The gap between AI that works for one person and AI that works for a team

The tension is real: standardize too early and you flatten the individual workflows that are actually producing results. But leave it alone and you end up with ten people building ten private knowledge bases that never connect. The mistake most teams make is starting with shared tools. They pick one IDE plugin, one chat interface, one set of approved models, and mandate it. That kills the gains because the gains come from each person adapting the tool to their own patterns. Start instead with shared context artifacts. A shared prompt library that anyone can contribute to and pull from. A shared eval set that tests what matters to the team, not just what matters to one person. These artifacts travel between people without forcing identical workflows. Someone using Cursor and someone using Claude Code can both reference the same eval criteria and the same prompt patterns. The goal is shared signal, not uniform process.