ANNUAL REPORT » DIGITAL TRANSFORMATION » CODE AND THEORY

Problem

Most digital platforms struggle to keep readers engaged once they finish a single piece of content. Without a way to intelligently link stories together, valuable articles quickly lose their shelf life and disappear from view. For TIME, this meant lower session durations, missed opportunities for deeper engagement, and a heavy reliance on manual tagging that was both time-consuming and often inaccurate.

Solution

Code and Theory created Surrounding Stories, a tool that builds a real-time “knowledge graph” to activate an entire content ecosystem. Instead of relying on manual links, the system uses AI to understand the deep connections between TIME articles. It recommends the “next-best” story at the exact moment a reader is most interested, based on their behavior and specific circulation rules. This kept TIME users in a continuous flow of information without requiring any extra work from editorial teams. 

Outcome

By automating content discovery, Surrounding Stories transformed how users interact with TIME’s digital libraries. The system effectively turned static archives into a dynamic, interconnected web of information that drives measurable loyalty and visibility. The tool increased discoverability and relevance across the entire site, boosted session duration and total pageviews per user, extended shelf life for older stories that would otherwise be forgotten, and reduced manual labor by eliminating the need for manual content tagging.