--- title: "SAGE-AIP: A Scale-Adaptive Gated Ensemble Framework for Predicting AI Adoption and Productivity Across Multiple Data Scales · AI Fault Line" url: https://aifaultline.com/research/p/doi-10-20944-preprints202611-0013-v1/ description: "AI Fault Line is a neutral, evidence-cited field guide to why experts looking at the same developments disagree about AI's effect on productivity, jobs, inequality, regulation and the pace of change." --- [Research](https://aifaultline.com/research/) # SAGE-AIP: A Scale-Adaptive Gated Ensemble Framework for Predicting AI Adoption and Productivity Across Multiple Data Scales Kittipol Wisaeng, Brian Akabagy Enyiawah · Preprints.org · 2 Nov 2026 [Read original source](https://doi.org/10.20944/preprints202611.0013.v1) Open access Where this sits in the debate - [Will AI actually boost productivity and growth?](https://aifaultline.com/debate/productivity/) · its finding: it depends ([why studies differ](https://aifaultline.com/debate/productivity/#why)) Direction of the finding as read from the abstract by an AI model ([AI-written](https://aifaultline.com/about/)). ## Abstract Artificial intelligence (AI) is frequently associated with productivity improvement, yet empirical conclusions depend on where AI exposure is measured, how productivity is defined, and which model is used. This study develops a multi-scale comparative framework using three datasets: 145,000 AI-adoption records from 10 countries and eight industries; 2,074 observed country-year records from 233 country or regional units (2015–2023); and 150 employee-level observations. Robust regression, country fixed effects, dynamic fixed effects, and difference-GMM sensitivity analysis are combined with a proposed Scale-Adaptive Gated Ensemble Framework for AI Adoption and Productivity (SAGE-AIP), which is benchmarked against six established regressors using temporal holdouts for the two longitudinal datasets and a train–test split for the employee data. The adoption dataset shows almost no systematic structure (R2 = 0.00018), and SAGE-AIP does not improve on simpler models there (test RMSE 28.976 vs. 28.900 for Elastic Net). In the global panel, automation intensity is strongly associated with AI-attributed productivity (β = 19.882, SE = 0.434), and SAGE-AIP attains the lowest test RMSE (4.448; R2 = 0.544). At the employee level, recorded AI-tool information remains positively associated with productivity (β = 0.905, SE = 0.220), but Random Forest outperforms SAGE-AIP (test RMSE 0.435 vs. 0.583). SAGE-AIP is therefore not uniformly superior; its value is scale-specific, which supports evaluating models across data scales rather than relying on a single dataset or model class. ## About this publication **Preprint.** Posted on a preprint server or repository (arXiv, SSRN, Zenodo and others) before or without peer review. Anyone can post there. [How sources are ranked](https://aifaultline.com/about/#source-ranking). Published 2026 (recently) Citations 0 (OpenAlex) 0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison. [Chart: published 2026] ## More on this topic Related by theme, not necessarily by method or conclusion. - [Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality](https://aifaultline.com/research/p/doi-10-1287-orsc-2025-21838/) Organization Science · 2026 - [Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality](https://aifaultline.com/research/p/doi-10-2139-ssrn-4573321/) SSRN · 2023 - [Generative AI at Work](https://aifaultline.com/research/p/doi-10-1093-qje-qjae044/) The Quarterly Journal of Economics · 2025 - [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://aifaultline.com/research/p/doi-10-48550-arxiv-2302-06590/) arXiv · 2023 Overview assembled automatically from collected metadata. [How this site is built](https://aifaultline.com/about/).