Essays on learning ecosystems, high-performing teams, and organisational capability. New work publishes here first. The essay index is below, with how we research, draft, and edit in the column beside it on wide screens. For a conversation about something you read: Tom · Janna.
New research is making a buying case for AI-personalised learning. A longer arc of implementation studies keeps saying the platform itself is less than a third of whether it lands. What CHROs and CTOs should ask before the next purchase.
New peer-reviewed studies show AI-personalised learning lifts performance and engagement at the individual level. What CHROs and senior L&D leaders should ask of that evidence, and what to measure beyond it.
Assistants sit beside the work; infrastructure sits in the path of decisions and deliveries. What changes for capability when models mediate more of the workflow.
Capability lives in incentives, norms, and what ships when trade-offs bite. Programmes help; they do not override the weekly curriculum of the system itself.
AI as the enterprise front door can hide the cues people learned from rough software. Friction that carries information, team norms, and guardrails still matter.
Where AI tools reach your trusted internal knowledge, connection is still not the same as shared understanding. Context needs governance, social learning, and clear ownership.
When AI integrates faster than judgement deepens, speed masks fragility. What to watch for in daily incentives, parallel human habits, and paying down delegation debt in the work itself.
From learning ecosystems to a personal practice: Sinter, judgement that deepens rep by rep, and why we built an instrument instead of another chat.
The gap between what we know and what we can act on often comes down to context. How shared understanding develops, and why it matters for teams.
Programs are interventions, not environments. Capability lives in the conditions around the work, not in the course.
The most durable learning happens between people. When learning becomes private, teams still look informed while shared pictures of quality drift apart.
Early learners rate themselves high because they lack the criteria to judge. What useful development data actually looks like.
The stretch between following a script and adapting under pressure. Skills tell you labels people carry; how people read messy situations tells you what growth might take.
Structures that make collective sensemaking part of normal work, not an add-on.
What the environment teaches when feedback structures are broken, and what changes when they're designed deliberately.
The cost of context-switching and what it means for how we design work and learning.