Your dashboards tell you who touched the tool. They cannot tell you whose work changed. Adoption telemetry closes that gap — computing change-management stage progression directly from the usage signals your AI deployment already produces, and mapping every stall to a class of intervention.
of generative-AI pilots show no measurable P&L impact.
of companies abandoning most of their AI initiatives — a jump of one year.
of AI users reach the stage where AI is consistently embedded in their workflows.
The reported causes of failure are organizational — workflow, sponsorship, behavior — not model capability. The diagnosis is settled. What’s been missing is an instrument.
NANTE classifies a deployment population — never individuals — into five stages of behavior change, computed deterministically from telemetry an enterprise already generates. Thresholds are published, falsifiable, and open to dispute.
nante (Twi): “walk” — as in nante yiye, walk well. The model measures a population’s walk through a change.
Two cohorts a usage dashboard calls identical — over 98% of both reach recurring use. One is integrating the tool into how work gets done; one has stalled at the surface. NANTE separates them.
Healthy: 25.1% reach depth (Transform + Embed) · composite 60.5 · no stall flag. Stalled: 0% past Navigate · composite 49.9 · shallow-plateau flag → workflow redesign, not more licenses. Figures are the reference and shallow-plateau cohorts from the paper’s evaluation (synthetic populations; reproducible via make results).
Adoption telemetry: continuous measurement of behavior change from production signals, interpreted through an explicit model of change — distinct from evals, usage analytics, product analytics, and survey-based change management.
NANTE: five stages, six diagnosable patterns (one healthy, five failure modes), defined thresholds, and a stall-to-intervention map — so measurement ends in action, not a score.
agent-adoption-kit (Apache-2.0): a local pipeline over your own exports. Every table and figure in the paper regenerates from the published code. Raw events never leave your environment.
The evaluation demonstrates computability and discrimination on synthetic populations. Every threshold is a proposed default, not a calibrated value; validating stages against real outcomes is the paper’s stated research agenda — and the reason the design-partner program below exists.
The framework’s next step requires what no synthetic population can supply: production deployments with observed outcomes. PolyWise is convening a small group of organizations running enterprise AI at cohort scale (50+ seats) to validate and calibrate NANTE against reality.
Participation is scoped, confidential, and cohort-level only — NANTE never scores individuals.
Young, D. A. (2026). Adoption Telemetry: Measuring Enterprise AI Adoption from Production Signals. Zenodo. https://doi.org/10.5281/zenodo.21943955
@article{young2026adoption,
author = {Young, Damon A.},
title = {Adoption Telemetry: Measuring Enterprise AI Adoption
from Production Signals},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.21943955},
url = {https://doi.org/10.5281/zenodo.21943955}
}