Engineering-Management

A coding agent can turn a detailed plan into code, tests, configuration, and infrastructure before the next planning meeting. That can reduce the time and effort needed for implementation. It does not settle whether the team chose the right problem, has permission to use the data, understands the operational consequences, or can release the change safely. That gap can make a familiar delivery failure happen sooner. The risk is easy to miss because the first result looks convincing. The pull request is large. The test suite is green. The service starts. People can demo it. By then, a team may have invested enough in one approach that changing direction feels costly or politically difficult. ...

AI can make a small team more capable. Public evidence still does not show that three-person AI-native teams outperform well-designed cross-functional teams at scale. The data supports local acceleration: research, coding, testing, and delivery activity can all move faster. It does not establish a universal optimal team size. This article maps the four conditions that determine whether a team of two or three can work sustainably—and two stress tests that reveal where it will break first. ...

AI resistance shows which part of the work system is still missing: purpose, role, skill, environment, accountability, or flow. This guide begins where diagnosis ends. If an area is red, open the relevant section and choose 1–2 actions for the next sprint. People’s positions are not personality types. The same person can be an advocate in one scenario, a productive skeptic in another, and an avoidant participant in a third. The source of resistance shows what needs to be fixed. A person’s position shows how to act. ...

AI adoption checklist for organizations AI is already in the team. The question is where you are now and what to do next. This checklist is based on real adoption work: mistakes that repeat from company to company and practices that consistently work. Before using the checklist, take the diagnostic →. It will show which zones are burning red in your own team. Four phases Phase Main question Main mistake 1. Sense-making Why are we doing this? Starting without an answer to “why” 2. Preparation Is the environment ready, and are there rules? Giving access without context 3. Launch Who is responsible, and how do we verify? No agreement on responsibility 4. Scaling Why did acceleration fail to shorten delivery? Scaling before it works in one team Phase 1. Sense-making Before anyone gets access to an AI tool. ...

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. ...