用综合指标衡量企业AI融入深度和实际影响
Intelligence Impact Quotient (IIQ): A Framework for Measuring Organizational AI Impact
- 结合使用频率、任务复杂度与自主性等多维度计算AI嵌入指数
- 生成0-1000标准化分数，支持跨部门横向对比
- 适合关注AI落地效果而非模型能力的管理者和决策者
智能影响指数（IIQ）是一种复合指标，用于量化人工智能系统在组织工作中的融合深度及其实际影响。不同于仅以访问次数或总令牌量作为依据，IIQ融合了新颖性加权的时间衰减令牌存量、使用频率、宽限期时效门控、组织杠杆率、任务复杂度与自主性。该方法生成原始智能采纳指数（IAI），并进一步归一化为0-1000的IIQ指数，便于异构用户与单位间的比较。论文还推导出亚日级更新规则及效率与财务影响的有界解释层。IIQ定位为面向部署的测量框架，旨在追踪AI在工作流中的嵌入程度，而非直接评估模型能力，也不替代因果生产力分析。合成场景表明，该指标可有效区分高频低杠杆使用、语义重复提示与更自主、高影响的AI辅助工作。
原文摘要 · Abstract (English)
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.
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