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EXCEL IS NOT ENOUGH: HOW AI POWERS SMARTER PLANNING
FFORECASTING TODAY is a complex game. As retailers contend with changing customer behavior, tariffs and other market moves, manual planning can’t keep up.
Consultancy AlixPartners is arming brands with artificial intelligence-powered modeling solutions that can support smarter decision-making in sourcing, promotions and more. Here, Sonia Lapinsky, partner and managing director at AlixPartners, discusses the impact of AI and why people remain an important piece of the technology puzzle.
SOURCING JOURNAL: In this era of uncertainty, what are the limits of traditional forecasting methods?
Sonia Lapinsky: Typically, brands are forecasting with a topdown, finance-driven approach based on historic trends, and then they end up struggling to connect their forecasts to the actual strategies of each business function. Although retailers have used this method for decades, it’s now a larger problem because business has become more challenging to predict. Consumers are more influenced by outside forces—friends, social media, influencers—than by brands themselves. The legacy way of forecasting has broken down. We keep seeing swings, from excess inventory and markdowns to insufficient stock, which negatively impacts sales because brands don’t have the products customers are looking for.
How can AI help retailers make more granular, accurate decisions?
S.L.: For answering complex questions, using something like Excel to create a forward-looking
“THE LEGACY WAY OF FORECASTING HAS BROKEN DOWN.”
projection simply becomes unmanageable. There are now more sophisticated ways to forecast leveraging advanced technology, AI and machine learning. Our AI Profit Engine starts with several years of a retailer’s transaction history down to the SKU level. It pairs that information with the customer file and brings in outside factors—for example, trends, macroeconomic factors, environmental and weather conditions—and builds a language learning model that runs regressions to determine exactly which factors are influencing that customer’s spend. One area we’ve been addressing with customers is pricing and promotion effectiveness. So many retailers have these broad, fullstore discounts at roughly the same time every year, indicating it’s probably just historic behavior driving them. We can build and
hone an AI/ML elasticity model to maximize either revenue or margin and give the right mix for promotion timing, depth and product to include or exclude. This type of exercise can result in margin improvements of around 200 to 300 basis points.
How are you using data and AI to help retailers enhance supply chain management and minimize the tariff impact?
S.L.: Understanding where production should be allocated and its impact on total cost, lead time and risk factors has been incredibly important over the last few months. We don’t know what’s going to happen after the pause. If a retailer hasn’t moved as much of their product into more beneficial countries as they should have, or may need to move more, AI tools can help with planning. Pulling in tons of data on imports, detailed
cost breakdown—including labor rates and raw material prices for different categories and product types—helps model what different product shifts could look like. It also shows how a given brand or retailer’s sourcing map compares to competitors.
AI is powerful, but there are still people in the driver’s seat. How can companies set teams up to get the most out of this technology?
S.L.: You need the right team at the table with the expertise to size the prize and understand the business implications, cost and timing of putting AI in place. There are many different places you could deploy new technology, so you need that experience and know-how to narrow down priorities, run assessments and determine where you’re going to get your best, quickest return.
Once you decide to test new technology, you need the right team on the ground to ensure you’re equipped to read and react to results. Then comes a major change management program to get the rest of the organization behind this and train employees for a new way of working. ■
SONIA LAPINSKY , partner and managing director, AlixPartners
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WILL TECH TARIFFS SLOW U.S. GROWTH?
by Kate Nishimura
▲ Desma’s “bread and butter”—direct injection molding machines—allow
footwear manufacturers to produce foam midsoles for performance shoes and sneakers.
the tariff roller coaster rumbles on, and for American manufacturers, there’s no getting off the ride anytime soon.
c Even with a drastic reduction in duties on China-made goods and a respite from global “reciprocal” tariffs, the looming threat of taxes on foreign wares will continue to sow confusion.
