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

TL;DR

The paper introduces the Intelligence Impact Quotient (IIQ), a comprehensive metric designed to measure how deeply AI systems are integrated into organizational workflows and their impact.

Contribution

It proposes a novel, deployment-oriented framework combining multiple factors to quantify AI integration and impact within organizations.

Findings

IIQ produces a normalized index for comparing AI impact across units.

The metric distinguishes between different types of AI usage, such as repetitive prompting and autonomous work.

Sub-daily update rules enable real-time tracking of AI embedding.

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.