What the New Research Actually Tells Us About AI Adoption in Garment Care
A new national study has placed laundry and dry-cleaning workers among the occupations using artificial intelligence at unexpectedly high rates.
According to the research, 49% of laundry and dry-cleaning workers reported using generative AI for at least one job-related purpose. Researchers had predicted an adoption rate of only 20.6% based on the occupation’s typical tasks.
That makes laundry and dry-cleaning workers one of the most “underpredicted” occupational groups in the study.
It is an attention-grabbing finding for the professional garment-care industry. But before we celebrate the idea that nearly half of the industry has embraced AI, we need to understand where the number came from and what it actually measures.
Where Did the Data Come From?
The findings come from an August 2026 National Bureau of Economic Research working paper titled What Work Does Generative AI Do?
The research was conducted by Alexander Bick, Adam Blandin, David Deming and Tyler Schumacher, who are affiliated with the Federal Reserve Bank of St. Louis, Vanderbilt University and Harvard University.
The researchers used data from the Real-Time Population Survey, an online labor-market survey designed to resemble the federal government’s Current Population Survey.
The occupational analysis combined four survey rounds conducted in:
- August 2025
- November 2025
- February 2026
- May 2026
Each survey round included approximately 5,000 American adults between the ages of 18 and 64. The final occupational analysis included 13,920 employed respondents with valid occupation and AI-use information.
The results were statistically weighted to make the overall survey more representative of the United States workforce.
How Many Dry-Cleaning Workers Were Surveyed?
This is the detail the headlines may not tell you.
Only 23 respondents in the pooled survey were classified specifically as “laundry and dry-cleaning workers.”
The 49% figure is a survey-weighted estimate based on those 23 responses.
That does not make the finding meaningless. It does, however, mean we should not treat it as conclusive evidence that exactly 49% of America’s garment-care workforce is using AI.
A group of 23 people is too small to represent every segment of our industry confidently, including owners, managers, customer-service representatives, production employees, route operators, pressers, spotters and alteration professionals.
The number should be viewed as a meaningful signal that deserves further investigation, not a final measurement of industry-wide adoption.
What Counted as Using AI?
The survey gave respondents a basic definition of generative AI and mentioned tools such as ChatGPT, Gemini and Midjourney.
Employed respondents were then asked:
“Do you use Generative AI for your job?”
A person only needed to answer yes to be classified as using AI at work.
That use could involve:
- Writing a social media post
- Researching a regulation
- Developing a promotion
- Drafting an employee procedure
- Responding to a customer
- Troubleshooting a piece of equipment
- Creating a marketing plan
- Summarizing information
- Preparing a business document
The survey did not require the respondent to use AI every day. It also did not require the business to have an AI strategy, employee training, approved workflows, privacy safeguards or measurable results.
This distinction matters.
Using AI once for a marketing idea is adoption for purposes of the survey. It is not necessarily operational transformation.
What Does the Predicted Rate Mean?
The study compared actual reported use with “exposure” scores designed to predict how useful AI should be within different occupations.
Researchers predicted that approximately 20.6% of laundry and dry-cleaning workers would use generative AI, based on the tasks normally associated with the occupation.
The actual weighted estimate was 49%.
The predicted number did not come from a second survey of cleaners. It was generated by a model that examined the work associated with the occupation and estimated how much of it could potentially be assisted by AI.
This may help explain why the industry was underestimated.
From the outside, dry cleaning can appear to be primarily manual work. People picture cleaning machines, presses, spotting boards, conveyors and garment racks.
But professionals inside the industry know the business involves far more.
Why Garment Care May Be More AI-Ready Than It Appears
Professional cleaners constantly work with information, judgment and communication.
They interpret care labels, research fabrics and embellishments, communicate risk, answer customer questions, manage claims, develop pricing, schedule production, document procedures, train employees, troubleshoot equipment and respond to regulatory requirements.
Owners and managers also handle marketing, hiring, customer retention, financial decisions, route planning and business development.
AI cannot press a garment, remove a stain or replace the technical judgment of an experienced cleaner. But it can assist with many of the information-heavy responsibilities surrounding that work.
That may be why garment-care professionals are using AI at rates higher than traditional occupational models expected.
The study’s authors reached a similar conclusion. They noted that occupations involving manual or face-to-face work may still contain substantial amounts of information gathering, planning and communication that AI can support.
My Perspective: Widespread Does Not Yet Mean Deep
As someone working directly with professional cleaners on AI education and implementation, I find the study encouraging. It confirms something I have seen firsthand: Our industry is more innovative and more willing to experiment than outsiders often assume.
But I would be careful about turning the 49% estimate into a victory lap.
The most important description in the study is that workplace AI adoption is “widespread but shallow.”
People across many occupations are experimenting with AI, but relatively few have integrated it deeply into the way their work gets done.
That is also the reality I see in garment care.
Some owners are using AI to create emails, promotions, job descriptions or social media content. Those are valuable starting points. However, occasional prompting is not the same as building a reliable AI-enabled operation.
Meaningful adoption happens when a business can answer questions such as:
- Which business problems are we using AI to solve?
- Which tasks should remain completely human-led?
- What information can and cannot be entered into an AI platform?
- How are employees being trained?
- Who reviews the output?
- How do we prevent technical promises or inaccurate customer guidance?