c For some footwear, apparel and textile manufacturers based in the United States, tariff turmoil has been a boon to business, and for others, it’s resulted in orbiting by brands and retailers that are voicing interest but aren’t quite ready to pull the trigger on onshoring.
c The uncertainty of President Donald Trump’s tariff strategy hasn’t just paralyzed retail decision-makers, but the supply chain. Without clarity about the future of trade policy, manufacturers are left wondering about when to scale up—and how.
c Much of the U.S. manufacturing landscape, across sectors and applications, relies on advanced, automated technologies that take the place of cobbler’s benches and traditional sewing machines. Some next-generation processes, like 3D printing, are on the verge of breaking through as production drivers in the U.S.
c But all of these activities, new and traditional, rely on machinery, much of which is sourced overseas and, under the current tariff regime, subject to potentially onerous duties.
“There’s a combination of issues happening right now. I think uncertainty in the marketplace has stymied some orders from coming to fruition, because people are wondering how the 90-day pause will conclude,” said Kim Glas, president and CEO of the National Council of Textile Organizations (NCTO), regarding the opportunities coming to American producers.
According to Glas, the rapid evolution of trade policy may be driving up interest in American manufacturing, but it’s not providing any clarity for producers that are trying to understand their place in the puzzle. In the absence of a clear path forward, the textile sector is waiting until July 9—the date that the deferral of reciprocal duties concludes—to see “what kind of market signals” will materialize.
NCTO has long been supportive of holding “trade predators” like China and Vietnam accountable for non-market activities. “But we have also advocated, including in the first Trump administration, for a few exceptions that we think are critical,” Glas said.
Access to state-of-the-art textile manufacturing equipment is necessary to help improve processes at the nation’s plants, both in order to drive efficiency and costcompetitiveness. But those upsides come with a hefty price tag. “It’s very expensive,” Glas said.
hail from Europe, and of course, China.
Glas stressed the tightness of margins in a price-competitive industry like textiles, where companies are likely to spring for the lowest-cost option. The risk in not having access to advantageous technologies is that foreign operators will gain those capabilities, underscoring their own attractiveness.
“We have to think about this in a holistic way. If the design is to unleash more U.S. investment, we’re all for that. We want to see our U.S. textile industry grow and we need the administration’s help,” Glas said. “But there is a recognition across the industry that a lot of the textile machinery is no longer made here, and will not be made here overnight. So we need to have a special dispensation for that.”
The NCTO lead said exemptions for production machinery could be written into potential forthcoming trade agreements or tariff regimes, or an exclusion process could be established after tariffs are reinstated (as was the case with Trump 1.0’s Section 301
THERE IS A RECOGNITION ACROSS THE INDUSTRY THAT A LOT OF THE TEXTILE MACHINERY IS NO LONGER MADE HERE, AND WILL NOT BE MADE HERE OVERNIGHT.”
NCTO
“When you apply a 10-percent tariff, or another tariff differential, it can make a real difference about whether or not you can afford to reinvest in your operation,” she explained. “When you do a big capital expenditure like that, you have to amortize those costs over a period of time.” But duty costs are paid upfront. And on a machine that costs tens, if not hundreds of thousands of dollars, even a 10-percent duty could be make-or-break for a small operation.
There are significant limitations to such equipment production in the U.S., Glas explained, and the advanced machinery is essential to most modern operations. Devices used for extruding, drawing, texturing or cutting man-made textile materials aren’t made stateside. Many of these technologies
duties on China). Either way, she hopes decisions surrounding solution for American manufacturers are “expeditious.”
“The tariffs are definitely making the machinery more expensive,” Mitch Cahn, owner of Newark, N.J.-based apparel and gear manufacturer Unionwear, told Sourcing Journal.
The producer, which specializes in items like baseball caps and tote bags, imported machinery earlier this year from Canada, before Trump’s tariff announcements. “We didn’t make the investment because we expected tariffs, we actually made the investment to ramp up for the U.S.A.’s 250th birthday” in 2026, he said. Cahn anticipates a surge of business surrounding the occasion, along with events like the World Cup and the Olympics.
