None of this is a hunch.
The four questions are not ours. Each carries decades of named research on why change succeeds or fails. Alignment traces to Cyert and March (1963) on who actually holds the decision, Cross and Parker (2004) on the informal network beating the org chart, and Kotter (1996) on the guiding coalition. Roles traces to Trist and Bamforth (1951), who found in the British coal mines that you redesign the work and the technology together or neither holds, and to Polanyi (1966) on the knowledge people hold but cannot easily tell. Readiness traces to Weiner (2009) and Armenakis, Harris and Mossholder (1993) on whether people are both willing and able, and to Cohen and Levinthal (1990) on absorbing only what you already have the footing for. Strategy traces to Goodhue and Thompson (1995): fit beats features. And the idea that change has a budget runs from Lewin (1947) through the change-saturation work to today.
As it happens, the research on how new things spread began on farms. In 1943 Ryan and Gross followed hybrid seed corn through two Iowa farming communities and found farmers adopted after a neighbour had proved it on nearby land, not when an expert told them to. Rogers built diffusion research on that study. We have added what is now understood about working alongside AI, held more carefully because it is newer: that people respond socially to machines that talk (Reeves and Nass, 1996), that trust has to be calibrated (Lee and See, 2004), and a field observation from one of the cohort behind this, a behavioural scientist who ran a 500-person user group and found people kept using the agent they liked over the one that was fastest or most accurate. That last one is unpublished and we treat it as a hypothesis to test, not a law. The method, and the evidence under it, are published in full, with the arithmetic as open-source code anyone can run.
The technology works now. That was never the hard part.
For years the question was whether the tools were good enough. They are. So the thing that now decides whether AI pays off is not the model, it is the business: whether the people are ready, whether the plan fits, whether anyone has worked out what changes. Every tool before this one made you bend to the machine, learn its forms, its logins, its menus. This is the first one that can bend to you and simply listen. That is the opening. Whether you can take it depends on you, and that is what Groundtruth reads.
For the business, and the program around it.
Two readers, one method. If you are a grower or a producer, it is a clear picture of your own place and an honest read on what is worth taking on, and what to leave for now. If you run a program or sit inside an RDC, the farm-level pictures roll up into a view across your whole network, so you can see where your people actually are, not where a survey says they should be.