- Are we saving time, reducing errors or producing a measurable business result?
- Can the process be repeated consistently?
Without those answers, a business may be using AI without becoming more capable.
Moving From AI Awareness to AI Readiness
The gap between trying AI and implementing it responsibly is the AI Readiness Gap.
Closing that gap requires more than access to technology. It requires clear processes, reliable information, employee preparation, human oversight and a connection between the tool and an actual business objective.
For a professional cleaner, that could mean using AI to:
- Turn the owner’s verbal knowledge into documented procedures
- Organize garment-risk information without making unauthorized technical promises
- Prepare customer follow-up communications
- Build employee training materials from approved practices
- Analyze recurring customer questions or operational bottlenecks
- Develop marketing based on real customer needs
- Improve internal communication and accountability
- Support business decisions with organized information
The objective is not to use AI everywhere.
The objective is to use it intentionally where it can strengthen the business while preserving the human expertise, technical judgment and customer trust that professional garment care requires.
What Should the Industry Take Away From the Study?
We should not say that this study proves nearly half of all cleaners have fully adopted AI.
We can say that a nationally weighted survey found unexpectedly high experimentation with generative AI among people classified as laundry and dry-cleaning workers.
That alone challenges the assumption that AI belongs only in technology companies, corporate offices and traditionally digital occupations.
Our industry is not standing on the sidelines.
But the real opportunity is still ahead of us.
The next stage will not be measured by how many cleaners have opened ChatGPT. It will be measured by how many businesses use AI responsibly to improve decisions, develop people, document knowledge, strengthen customer relationships and build operations that do not depend entirely on what one person knows.
The 49% number is not the end of the story.
It is an invitation to find out what meaningful AI adoption in professional garment care can actually become.
Research Sources
The research discussed in this article comes from the National Bureau of Economic Research working paper What Work Does Generative AI Do? and the Federal Reserve Bank of St. Louis summary published September 1, 2026.
The public occupation-level dataset reports that the laundry and dry-cleaning estimate was calculated from 23 unweighted respondent observations across the four pooled survey periods.
Sources: NBER working paper, Federal Reserve Bank of St. Louis analysis, public occupational dataset
Frequently Asked Questions About AI Use in Dry Cleaning
Are 49% of dry-cleaning workers really using AI?
A nationally weighted survey estimated that 49% of people classified as laundry and dry-cleaning workers used generative AI for at least one job-related purpose. However, the estimate was based on only 23 respondents in that occupation. It should be viewed as an indication of unexpected AI experimentation, not definitive proof that 49% of the entire industry regularly uses AI.
How many laundry and dry-cleaning workers participated in the study?
The occupational dataset included 23 respondents classified as laundry and dry-cleaning workers. The complete analysis included 13,920 employed respondents across hundreds of occupations.
When was the AI workplace survey conducted?
The researchers combined four rounds of the Real-Time Population Survey conducted in August 2025, November 2025, February 2026 and May 2026.
Where did the dry-cleaning AI statistic come from?
The statistic comes from the National Bureau of Economic Research working paper What Work Does Generative AI Do? The study was conducted by researchers affiliated with the Federal Reserve Bank of St. Louis, Vanderbilt University and Harvard University.
What counted as using generative AI at work?
Respondents were asked whether they used generative AI for their jobs. A person could be counted as an AI user after using a tool such as ChatGPT, Gemini or Midjourney for at least one work-related purpose. The question did not require daily use or full integration into business operations.
Why was AI use among dry-cleaning workers higher than predicted?
Traditional AI-exposure models may underestimate the amount of information-based work involved in professional garment care. Although cleaning and finishing garments require hands-on expertise, the business also involves research, customer communication, marketing, training, documentation, regulatory compliance, troubleshooting and operational decision-making.
How can dry cleaners use AI in their businesses?
Dry cleaners can use AI to help document procedures, prepare marketing, organize customer communications, develop employee training, research regulations, analyze business bottlenecks and support operational decisions. AI output should be reviewed by a qualified person, especially when it involves garment risks, customer claims or technical recommendations.
Can AI replace an experienced dry cleaner?
No. AI cannot replace the hands-on skill, garment knowledge and professional judgment of an experienced cleaner, spotter, presser or alteration specialist. It can support professionals by organizing information, drafting communications and assisting with selected administrative and decision-support tasks.
What does “widespread but shallow” AI adoption mean?
“Widespread but shallow” means that people across many occupations have tried or used generative AI, but the technology is often used for only a small portion of their work. Occasional use does not necessarily mean AI has been integrated into approved, repeatable and measurable business processes.
What is the difference between AI adoption and AI readiness?
AI adoption means that someone has started using an AI tool. AI readiness means the organization has the processes, policies, training, data practices, human oversight and business objectives required to use AI responsibly and consistently. A business can have high AI use while still having a significant AI Readiness Gap.
What should garment-care businesses do before implementing AI?
Before expanding AI use, a garment-care business should identify the business problem being addressed, establish rules for sensitive information, determine which decisions require human approval, train employees, verify AI-generated output and define how success will be measured.
What is the main takeaway from the study?
The study does not conclusively prove that half of all dry-cleaning workers have adopted AI. It does suggest that workers in hands-on occupations are finding more uses for generative AI than conventional models predicted. The next opportunity is to turn that experimentation into responsible, repeatable and measurable business capability.