According to the business owner, doubling
down on automated machinery (this time, on an apparatus that sews canvas totes) was a matter of necessity. “We were having a lot of difficulty hiring more sewers; the pool was dry,” he said. “We had to invest in machinery to make up the gap between what we were doing and what we want to be able to do next year.”
The tote bag machinery, when it’s operating at full speed, will do the work of 44 people with a single operator. “We’re still ramping up; the goal is to have it probably operating 18 to 20 hours a day by the end of the year,” he said.
“We didn’t do it to speed up or save money. We just did it because there was really no way for us to grow linearly—not even exponentially,” he explained. “There’s no way for us to keep adding sewers to our operation, so we need it.”
These planned investments in technology aren’t a bid to replace human headcount with machines—in fact, Unionwear is holding on tight to the sewers that it employs. One prevailing issue inhibiting the growth of American manufacturing is the lack of a pipeline for skilled, affordable labor. The group has plans to automate other production processes, and is in talks with American manufacturers for those projects.
Since the reciprocal duties on more than 60 countries were announced, Unionwear has seen a “considerable” increase in interest and sales, Cahn said.
“If the tariffs are here to stay, the return is actually going to be much greater than it would have been without the tariffs,” he said of the investment in new technology. “And the reason for that—and it’s something we didn’t expect—is the possibility that with automation, we actually can be competitive with import prices that have tariffs on them.”
Cahn said that even if the Canadian-made machinery was tariffed at the 25-percent rate that Trump originally threatened, the company still would have made the buy. “It really opens up a much bigger market for us,” he added.
Kuba Graczyk, founder of Los Angeles-based 3D-printed footwear startup Koobz, agreed that investments in automation are key to expansion as a U.S. producer.
The group, which prints mono-material, single-piece shoes, currently operates 60 3D printers and is building out a factory in Ventura, Calif. that Graczyk said will house 4,000 printers at the end of the next two years. This will give the startup the ability to churn out “a couple million pairs of shoes a year,” he estimates.
Since April 2—Trump’s so-called “Liberation Day”—business has picked up, he added.
“Customers who were actively working with us decided to substantially accelerate, customers who were just, like, looking at this as something interesting decided to launch projects with us to see where it could go instead of being hesitant,” Graczyk said. “And those folks who ghosted me suddenly decided, ‘Hey, let’s get on that again.’”
“Of course, it tapered down” over the course of the ensuing weeks, which saw reductions in duties on China-made goods, deferrals on all reciprocal duties and a trade deal with the United Kingdom, among other trade developments.
“But out of all of this interest, we were able to create an amazing pipeline which is wired long-term, because one of our gauging questions was, ‘If the tariffs get back to [what they were previously] would you still work with us?’”
Koobz has decided only to take on business with partners that have a long-term plan for onshoring and budget allocated to the effort.
But scaling up from 60 to 4,000 3D printers—which Graczyk said ring in at about
Kim Glas,
▲ Desma crafts directinjection molding machines used by the footwear industry.
$600 apiece—will require significant capital expenditure. While the price tag on the devices is modest, a tariff will add to it, and the group is looking to scale aggressively in a short period of time.
Koobz looked into 3D printer options made outside of China, and found that the models made stateside as well as in Europe cost more and came equipped with fewer, less-advanced features. “There are other sources than China. In Europe, there’s still a handful companies that can manufacture equipment of that sophistication, at that scale—maybe not as good, maybe a little bit different architecture,” Graczyk said.
But beyond price and performance, the factory owner is also looking to develop a smart and resilient supply chain, starting with machinery. One way to foster this could be to diversify sourcing for machines, but there would be differences between the units and the way they operate, as well as possible differences in quality and output.
