We're interviewing 100 EOS Implementers about how AI actually shows up in their practices in 2026.
Not how the LinkedIn gurus say it should. How it actually does.
Here's why that distinction matters. The AI conversation aimed at implementers is loud, and almost none of it comes from inside a real practice. It comes from tool vendors, keynote slides, and people who have never sat across from a leadership team at 8 a.m. with a scorecard that went sideways. The advice is confident. The evidence is thin.
So we're going to the source. One hundred implementers, one at a time, twenty minutes each. Real practices, real client books, real answers.
The questions are simple. Where do the hours in your practice actually go? What have you tried with AI — and what did you quietly stop using after two weeks? What would you never let it near? If you could hand one piece of your week to someone tomorrow, what would it be?
We have a hypothesis going in, and we're putting it on the record so you can hold us to it: we suspect the interesting problem isn't meeting prep. The platforms are getting good at that. We suspect the real weight is everything between sessions — the context of six clients' rocks, issues, and people living in one implementer's head, with no way out of it.
That's a hypothesis, not a finding. The whole point of asking a hundred people is that we might be wrong.
If the interviews say the real pain lives somewhere else entirely, that's what the report will say. Research you already know the conclusion to isn't research — it's marketing with a survey attached, and there's plenty of that around already.
We're publishing the full report this summer. Every participant gets featured and gets the findings first, before anything goes public. There's no pitch inside the interview — it's research, and it stays research.
Run a real practice? Twenty minutes and you're in. And if you'd rather first see what the between-sessions problem looks like in your own practice, that's exactly what the call below is for.