Generative AI and Technology Adoption in Organizations

Last registered on July 13, 2026
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Pre-Trial

Trial Information

General Information

Title
Generative AI and Technology Adoption in Organizations
RCT ID
AEARCTR-0018777
Initial registration date
July 07, 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
July 13, 2026, 7:43 AM EDT

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

Locations

Country
Uganda
Region
National

Primary Investigator

Name
Svenja Friess
Affiliation
LMU Munich
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Other Primary Investigator(s)

PI Name
Silvia Castro
PI Affiliation
INSEAD & LMU Munich
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PI Name
Maren Alana Mickeler
PI Affiliation
ESSEC Business School
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PI Name
Hoa Ho
PI Affiliation
LMU Munich
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PI Name
Evelina Grinuite
PI Affiliation
LMU Munich
Contact Investigator
PI Name
Svenja Friess
PI Affiliation
LMU Munich

Additional Trial Information

Status
In development
Start date
2026-07-01
End date
2027-12-31
Keywords
Behavior, Finance & Microfinance, Firms & Productivity, Gender
Additional Keywords
Generative AI, field experiment, technology adoption, peer effects
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Firms are increasingly deploying GenAI tools, however, we lack evidence on effective implementation strategies. We study whether designating a peer facilitator improves the frequency and the quality of employee engagement with a new GenAI tool. Partnering with a commercial bank that introduced a customized GenAI credit-assessment tool, we randomly assign in half of the branches a designated employee who supports colleagues' use of the tool. We measure effects on (i) tool usage patterns and (ii) the quality of loan officers' credit-assessment decisions, and (iii) officer-level heterogeneity in adoption and usage. The design allows us to isolate the causal effect of peer facilitation on GenAI tech adoption, informing organizational strategies for AI implementation beyond simple tool provision.
External Link(s)

Registration Citation

Citation
Castro, Silvia et al. 2026. "Generative AI and Technology Adoption in Organizations." AEA RCT Registry. July 13. https://doi.org/10.1257/rct.18777-1.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
All branches in the study receive a generative AI credit-assessment tool and standard training. The tool is customized and trained on the firm’s internal information base (policies, product databases, procedural manuals, past product to client matches) and employees’ knowledge. The main objective of the tool is to assist employees on the credit analysis of loan applications. In each treatment branch, one or more employees are additionally designated as GenAI facilitator: they take on a support role — encouraging colleagues use the tool, troubleshooting problems, and acting as a knowledge broker (within and across organizational units). Facilitators are identified before deployment targeting employees who are both helpful and technology-affine. Control branches receive the tool and training without a designated facilitator.
Intervention Start Date
2026-09-01
Intervention End Date
2027-12-31

Primary Outcomes

Primary Outcomes (end points)
Adoption of the tool, and the quality of officers' use of the tool
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The partner bank has 80 branches, with the tool rolled out in two phases. In the first phase, 40 branches receive the tool and enter this study: they are formed into 20 matched pairs, and within each pair one branch is randomly assigned a facilitator and one is not, yielding 20 treatment and 20 control branches. This first-phase sample is confirmed and constitutes our core sample.

Contingent on the partner firm's continued participation, we plan to extend the study to the remaining 40 branches when the tool is rolled out to them in a second phase (i.e., after the intervention concluded) using the same matched-pair randomization to add 20 further treatment and 20 further control branches — for a total of 40 treatment and 40 control (80 branches) if the second phase proceeds.
Experimental Design Details
Not available
Randomization Method
Pair wise matched randomization
Randomization Unit
Branches
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
40 (+40 if operationally possible)
Sample size: planned number of observations
All officers (except salary bankers)
Sample size (or number of clusters) by treatment arms
20 control branches 20 treatment branches
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
LMU Munich
IRB Approval Date
2026-04-11
IRB Approval Number
ETH-SOM-065
IRB Name
ESSEC Business School
IRB Approval Date
2026-03-10
IRB Approval Number
226-017
IRB Name
Makarere University CoBAMS-REC
IRB Approval Date
2026-05-11
IRB Approval Number
CoBAMS-REC-2026-686
Analysis Plan