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Alexia Cambon

Publications and source records attributed to Alexia Cambon.

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Scaffolding Human-AI Collaboration: A Field Experiment on Behavioral Protocols and Cognitive Reframing

Organizations have widely deployed generative AI tools, yet productivity gains remain uneven, suggesting that how people use AI matters as much as whether they have access. We conducted a field experiment with 388 employees at a Fortune 500 retailer to test two scaffolding interventions for human-AI collaboration. All participants had access to the same AI tool; we varied only the structure surrounding its use. A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use and substantially lower document production. A cognitive scaffolding intervention (partnership training that reframed AI as a thought partner) was associated with higher individual document quality at the top of the distribution. Treatment participants also showed greater positive belief change across the session, though sensitivity analyses suggest this likely reflects recovery from carry-over effects rather than genuine training-induced shifts. Both findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length.

econ.GN

Evolving the Productivity Equation: Should Digital Labor Be Considered a New Factor of Production?

As the digital economy grows increasingly intangible, traditional productivity measures struggle to capture the true economic impact of artificial intelligence (AI). AI systems capable of cognitive work significantly enhance productivity, yet their contributions remain obscured within the residual category of Total Factor Productivity (TFP). This paper explores whether it is time for a conceptual shift to explicitly recognize "digital labor," the autonomous cognitive capability of AI, as a distinct factor of production alongside capital and human labor. We outline the unique economic properties of digital labor, including scalability, intangibility, self-improvement, rapid obsolescence, and elastic substitutability. By integrating digital labor into growth models (such as those by Solow and Romer), we demonstrate strategic implications for business leaders, including new approaches to productivity tracking, resource allocation, investment strategy, and organizational design. Ultimately, treating digital labor as an independent factor offers a clearer view of economic growth and helps organizations manage AI's transformative potential.

econ.TH

Early Impacts of M365 Copilot

Advances in generative AI have rapidly expanded the potential of computers to perform or assist in a wide array of tasks traditionally performed by humans. We analyze a large, real-world randomized experiment of over 6,000 workers at 56 firms to present some of the earliest evidence on how these technologies are changing the way knowledge workers do their jobs. We find substantial time savings on common core tasks across a wide range of industries and occupations: workers who make use of this technology spent half an hour less reading email each week and completed documents 12% faster. Despite the newness of the technology, nearly 40% of workers who were given access to the tool used it regularly in their work throughout the 6-month study.

econ.GN