach treats AI as both an editorial and a revenue tool.

Key takeaway

Neither approach is universally better; they reflect different organizational priorities. The Times prioritizes editorial impact (helping reporters do work they could not do otherwise), while the Post prioritizes operational efficiency (making existing processes work better). Writers and editors evaluating their own organization’s AI strategy can use this comparison as a framework for choosing between impact-first and efficiency-first approaches.

Who should read this

Newsroom leaders and content strategists developing AI adoption plans, and writers interested in understanding how organizational decisions about AI affect the work they are asked to do.