Intelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impact

2026-05-14

AI Summary

Existing metrics—such as access counts or total token usage—struggle to accurately capture the depth of AI integration and its actual business value within organizations. This work proposes the Intelligence Impact Quotient (IIQ), a novel framework that integrates multiple dimensions—including semantic novelty, temporal decay, task complexity, organizational leverage, usage frequency, recency of activity, and autonomy—into a unified index. Through composite modeling, normalized mapping, and sub-daily update mechanisms, IIQ produces a standardized 0–1000 score that effectively distinguishes between high-frequency, low-value interactions and high-impact AI collaborations. The framework enables comparable evaluations across users and departments and demonstrates strong sensitivity and discriminative power across diverse usage patterns in synthetic validation scenarios.

Abstract

The Intelligence Impact Quotient (IIQ) is a composite metric intended to quantify the depth to which AI systems are integrated into organizational work and their impact. Rather than treating access counts or aggregate token volume as sufficient evidence of impact, IIQ combines a novelty-weighted, time-decayed token stock with usage frequency, a grace-period recency gate, organizational leverage, task complexity, and autonomy. The formulation produces a raw Intelligence Adoption Index (IAI) and a normalized 0-1000 IIQ index for comparison between heterogeneous users and units. We also derive sub-daily update rules and a bounded interpretation layer for estimated efficiency and financial impact. The paper positions IIQ as a deployment-oriented measurement framework: a formal proposal for tracking AI embedding in workflows, not a direct measure of model capability or a substitute for causal productivity evaluation. Synthetic scenarios illustrate how the revised metric distinguishes between frequent low-leverage use, semantically repetitive prompting, and more autonomous, higher-consequence AI-assisted work.

Problem

• AI impact measurement
• organizational AI integration
• Intelligence Impact Quotient
• AI deployment evaluation
• workflow embedding

Innovation

• Intelligence Impact Quotient
• AI integration measurement
• composite metric
• organizational leverage
• autonomous AI usage