An AI product ships an output. A draft, a recommendation, a summary, a decision. The dashboards say it worked: latency held, the model returned, the feature was used. None of those measures the thing that actually determines whether the product created value – whether the person on the other side ended up clearer, able to act, and still trusting the system.
That is the gap. We measure whether the signal left the building. We do not measure whether it landed.
Output is not the bottleneck
Generative systems have made output abundant. The constraint has moved downstream, to the receiver – the human inside the system who has to absorb what the machine produced, form meaning from it, and act. When that receiver is overloaded, mis-pitched, or quietly distrustful, the output is intact and the outcome still fails.
Four reads at the receiving end
Participant Coherence reads the downstream end of the chain across four conditions. Signal Landing: the output arrives intact and legible. Meaning Formation: the receiver forms the intended meaning, not a near-miss. Action Coherence: what follows is coherent action, not reactive motion. Trust Continuity: confidence in the system holds, or grows, rather than quietly eroding with each interaction.
None of these are model metrics. They are conditions in the human–AI boundary – and they are designable. A product that adapts to the receiver, rather than broadcasting at them, moves all four. That adaptation is the work.
Why it belongs on the product
Operating-model coherence asks whether the organisation can hold what AI accelerates. Participant Coherence asks the same question one layer out: whether the product's output holds for the person who receives it. One substrate, two ends of the same chain. Build for output alone and you scale noise. Build for coherence and the product compounds.