AI Tool Use, Take-Up, and Learning

Last registered on May 27, 2026
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Pre-Trial

Trial Information

General Information

Title
AI Tool Use, Take-Up, and Learning
RCT ID
AEARCTR-0018655
Initial registration date
May 19, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
May 27, 2026, 10:06 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Gideon Moore
Affiliation
Stanford Economics Department
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Other Primary Investigator(s)

PI Name
Nicholas Bloom
PI Affiliation
Stanford University
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Additional Trial Information

Status
On going
Start date
2024-06-01
End date
2026-12-31
Keywords
Firms & Productivity, Labor
Additional Keywords
Artificial Intelligence
JEL code(s)
O, J, M
Secondary IDs
Stanford IRB: eProtocol 87086
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Despite the rapid rise of AI tools in knowledge work, there is limited evidence describing their effects on productivity or worker outcomes. In this project, we study the rollout of an AI tool suite to sales and support workers at a large technology firm over the course of several quarters in 2024-2025. We assess AI tool use not only on standard performance outcomes (e.g., sales, profits), but also on worker- and customer-focused outcomes: are gains driven by learning, where workers improve, or by selection, where less-productive workers attrit? When firms expand capacity with AI, which customers receive extra benefits? Findings will help firms, workers, and policymakers understand the labor market consequences of generative AI: who benefits, and through what mechanisms.
External Link(s)

Registration Citation

Citation
Bloom, Nicholas and Gideon Moore. 2026. "AI Tool Use, Take-Up, and Learning." AEA RCT Registry. May 27. https://doi.org/10.1257/rct.18655-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2024-06-01
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
We are interested in both employee and customer outcomes of the experiment.

Employee outcomes:
* Pitch rate (replicating partner's prior analysis)
* Win rate (replicating partner's prior analysis)
* Take-Up
* Satisfaction
* Attrition
* Subsequent learning
* Heterogeneous effects by skill and tenure

Customer outcomes:
* Satisfaction
* Future business interactions with large tech firm
* Business success
* Heterogeneous effects by firm size and tenure
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is a randomized controlled trial conducted by a large tech firm in the ordinary course of business. Sales and support employees were randomized into a treatment arm, which received access to a suite of generative AI tools (including real-time call-listening AI assistants that surface suggestions to workers), and a control arm, which continued to work without access to these tools. The experiment ran across several quarters in 2024-2025, after which the AI tool suite was rolled out company-wide. Stanford researchers do not interact with participants and observe only aggregate analyses produced by the large tech firm's causal inference team based on Stanford-directed analysis code.
Experimental Design Details
Not available
Randomization Method
Computer
Randomization Unit
Employee and firm. Employees were randomly assigned to treatment or control, and all employees a given firm interacted with were in the same treatment group.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
All employees in this subsidiary of the large tech firm--thousands, along with their thousands of customers.

Customers are randomized *with* employees; that is, although a customer may speak to different employees on different calls, all employees a customer speaks to will be within the same treatment arm.
Sample size: planned number of observations
Same as cluster
Sample size (or number of clusters) by treatment arms
Determined internally by tech firm--will be disclosed to researchers following NDA
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford University Institutional Review Board (Non-Medical Panel)
IRB Approval Date
2026-05-15
IRB Approval Number
87086
Analysis Plan

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