Gallup's 8.7× multiplier appears in every AI transformation deck. It attaches to perceived transformation, not adoption frequency (1.7×), and it is correlational — which changes where your budget should go.
16% of people already use AI that acts without human intervention, yet 66% say oversight is essential. The limits of autonomy are set by your human layer, not by the model.
Full calendars, friendly teams, smooth handoffs — and still no collaboration. Four look-alikes get mistaken for the real thing, and AI deployed on top of them only automates the illusion.
Two numbers from the same Gallup study, the same sample, the same quarter. The gap between them is the most expensive one in today's AI implementations — individual gains that never add up to organizational change.
Shadow AI is AI use without your knowledge, approval, or governance. Gallup's undivided 52% hides it, the 47%-vs-25% plan gap feeds it, and only an instrument built on psychological safety can measure it.
Gallup's Q2 2026 data puts US workplace AI use at 52%. That locates the market average — not your organization. Three questions a benchmark can never answer, the difference between description and causality, and a 90-day sequence for building your own baseline.
License counts and login stats measure whether people touched the tools — not whether work changed. Here are the three layers of metrics that actually predict AI ROI, and how to collect them.