CHICAGO — With remarkable speed, artificial intelligence — AI — has gone from a poorly understood buzzword to an invaluable tool for many companies, including dry cleaners. These early adopters aren’t just using AI to polish their emails — they’re automating customer service, analyzing complex data patterns and even training staff with tools that seemed like pure fantasy just a few years ago.
In Part 1 of this series, we examined how some cleaners have discovered what AI can do for them in their everyday tasks. Today, we’ll continue by seeing some other surprising places that cleaners are finding to put artificial intelligence to work.
Practical Applications
The specific applications of AI in drycleaning operations can be as varied as the businesses themselves. At Dublin Cleaners in Columbus, Ohio, Sea Khun, the company’s administrative assistant with an IT background, has integrated AI into virtually every aspect of his workday, which begins with email management and extends to complex automation projects.
“I create custom scripts that I can run when I want to compare data, using color filtering, for example,” Khun says. “We have Wednesday meetings with our upper management, and I’ve created a custom shading system to display certain goals. If we meet the goals, it’s green. If we’re below our goals, it’s red.”
For customer service, AI chatbots have become increasingly sophisticated. Dublin Cleaners uses a system that crawls its website to provide informed responses to customer inquiries.
“Those replies can go on Google and Yelp,” Khun says, “and then on our direct website as well.”
Robert Strong, president of California’s Country Club Cleaners, has found AI particularly valuable for research and due diligence.
When considering acquiring another business in Monterey, Calif., he needed to understand the local water quality. Rather than calling utility districts and trying to interpret technical reports himself, Strong simply asked AI to compare Monterey’s water quality with that of his existing facilities.
“The AI came back and told me the water quality is much better than our Livermore facility, but not nearly as good as our San Ramon location,” Strong says. The AI even explained why, breaking down the mineral content differences between locations. “I don’t know how to read a damn water report. AI just did it all for me.”
Personalization at Scale
Rachelle Balanzat, CEO of upscale drycleaning service Juliette in New York City, believes that one of AI’s most powerful applications for dry cleaners is the ability to personalize customer interactions at scale. She uses AI to tailor messaging based on customer behavior patterns.
“If someone hasn’t used us in three weeks, they get a check-in text,” she says. “If someone prefers 5 p.m. pickups, our system learns that. Some of the most ‘human’ parts of our service experience are powered by data.”
This level of personalization extends to operational efficiency as well. At Juliette, AI supports logistics, analyzes revenue patterns and even helps train new staff members.
“We’ve cut down on missed pickups, increased repeat orders and reduced customer churn — all through smart automations,” Balanzat says.
Training and Documentation
The same AI capabilities that personalize customer experiences are also streamlining behind-the-scenes operations.
One of AI’s top strengths for dry cleaners is in creating training materials and internal documentation. Khun regularly uses AI to write policies, training guides and internal communications, including bilingual versions for Spanish-speaking team members.
“It’s as simple as telling these AI chatbots, ‘Hey, I also need a Spanish version of it,’” he says. “It’ll then spit out both the English version and the Spanish version.”
The quality of these translations has impressed even native speakers.
“I’ve asked our Spanish-speaking employees who also understand English to review these,” Khun says. “I’ll ask, ‘Does this sound right to you?’ and they tell me it’s very close, with no changes needed.”
Overcoming Initial Resistance
Despite AI’s growing capabilities, adoption isn’t always smooth. The biggest challenge, according to Khun, is often psychological rather than technical.
“People think that because it’s technology and they hear ‘AI,’ they believe it’s probably going to be hard to learn,” he says. “It’s not hard to learn. You speak to it in plain language.”
Strong has found that, while there is a learning curve and the AI’s work still needs to be double-checked, the benefits far outweigh the initial investment in learning.
“I watched an hour’s worth of videos and found that there were so many things I could do with it just from that,” he says.
Success with AI implementation also depends on leadership support. Khun credits Dublin Cleaners owner Brian Butler with fostering an environment where AI experimentation is encouraged.
“He’s been extremely curious about it every step of the way,” Khun says. “He’s been motivational, asking, ‘Let’s see what this can do.’ It pushed me to experiment more, to try harder and to make it happen.”
Balanzat has found that customer buy-in really hasn’t been a factor, since many customers don’t realize they are interacting with AI-powered systems.
“The best part? They just feel like Juliette ‘gets them,’” Balanzat says. “We’ve seen that those who do engage with the tech, like our AI concierge, are impressed. We’ve had customers say, ‘Wait, that wasn’t you?’”
Come back Thursday for the conclusion of this series, where we’ll look at some of the AI platforms available to dry cleaners, as well as how to get started and what may come. For Part 1 of this series, click HERE.
Have a question or comment? E-mail our editor Dave Davis at [email protected].
