Team-Effectiveness

AI Resistance Map A working template for a team. It helps turn AI-resistance diagnostic results into a draft AI Working Agreement. The map does not ask you to collect quotes or label people. The entry point is different: the diagnostic highlights gaps in the work system, and the map helps you understand how those gaps show up in work and which agreement the team needs to make explicit. Where to start First, take the AI Resistance Diagnostic →. Twelve questions, five minutes. The questionnaire shows where gaps have appeared in the work system. ...

Diagnostic: how your team works with AI Twelve questions, six pairs. In each pair, the first question describes a real situation. The second checks the same zone from another angle. For primary questions, Often or This is our norm is a red signal. For control questions, Not us or a weak maturity answer is usually the red signal. If the primary question is “often” and the control question is “not us,” the problem is probably there. The team may not see it yet or may not be ready to admit it. ...

Why teams resist AI Every month, one question about AI in teams: how roles change, where processes break, and what to do about it. This is The Human Loop. The first issue: why teams resist AI — and what to do about it. Inside: a map of six sources of resistance, five positions people take, an E2E-team case, the diagnostic, the AI adoption checklist, and the AI Resistance Map as a working template. ...

We went from early LLM experiments to broad use of chats and coding agents across a large engineering organization at Raiffeisenbank. This article is based on Raiffeisenbank’s experience adopting LLM tools and coding agents across roughly 500 engineers. On the adoption dashboards, everything looked healthy: people opened the tools, tried them, came back, brought examples, showed demos, and tech leads helped their teams learn. The flow metrics did not move for a long time. ...

TL;DR: An effective team is one that: Achieves its business goals Maintains psychological safety and team motivation Delivers predictably through stable flow metrics Three pillars. One triangle. The Premise Two fintech teams spent three months building limit management systems in parallel. One for credit cards, one for checking accounts. Each team: 7–8 people, costing ~$100K/month (average US/EU engineering salary $120–150K/year per person, fully loaded). At the quarterly review, they discovered 70% functional overlap. ...