---
id: "concept-ai-economic-value-measurement"
type: "concept"
source_timestamps: ["§ A Survey of Executives Suggests Anticipatory Effects"]
tags: ["roi", "ai-metrics", "executive-decision-making"]
related: ["claim-genai-hardest-to-value", "concept-individual-vs-process-productivity", "action-controlled-experiments"]
definition: "The practice of quantifying the financial and operational return on investment from AI technologies, which is currently proving exceptionally difficult for generative AI compared to other AI types."
sources: ["execution"]
sourceVaultSlug: "hbr-seg-execution"
originDay: 8
articleStem: "hbr-foci-62-layoffs-ai-potential-not-performance"
sourceUrl: "https://hbr.org/2026/01/companies-are-laying-off-workers-because-of-ais-potential-not-its-performance"
sourceTitle: "Companies Are Laying Off Workers Because of AI’s Potential—Not Its Performance"
---
# AI Economic Value Measurement

**Definition:** The practice of quantifying the financial and operational return on investment from AI technologies — currently proving exceptionally difficult for generative AI relative to other AI types.

The December 2025 survey found that while **90% of organizations** report getting moderate or a great deal of value from AI overall, generative AI is specifically cited by **44% of respondents** as the *most difficult* form of AI technology to assess for economic value. It is considered harder to value than **analytical AI, deterministic AI, and agentic AI** (the AI typology assumed by [[prereq-ai-typology]]), largely because its outputs are qualitative and its impact on knowledge work is diffuse rather than easily measurable.

This measurement difficulty is what makes [[concept-anticipatory-ai-layoffs]] possible: executives act on belief because they cannot cleanly measure return. The remedy the authors propose is [[action-controlled-experiments]] on [[concept-narrow-deep-use-cases]], where impact *can* be isolated.

Supports [[claim-genai-hardest-to-value]] and its contrarian framing [[contrarian-genai-hardest-to-value]]. Directly linked to the translation problem in [[concept-individual-vs-process-productivity]].

**Adjacent evidence (enrichment):** Grant Thornton's 2026 AI Impact Survey frames an *AI proof gap* — 78% of C-suite leaders lack strong confidence they could pass an independent AI governance audit within 90 days. EY finds 88% of employees use AI but only 28% of organizations achieve transformational results. Both corroborate that measurement and proof, not adoption, are the bottleneck.


## Related across articles
- [[question-defining-ai-roi]]
- [[claim-converged-payback-period]]
- [[quote-roi-kept-by-employee]]