# How Washmen Uses AI for Business Operations to Protect Luxury Garments > See how UAE retail laundry company Washmen uses AI Teammates for business operations, protecting luxury garments and cutting their damage rate to .5% Source: https://asana.com/resources/washmen-ai-business-operations ## How Washmen protects luxury garments—and customer trust—with AI Teammates When Andrew Villamor, Operations Associate at Washmen, starts his workday, hundreds of luxury garments are already moving through production at The Finery, the laundry company's luxury garment care service. Some are designer handbags; others are couture dresses, tailored suits, or delicate blouses from brands like Louis Vuitton, Chanel, Dior, and Tom Ford. Many are worth thousands of dollars, and every one requires precise treatment.  For a long time, the quality control operations at The Finery worked because the internal volume was manageable. Laundry experts relied on years of experience, remembered past damage cases, and applied that knowledge to each item they handled. But as The Finery grew to process up to 300 luxury garments every day, relying on human-centered institutional memory became harder. Even when previous damage investigations had been documented, expensive mistakes could recur if the right information wasn't available when someone needed it. Now, a network of [Asana AI Teammates](https://asana.com/product/ai/ai-teammates) works alongside the Operations team, bringing years of operational knowledge to the care of every garment that moves into production. The future of AI in Operations isn't about replacing people. It's about making your organization's knowledge impossible to lose. ## When operational knowledge needs to scale The Finery had always documented important information like damage investigations, brand-specific care instructions, customer cases, and lessons learned from previous issues.  But as operations grew, it became more difficult to rely on the institutional knowledge that lived in scattered documents and individual human memory. One incident highlighted that limitation. The team thoroughly investigated a damage case and documented the lessons learned. Weeks later, the exact same type of damage happened again.  "The problem wasn't that our experts became less skilled," Andrew says. "The problem was that human memory doesn't scale." Rather than looking for ways to help people remember more, Andrew started thinking about how to build a system that could remember _everything_—and make that knowledge available to everyone on the team. Instead of relying on one AI agent to do everything, he built a network of AI Teammates for his operations.  "We didn't build AI to replace expertise," says Andrew. "We built AI to make expertise scalable." ## How AI Teammates turn institutional knowledge into an operational system To make expertise scalable, Andrew embedded AI Teammates directly into Washmen’s existing workflow. Each teammate handles a specific part of the process—researching information, checking recommendations, or surfacing historical context—so people can make better decisions with less manual work. Here’s how it works: ### Step 1: The first AI Teammate assesses the garment's value When a garment enters The Finery, Washmen's internal sorting software automatically creates an Asana task containing everything the team already knows about the item, including brand, material, and care details. As soon as the task is created, Washmen’s first AI Teammate gets to work. Using the information already available in the task, it researches the approximate retail value of the garment and updates a corresponding custom field in the task automatically.  That additional context helps the team understand operational risk, identify trends across brands, and provide supporting information if a damage claim needs to be investigated. ### Step 2: The next AI Teammate validates treatment decisions Next, one of Washmen's laundry experts assesses the garment and recommends the appropriate cleaning and treatment methods. Once this assessment is complete, an AI Teammate reviews the recommendations and compares the proposed treatment against multiple sources including the garment's care label, Washmen's luxury brand care database, and years of internal operational knowledge.  If the teammate detects a potential issue, it flags the task, explains the concern, and asks the laundry manager to review the recommendation, leaving the human expert to make the final call before processing continues. ### Step 3: A third AI Teammate searches historical cases for potential risks At the same time that the assessment is happening, another AI Teammate searches years of previous damage investigations for similar garments. It compares the current garment to previous cases using factors like brand and material. If the Teammate finds a meaningful match, it adds the previous investigation directly to the task. It explains the potential risk, recommends what should be reviewed, and sends an immediate Slack notification to the laundry manager, so they can review the case before the garment reaches production. ### Step 4: If a damage claim occurs, the final AI Teammate steps in to review and assemble the case Damage cases at The Finery are rare, but when one does occur, another AI Teammate helps compile the investigation. The teammate reviews customer conversations, processing records, garment value, and operational details to determine whether the garment was handled according to the recommended treatment. The AI Teammate then gathers the investigation into a single case summary, recommends a compensation package, and drafts a response that the customer team personalizes before sending. ## Fewer damages, faster investigations For Washmen, the impact of [AI in Operations](https://asana.com/resources/operations-ai-statistics) has had a measurable impact. The company reduced its damage rate by 83%, helping protect both high-value garments and the customer trust that comes with them. Every week, the quality control AI Teammate identifies around 15 garments that need another review before reaching production, helping Washmen save an estimated 240,000 dirhams (~$65,000 USD) in potential damage costs each year.  When a damage claim does happen, investigations move faster because the relevant information has already been gathered. Instead of manually piecing together customer conversations, processing records, and historical cases, the team starts with a complete case summary that's ready for review. Before, our operation depended on what our experts could remember. Today, our experts are supported by a system that remembers everything we've learned. ## Stitching together knowledge for improved operations Every garment that passes through The Finery carries a different level of risk, and every customer expects their garment to be returned in perfect condition. The responsibility for making those care decisions still belongs to Washmen's experts. AI Teammates make sure they have the right information before they act. As every garment moves through the workflow, the system’s knowledge grows. Each completed investigation, quality review, and customer case adds to the knowledge available for the next one. For Andrew, the value of AI isn't just automation—it's capturing the expertise that already exists within a team. His advice for businesses building [AI into their operations](https://asana.com/resources/operations-growth-ai) is straightforward: don't start with AI. 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