Smaller teams want design leaders who make, again. Here’s how.
Patrick Neeman · 28 October 2026
Read the originalPatrick Neeman, a design leader, argues that AI is shrinking design teams, so design leaders must again do production work and have it reviewed in public.
Patrick Neeman has led design teams at Icertis, Evisort and Workday, and writes in UX Collective. He argues that the large design organisations of the low-interest-rate years are shrinking. In his view, AI tools let one designer do a small team's work, so leaders are now judged by what their teams ship.
- Neeman cites Figma's 2026 AI report, in which the share of designers taking part in development doubled in a year, from 21% to 41%.
- For AI features, he argues that evaluations become the specification. His example is ten real contracts, the five facts a lawyer needs, and a ban on invented renewal dates.
- He suggests that a director work one day a week inside one team close to launch. After a quarter, the director compares its cycle time and quality with other teams.
- He asks each leader to own one piece of production work a month, such as a component or a prompt, under the same review as everyone else.
A design organisation can test the author's argument on one team for one quarter before changing any reporting lines. This is an opinion essay drawing on the author's career and on published surveys, not a measured study.
List the week's pending decisions, mark the few that are hard to undo, and return every reversible decision to the person doing the work.
Have each design leader own one piece of production work a month, such as a component or a prompt, and put it through the usual review.
Derived by Working Surface from the article; more in the Playbook. Source line: The player-coach sets the quality bar by meeting it in public, reviewing outputs in the same channel as everyone else and showing the edits.
29 October 2026: as AI agents raise the volume of work to check, teams move the check closer to the work: fixed rules for what agents may fix, design rules written where agents read them, reviewers trained on how AI-written code goes wrong, and leaders who do the work and are reviewed in public.