“We are fortunate enough that we haven’t pulled the trigger on any anybody yet, but we are at risk of slowing down because we would rather take more time to de-risk this as much as possible; to slow our progress instead of building while still thinking about where to source,” he said. “We know we have to build a system which is very flexible.”
Koobz is having discussions with 3D printer manufacturers about the potential of nearshoring printer production to free-tradeagreement countries like Mexico, and some are already considering doing so.
“Short-term, I’m not super worried about securing our next year’s growth, because the printers that we’re using for the current stage of products are already in the U.S., in distributor warehouses,” he said. “We’ve already purchased some of them, so there’s some frontloading of this equipment. But thinking forward, we need to add multiple colors, multiple materials—those machines are a little bit more sophisticated, and inventory of those doesn’t exist.”
The already-bought machines and those available onshore will float the company through to the last quarter of 2026, he believes. After that, Koobz will “have to start solving the puzzle” of where to source the technology that powers its operations.
“Who are we going with for the next stage of building? Are we keeping the same equipment, the same manufacturer, buying higher-tier machines from them—or are we switching to something else because of the tariffs?”
To Graczyk, there are bigger concerns than the added financial burden caused by the import taxes. It’s the breakdown in the U.S.China trade relationship—and the inkling that it could get worse, not better—that gives him pause about eating the cost of potential duties and sticking with Chinese suppliers.
“We already figured out how to work it out with the previous tariffs, the 145 percent, because [the printers] generate so much margin and profits that we can absorb [the tariff cost],” he said. But he worries about a “complete decoupling” of the world’s two biggest economies.
“We believe our business model supports the investment in this equipment, even with those outrageous tariffs, but the biggest threat is to business continuity; whether our business needs can be met long-term with companies based in China,” he added.
But machines manufactured outside of China, too, will be subject to trade barriers—even those made in nations the U.S. considers allies.
Desma, which crafts direct-injection molding machines used by the footwear industry in Germany, has also felt the impacts of tariff talk.
While goods from the country face only a 10-percent duty rate (for now), the intense swings in the administration’s tariff strategy are not doing anything to propel what was already a sustained and healthy trend toward onshoring, according to regional sales manager Marco Schafer.
“Many people consider options and discuss scenarios, but we have not experienced a rush into investing into manufacturing capabilities, and going at it full-steam,” he said. “And I think people are right to be cautious, because you just saw what happened with China—you went from next to nothing to over 100 percent. Now they’re back to 30 percent, and it’s questionable if that has any effect whatsoever, or if the market will eventually just absorb those costs and not much will change.”
Schafer said footwear firms have been eager to bring some portion of their manufacturing closer to home for at least three or four years, and those that understand the business case for doing so didn’t need tariffs to push them over the finish line.
“It’s not so much the Made in USA label; there are some hard economic figures” that underscore the appetite for reshoring. “You are in the market you’re selling in, so your logistics are shortened. The other thing is capital—if you order container loads of goods from Asia, your capital is tied up for quite a long time, whereas if you manufacture here and you have shorter lead times, your cash flow is actually improved.”
But it’s a decision every company has to make for itself, and much of it has to do with modeling costs versus output. “A simplified view: you realistically have to make at least 500 pairs of the same or similar product in a day, in a oneshift operation, to even be able to consider an investment into automation,” he believes.
Desma’s “bread and butter”—direct injection molding machines—allow footwear manufacturers to produce foam midsoles for performance shoes and sneakers. The
largest, most advanced model can churn out 1,500 to 2,000 pairs per day. All told, it’s a big investment, with machines costing hundreds of thousands of dollars.
Ergo, the footwear manufacturers who are intent on scaling operations using these machines aren’t doing so on a whim.
“All the major projects we’re working on— whether those are already projects we have on order, or projects we hope to have on order soon—they all originated in 2024,” Schafer said. “Those projects don’t happen overnight; the machines and calculations are complex, so you have to really be sure that you believe in your product and in your forecast.”
