AI Adoption: Opportunities and Challenges for Small and Medium Businesses

Last registered on January 27, 2025
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Pre-Trial

Trial Information

General Information

Title
AI Adoption: Opportunities and Challenges for Small and Medium Businesses
RCT ID
AEARCTR-0015262
Initial registration date
January 23, 2025

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
January 27, 2025, 10:00 AM EST

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
Christina Langer
Affiliation
Stanford University
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Other Primary Investigator(s)

PI Name
Angela Aristidou
PI Affiliation
UCL School of Management
Contact Investigator
PI Name
Erik Brynjolfsson
PI Affiliation
Stanford University

Additional Trial Information

Status
In development
Start date
2025-01-30
End date
2026-12-31
Keywords
Firms & Productivity
Additional Keywords
AI adoption, SMEs, workshops
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The impressive rise of Artificial Intelligence (AI) has sparked significant attention and awe but also concerns on what these new
technologies may mean for the economy and society. Especially small and medium enterprises (SMEs) exhibit room to grow regarding
AI adoption. However, less is know about the concerns SMEs have when it comes to AI adoption as well as about suitable interventions
to help them overcome these barriers.

We aim to study the impact of a designed intervention to increase AI adoption in the population of SMEs in the UK. This will give us the opportunity to analyze the effectiveness of a intervention targeted at increasing UK SMEs' AI adoption in a real world scenario.
External Link(s)

Registration Citation

Citation
Aristidou, Angela , Erik Brynjolfsson and Christina Langer. 2025. "AI Adoption: Opportunities and Challenges for Small and Medium Businesses." AEA RCT Registry. January 27. https://doi.org/10.1257/rct.15262-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-01-30
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
1. AI adoption metrics (Number of AI tools implemented, percentage of employees using AI, number of business functions using AI)
2. Business performance metrics (revenue, costs, productivity)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Implementation quality (AI action plan completion rate, percentage of staff trained in AI tool usage, implementation timeline achievement, staff satisfaction with AI tools)
2. Organizational development (AI readiness scores, data infrastructure development, staff attitude, implementation barriers)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study employs a randomized controlled trial with firm-level randomization. We will use a staggered treatment design with rolling workshop enrollment, targeting 500 firms across 10-12 workshops (40-50 firms per workshop). The study period spans 1 year with multiple surveys.
Experimental Design Details
Not available
Randomization Method
Randomization will be done in office by a computer. Treatment timing (workshop participation) will be randomly assigned with rolling sign-up of firms for workshops.
Randomization Unit
Firm
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 firms
Sample size: planned number of observations
500 firms
Sample size (or number of clusters) by treatment arms
We plan with 10-12 workshops with 40-50 firms each (500 firms in total). We will get sign-up lists for each workshop and randomly assign signed up firms to either treatment (workshop slot) or control (waitlist). Waitlisted firms will act as control and will get the workshop later. With a sign-up rate of 100 firms per workshop, we expect about 40 firms in the treatment group and 40 firms in the control group per workshop, accounting for no-shows.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
UCL LREC
IRB Approval Date
2024-11-21
IRB Approval Number
UCLSOM0197
Analysis Plan