Measuring Creativity in the Age of Generative AI: Distinguishing Human and AI-Generated Creative Performance in Hiring and Talent Systems

TL;DR

This paper proposes a new quantitative framework for measuring creativity as novelty in synthesis, addressing challenges posed by AI-generated artifacts in talent evaluation and organizational value creation.

Contribution

It introduces a distributional, process-based measure of creativity operationalized through idea generation and transformation within embedding space, distinguishing human from AI contributions.

Findings

Metrics align with intuitive judgments of creativity.

AI environments shift creative output toward bimodal distributions.

Distinctiveness becomes a key signal of human creativity in AI-mediated settings.

Abstract

Generative AI is rapidly transforming how organizations create value and evaluate talent. While large language models enhance baseline output quality, they simultaneously introduce ambiguity in assessing human creativity, as observable artifacts may be partially or fully AI-generated. This paper reconceptualizes creativity as a distributional and process-based property that emerges under shared constraints and competitive incentives. We introduce a quantitative framework for measuring creativity as novelty in synthesis, operationalized through idea generation and idea transformation within embedding space. Empirical evaluation demonstrates that the proposed metrics align with intuitive judgments of creativity while capturing distinctions that surface-level quality assessments miss. We further identify a structural shift toward bimodal distributions of creative output in AI-mediated environments, with implications for hiring, leadership, and competitive strategy. The findings suggest that in the age of generative AI, distinctiveness rather than fluency becomes the primary signal of human creative capability.