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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.14455 |
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| _version_ | 1866909041986371584 |
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| author | Rajah, Chandan Sengupta, Neha Castanedo, Federico Mills, Robin Bahree, Amit Muthukrishnan, Ramesh Krishnan Murray, Larry |
| author_facet | Rajah, Chandan Sengupta, Neha Castanedo, Federico Mills, Robin Bahree, Amit Muthukrishnan, Ramesh Krishnan Murray, Larry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14455 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impact Rajah, Chandan Sengupta, Neha Castanedo, Federico Mills, Robin Bahree, Amit Muthukrishnan, Ramesh Krishnan Murray, Larry Artificial Intelligence Machine Learning 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. |
| title | Intelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impact |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.14455 |