Industry-leading insights

There is a persistent belief in digital transformation that more tools create more speed. Add automation. Add collaboration platforms. Add AI assistants. Add dashboards. Over time, the organization becomes more capable, and capability is often mistaken for velocity. In practice, I’ve seen the oppos ...

When AI initiatives stall, training is often the first thing organizations reach for. More sessions, better documentation, and new internal resources meant to help teams feel comfortable with the tools. The assumption is simple. If people are not using AI effectively, they must not understand it we ...

AI has become the easiest thing to blame. But the uncomfortable truth is that most AI initiatives don’t fail because of technology, they fail because the way work actually happens was never designed to absorb it. Where the Frustration Really Comes From Over the past couple of years, AI has taken a l ...

In most organizations, accountability follows a familiar pattern. A decision is made. A person signs off. If the outcome falls short, everyone knows where responsibility sits. That clarity begins to blur the moment AI enters the process. We see this pattern often when working with leadership teams. ...

Most AI initiatives do not fail during the pilot phase. They stall shortly after it ends, when ownership and responsibility are still unclear. Why Pilots Feel Safe Pilots exist in a protected space. Expectations are intentionally low, risk feels contained, and success is loosely defined. Teams are a ...

Over the past two years, AI has moved from a distant emerging technology to a defining force reshaping how organizations operate, communicate, and compete. It is no longer an optional innovation track—it is a structural shift in how work gets done. And yet, while some organizations move boldly into ...