A new survey of 801 executives says enterprise AI has an "orchestration gap." I read all of it. Here are my margin notes on what the numbers show, and the idea in it I think matters most.

As AI makes research, design, coding and other knowledge work dramatically faster, what happens to the Product Operating Model? The answer may be that it becomes less about managing execution and more about helping organisations decide what is actually worth doing.

Mark Ajzenstadt recently posted a list of 30 mistakes he sees enterprises make with AI transformation. It's one of the most practical things I've read on the subject this year, and it's worth bookmarking. What struck me most, though, was how few of the 30 are really about AI.

AI is saving people time. Turning those gains into business results takes changes to workflows, decision-making, and accountability.

Introducing new tools is easy. Changing how an organization creates value, makes decisions, and delivers work is much harder.

AI creates value when it improves how work happens, not simply when another tool is added to the stack.

Effective agentic systems require more than a good prompt: context, intent, boundaries, and feedback.

Practical capability building so people can evaluate AI-generated work and adopt new ways of working.