In short, tariffs are generating interest, but they’re not turning the tides for makers of advanced machinery. Even if an American footwear firm decided today that the unstable trade environment necessitated a sea change in sourcing strategy, they couldn’t fast-track that shift.
“We’re dealing with six-to-seven-month lead times after we after we get an order, but to get the right configuration of the equipment, whether it’s a machine or automation line, you’re easily involved with engineering six to 12 months before a company is ready to place a [purchase order]. These are often two-year projects,” he said. “People know that if they get into this field, it’s a big commitment.”
There are myriad other factors in the equation, from availability of raw materials (many of which are still sourced from Asia or Europe), to staffing (workers must be trained on robotics and electronics), and facilities, which must be equipped to support the machinery and its output.
“All that needs to be put into consideration,” Schafer added. “And therefore, the whole tariff thing—yes, it triggered some discussions, but no active projects as of yet.”
That could change with more clarity about the future of America’s trade relationships. Of the volatility of the past two months, Schafer said, “We hope that the worst is behind us, and that after the loud time comes the time of more quiet negotiations behind closed doors.”
▲ Koobz prints mono-material, single-piece shoes.
SAFEGUARDING
artificial intelligence and automation , for all their promise to make life more efficient for workers, have caused anxiety among Americans about the stability of their current and future jobs. c More than half of working Americans say they feel more worried than they do hopeful about how AI might be used in the workforce, according to February data from Pew Research Center. About one-third of working U.S. adults said they believe AI being leveraged in the workplace will lead to fewer future job opportunities for them; low and middle-income Americans were more likely than their high-income counterparts to worry over fewer job opportunities, Pew’s data shows. c As technology continues to proliferate, experts agree that unions will likely continue to bring up AI and automation, whether when making demands, negotiating contracts or otherwise, in part because of those very fears. c Nicole Brenecki, partner at Jodré Brenecki LLP, said, for many industries, the urgency seems high.
“I think [AI and automation] will become a sticking point sooner rather than later,” Brenecki told Sourcing Journal. “There are already some prior instances where automation is being raised by union workers. This just reflects the general concern within society and the general anxiety that we all have regarding AI and how far it’s going to go.”
Potential demands could include advanced notice for technological integration, advanced notice and compensation for displacement by automation, upskilling requirements and more.
John Weaver, chair of artificial intelligence practice for McLane Middleton, said future proofing is a goal for many unions looking to integrate language about AI and automation into their contracts. When unions negotiate contracts, they typically cover multiple years, with some unions inking five and six-year agreements with their industry counterparts. At the rate technology changes, that can prove an intimidating prospect for groups
trying to protect their members.
“They’re worried about precedents—that [companies] might start to use [AI] a little bit now, and that puts AI’s foot in the door, so that when they negotiate the next [contract], they’re going to have a much stronger position to argue [in favor of] these things,” he said.
Still, he warned, labor representatives should be wary of trying to outright bar companies from using AI and automation in their operations.
Weaver said trying to stymie emerging technology entirely could unintentionally lead to job loss. In effect, if a specific company is barred from using AI or automation for a task their competitor can use it for, the efficiencies could be so significant that it could put the company prohibited from using the technology out of business, he said.
“It’s a fool’s game, because no company, no industry, exists in a vacuum. Even if your company or your industry prevents AI from
More than half of working Americans say they feel more worried than they do hopeful about how AI might be used in the workforce.
JOB SECURITY
being incorporated into it, other companies, other industries are going to do that. That’s going to have an effect on you, despite your best efforts to keep it out,” Weaver said.
Unions like the International Brotherhood of the Teamsters have adopted the approach that they support some technologies that make workers’ lives better or easier, but they strongly oppose technologies that would cause full-on replacement of human workers. The Teamsters have been particularly vocal about issues like autonomous trucking, which is inching toward fully removing humans from the cab of a delivery truck in some states.
