Two trials, opposite outcomes. What the newest research says about reading AI coaching results, and where the tool stops working.

In the new edition of my book, Building Successful Communities of Practice, I share insights into community size and interactions. I’ve been using this theory with clients to create nested layers that include communities of practice that serve as a model for their organisation.

A magazine for people who build organizations, question assumptions, and believe that better systems begin with better questions.

We've just spent a week travelling around the country to visit SINA, a network that has trained hundreds of scholars to tackle youth unemployment across Africa by building what it calls "freesponsible" community hubs. 23 hubs in nine countries. More than 120 enterprises launched. Over 1,600 jobs created. All of it without managers.

My goodness.

Leaders today are operating in a state of “ambient urgency,” where constant change, competing priorities, and relentless demands make everything feel equally important. Traditional prioritization and time-management frameworks were designed for temporary crises and are less effective when urgency becomes the norm. Rather than trying to do more, leaders need to make fewer, better decisions by consciously choosing what to pursue, delegate, defer, or stop. Three decision-making tests can help.

On the 13th of July, NISTA published the latest Major Projects annual report. The framing of which is 'strong foundations for delivery.' The scale of what's covered is hard to wrap a brain around. £924.2bn of whole life cost, across 189 major programmes. Many running for a decade or more. The thing that stands out for me, is that 58% of those are rated amber. They are stuck. Hold that thought.

Technology is meant to make coding partnerships better, but a new study suggests AI-coding agents are throwing up silos.

Can AI finally fix the knowledge decay issue that’s plagued software engineering teams for decades? Neel Sundaresan, GM at IBM, thinks so.