Artificial intelligence (AI) is already reshaping the textile industry—not with flashy headlines but with practical improvements in planning, sourcing and execution. Manufacturers are using AI to improve operations, avoid common pitfalls and align teams for long-term success, with insights on reshoring, leadership and continuous improvement integration.
In a recent discussion about AI, a plant manager said, “I don’t need fewer people. I need fewer surprises.” He wasn’t chasing shiny tech; he was simply fed up with late orders, quality issues and constantly reacting after the fact. He thought AI might be the key—but only if it helped his team get in front of issues instead of constantly playing “catch-up.”
He is not alone. That mindset is spreading across the textile industry. AI isn’t some future trend; it’s already being used—quietly and effectively—to improve everything from order entry to sourcing decisions. The bigger question is: What do we do with it next?
Informed decisions
AI thrives in complex environments like textiles, where there’s no shortage of data but rather, less time to act on it. Firsthand evidence has shown how AI can make something as tedious as order entry faster and smarter. In one case, a manufacturer used it to learn from past transactions and streamline the process.
No massive enterprise resource planning overhaul. No big training effort. Just cleaner inputs, quicker processing and a smoother experience for both the team and the customer. That’s where AI really shines—in small, focused ways that make work easier.
Supporting human expertise
Some are concerned AI will replace jobs, but so far, that’s not what the industry is showing. The best use cases are designed to support the people who already know the work. These are the planners who now can see trends sooner, maintenance teams who get early alerts before something breaks and supply chain managers who can shift plans based on live insights rather than yesterday’s data.
AI doesn’t eliminate intuition—it enhances it. And when your team trusts the system, that’s when real improvement starts.
Escaping ‘pilot purgatory’
Many companies begin working with AI by running a pilot program, getting a few cool dashboards then … nothing. The project dies in isolation. But in one portfolio group we supported, things looked different. Instead of limiting AI to one department, the group used it to analyze supply chain trends across its entire brand portfolio.
Patterns appeared that had been hiding in the “noise.” Teams started adjusting proactively, not reactively, and they saw measurable impacts on margins and delivery times. That kind of success doesn’t come from tech alone; it comes from alignment across people, processes and priorities.
A new era of reshoring
AI also has a growing role in the reshoring conversation, but bringing production back to the U.S. is tough. Labor costs, logistics and speed-to-market pressures are real. But AI gives domestic manufacturers a fighting chance.
By improving planning accuracy, automating time-consuming steps and helping teams do more with less, AI makes it easier to keep operations local and competitive. And it’s not just on the plant floor.
In apparel and fashion, for example, AI tools can speed up the design-to-launch cycle by spotting trends early and recommending product concepts, driving faster launches and cutting down on waste both in fabric and inventory. That’s not just a productivity boost—it’s agility. And in this market, agility can be the edge that keeps a business from falling behind.
What’s next for leaders?
The first step isn’t about which AI platform to use but rather about identifying the real issues your team deals with every day, such as late orders, quality defects and inconsistent forecasts and then asking, “Where could AI help us improve?” From that point on, it comes down to trust.
People won’t use a system they don’t understand or believe in. That’s why it’s crucial to bring operations, information technology and continuous integration (CI) together early and to communicate not just what the tool does but why it matters.
Failure is OK
It’s no secret that a lot of AI efforts fall flat. Some estimates say the majority never make it past the pilot phase. But that’s not necessarily failure; it’s part of the learning curve. Most of the time, the issue isn’t the model but the rollout. Teams don’t trust the data, the output doesn’t match real-world needs or the change feels too disconnected from how things already work.
That’s where CI teams come in. They already know how to define problems, align stakeholders and measure impact. When they’re involved early, they help shape the AI effort into something people can actually use—and want to use.
In one upholstery mill, the team had a solid AI forecasting tool, but no one was using it. After looping in the CI group, they embedded it into daily huddles and decision-making routines. Forecast accuracy went up, and so did adoption. The algorithm helped, but what really made the difference was getting people on the same page.
The leadership shift
Adopting AI isn’t just about learning new tools; it’s about rethinking how decisions are made. That’s a big shift, especially in industries like textiles where operational wisdom often lives in people, not platforms.
Leaders who used to rely on intuition or tribal knowledge are now being asked to interpret recommendations generated by algorithms. That transition doesn’t happen overnight. It requires a culture where questions are encouraged, where data literacy is valued and where frontline teams feel empowered to engage with digital tools rather than be intimidated by them.
Teams can succeed by pairing seasoned operators with data analysts, encouraging shared ownership of the solution. No one is trying to turn every operator into a data scientist. It’s about helping people see AI as an assistant and not a critic as well as creating space for collaboration between logic and experience.
Action over hype
AI isn’t a silver bullet, but it’s also not science fiction. For textile companies ready to think strategically, it’s a practical way to improve how teams operate, how customers are served and how decisions are made. It’s best to start small and keep your team in sync. Ignore the hype and just aim for better outcomes.
In the end, AI isn’t just another tool but a shift in how leaders think. The ones who thrive are open, humble and ready to keep learning. And that’s where the value is.
Todd Morgan is the chief growth officer at Infopine, vice president of Supplier Strategy at SimplSourcing and the creator of AI-6X™, a strategic framework for guiding human-led artificial intelligence (AI) transformation. He is a graduate of NC State’s Wilson College of Textiles and has more than 30 years of experience working with manufacturers, designers and business leaders to use AI to solve operational challenges.
At the Advanced Textiles Association Outlook® Leadership Conference in Green Bay, Wis., this summer, Elaine Stephens, head of customer engineering, Wisconsin, for Google Cloud, energized attendees with ideas on how artificial intelligence (AI) can be used in business. She said the time to start planning how to use it is now, because employees are likely already using it, sanctioned or not by their company. It’s better to provide vetted tools for tasks and establishing company procedures for its use.
“Humans are slow to change,” Stephens noted but also added that at some point, humans who don’t know how to use AI will be replaced by those who do. People’s expertise is still needed to review and tweak AI’s results, but AI can also be used as a tool to enhance worker productivity. And it’s improving all the time.
“If you tried something six months ago and it didn’t work, don’t put it down for too long,” she said.
Every company will have to decide for itself what tasks could be assisted by AI tools, she advised, but the technology can be a great equalizer in helping smaller companies implement things in areas where they may not have the capacity today.
A few of the examples Stephens gave included: the ability to have customer service or call centers assist customers in multiple languages; drafting requests for proposal (RFP) documents, referencing from an internal library of every RFP that a company has ever written up in the past; composing performance reviews; creating digital samples or design ideas; digging through supply chain data; inspecting products for defects; summarizing studies and long documents—even creating podcast episodes about the information, with non-human speakers. She used AI to assist her in creating her Outlook presentation, and then it helped her critique her delivery as she practiced it.
“AI is the worst it will ever be today,” she said. “If you start too late, you’re really, really far behind.”
—Cathy Jones
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