Already, a short list of unions have seen success in negotiating AI and automation-related provisions into their most recent contracts.
In one of the more contentious battles, the International Longshoremen’s Association (ILA) took the East Coast ports to task over automation, eventually inking a six-year contract that Harold Daggett,
the union’s president, heralded as offering “full protections against automation” for dockworkers.
The Writer’s Guild of America secured some AI-related provisions in its most recent contract, after a 148-day strike. The contract stipulates that AI cannot “write or rewrite literary material,” allows writers to use AIpowered tools to help them if the company they’re working with agrees to it and more. That contract was considered one of the early wins for unions campaigning on technologyrelated demands.
Meanwhile, the Teamsters in 2023 adopted a contract with UPS, good through 2028, that stipulates that the logistics company cannot implement drone-based technology or driverless vehicles for transporting, delivering or picking up packages. If the company is interested in implementing such technologies, the contract states, it would “be required to notify the [Teamsters’] National Negotiating Committee six months in advance of any such change and shall be required to bargain the effects of any such change.”
While some unions have adopted AI as a worthy issue, not all have done so. Simultaneously, many Americans aren’t involved in unions; according to the Bureau of Labor Statistics, 9.9 percent of Americans were members of a union in 2024. That figure drops to 5.9 percent when considering only private-sector workers.
Despite the fact that AI is likely to impact many knowledge workers’ jobs, Weaver said he doubts many white-collar or non-manual industries will unionize en masse simply because of the threat of AI.
“I would be surprised to see white-collar workers start to unionize, partly because of historical trends, and partly because I think the connotation of unions at this point is that it’s for certain fields,” he explained.
Still, existing unions are likely to have a larger impact on society than their immediate contracts, if AI and automation become central issues.
Brenecki said the unions making noise about AI and automation could capture legislators’ attention, incentivizing them to propose or act on regulations that are meant to protect a broader swath of workers, even those not represented by a union.
“A good union can make a deal with the companies that they’re discussing it with, but that in itself is not going to spark legislation that would be adopted everywhere, within every company,” she said. “The next step here is raising awareness for these issues to an extent that would make the legislature act in a certain way.”
While there has been an absence of AIfocused legislation at the federal level, some states have introduced or implemented various regulations, some of which apply to workers.
In New York, the Fashion Workers Act goes into effect in June, with language that addresses the use of AI to create and use digital likenesses of real-life models. New York, alongside a handful of other states, has also instated legislation aimed at protecting warehouse workers from unrealistic, unsafe
by Meghan Hall
quotas—often tracked by automated systems.
Earlier this year, Kathy Hochul, New York’s governor, proposed an update to the Warehouse Adjustment and Retraining Notification Act (WARN Act) requiring companies to disclose whether layoffs were brought on by AI replacing employees.
Meanwhile, autonomous trucking is a hot legislation topic in other states, primarily California. The Teamsters have thrown their heft behind a bill that would prohibit autonomous vehicles (AVs) without human operators “from delivering commercial goods directly to a residence or business for its use or retail sale.”
There are myriad examples of legislation popping up in a piecemeal way—and earlier this year, experts told Sourcing Journal that trend is likely to continue if Congress doesn’t pass federal legislation. Brenecki noted that, in the absence of specific and sweeping
THERE’S A BIG GAP BETWEEN… WHAT LAY PEOPLE KNOW AND WHAT THE TRUTH OF THE TECHNOLOGY ACTUALLY IS.”
John Weaver, McLane Middleton
H The Teamsters have thrown their heft behind a bill that would prohibit autonomous vehicles (AVs) without human operators “from delivering commercial goods directly to a residence or business for its use or retail sale.”
legislation, unions can feasibly demand almost anything when it comes to AI and automation for that exact reason.
“As long as there’s no actual legislation that needs to be followed, you do have a lot of freedom in how these contracts could be [structured],” she said.
