Discover Artificial Intelligence· 2026Q1
Beyond the average association between artificial intelligence and sales performance
- 0citations
- Q1SCImago
- 2026year
Short summary
Automated lead generation is negatively associated with revenue growth at the 75th and 90th percentiles, contradicting the assumption that AI uniformly boosts sales.
AI-generated from the title and abstract; the full text is not read.
Key points
- Automated lead generation shows a negative association with revenue growth at the 75th and 90th quantiles.
- AI usage, marketing budget, sales-team size, and personalized campaigns did not show significant associations with revenue growth across quantiles.
- The study used an exploratory synthetic firm-level dataset (n=50) with OLS and quantile regressions and bootstrap inference.
- Marketing efficiency and revenue per salesperson were excluded to prevent target leakage.
AI-generated from the title and abstract; the full text is not read.
Abstract
The assumption that AI-driven marketing technologies uniformly improve sales performance remains insufficiently tested. Using an exploratory synthetic firm-level dataset (n = 50), this study estimates OLS and quantile regressions at tau = 0.10, 0.25, 0.50, 0.75, and 0.90, with 2000-replication pairs-bootstrap inference. A data-integrity audit showed that marketing efficiency and revenue per salesperson are deterministic functions of revenue growth and were therefore excluded from the inferential models to prevent target leakage. In the corrected specification, automated lead generation is negatively associated with revenue growth in OLS and at Q75 and Q90, with 95% bootstrap confidence intervals excluding zero. AI usage, marketing budget, sales-team size, and personalized campaigns have wide bootstrap intervals that include zero at every reported quantile. Huber and ridge estimates preserve the OLS coefficient signs but differ in magnitude, underscoring the exploratory nature of the results. The study contributes a cautious, distribution-sensitive assessment and identifies priorities for validation with larger, real-world data.
The authors' abstract, as published at the source. Discover Artificial Intelligence, 2026 · DOI ↗
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