Intelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impact
For organizations and researchers, IIQ offers a formal framework to quantify AI embedding in workflows, addressing the need for nuanced impact measurement beyond simple usage counts.
The paper proposes the Intelligence Impact Quotient (IIQ), a composite metric to measure organizational AI integration depth and impact, combining novelty-weighted token stock, usage frequency, recency, leverage, task complexity, and autonomy. Synthetic scenarios show IIQ distinguishes between low-leverage frequent use and high-autonomy impactful AI work.
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.