Still, union representatives need to back up the claims they make about emerging technology, said employment attorney Jamie Wright.
She noted that, particularly because emerging technologies are just that— emerging—unions would be wise to collect concrete data and examples of exactly why they make certain demands, rather than based on sweeping assumptions.
“Unions advocate for their workers based on conditions, based on pay and based on other ancillary asks. Absent them being able to say, ‘Here is the data, and here is how our industry is being impacted by AI and this is why we want protection,’ that…could make them lose credibility,” Wright said.
But data and concrete projections aren’t the only help unions might need to get solid technology provisions over the finish line in their contracts.
Weaver said, regardless of the type of technology issue unions negotiate on, involving experts entrenched directly in AI and applicable legislation will become important for such negotiations. That’s particularly because those versed in technology can help those not yet exposed to it at the same level understand the actual versus perceived threats.
“There’s a big gap between…what lay people know and what the truth of the technology actually is, so having experts that are able to say, ‘Your expectations are not realistic,’ or, ‘The other side is playing you, they are taking advantage of your ignorance,’ would be very useful,” Weaver said.
Meghan Hall
HUMAN VS. AI: WHO STYLED IT BETTER?
as ai continues to proliferate , it’s coming closer and closer to the consumer. While I’m a reporter, I’m also a consumer. I know a lot about technology, but sometimes I feel I can hardly work my iPhone, despite being a Gen Zer. c And now that we’re hearing more about AI stylists and the rise of agentic AI—which, for consumers, comes along with the promise that an autonomous agent could, in the near future, shop for you—I wanted to test out a few tools to understand the hype through a different lens.
c That in mind, I decided to work with a personal stylist and compared her work to that of two AI “stylists.” For the test I enlisted ChatGPT and ThredUp’s Style Chat function. I do want to caveat my choice to test ChatGPT; while I know that, today, its primary function isn’t shopping, nor styling, it remains one of the most well-known AI tools on the market, and it’s the system that brought generative AI into the the zeitgeist in a very real way. c Let’s start from the beginning. My friends, Max and Will Grove, who are involved with the Lesbian, Gay, Bisexual & Transgender Community Center of New York, invited me to join them at their table at the organization’s annual fundraising gala. I happily accepted, with just one issue—I had nothing to wear. c I needed one formal look, complete with a bit of playfulness, but still sleek. Rather than doing all the legwork on selecting an outfit by myself, I enlisted some help. That’s where my stylists—AI and human—came in.
A reporter put a personal stylist’s skills to the test against emerging AI tools. by
WORKING WITH A HUMAN STYLIST
When I met Phylicia B. Alexander, the (human!) stylist I worked with for four of the six looks included in this piece, I recall telling her, “I’m just a regular person,” feeling a bit out of place. I had never worked with a personal stylist before. After all, I’m a 25-year-old, New York City-based reporter; personal styling doesn’t exactly fit into my typical monthly budget (much to my chagrin, after this experiment).
Prior to our first meeting, I had shared the link to further details about the event, and we spent our first face-to-face session (via Zoom) discussing what I envisioned myself wearing and the frustrations I’ve previously had with shopping for clothing. At one point, she asked me how I felt about myself, on a scale from one to 10. I answered honestly, and told her that I have, at times, struggled with my self image, particularly because of the proportions of my body. Telling someone I just met about that, in granular detail, felt daunting.
Alexander handled it with grace and deep professionalism.
“I want you to know you’re absolutely not alone on that,” she told me. “I’ve worked with women of all different body types, sizes, all of that—and I honestly hear that exact same thing.”
After our first meeting, Alexander, who specializes in styling for events and occasions, asked me to fill out a questionnaire, which asked about my budget for the look ($250), descriptors of my personal style, preferences around color and more.
The form also asked, “If you get a compliment at your event, what are three to five words you’d want them to use to describe your outfit?” My response was, “sleek, unique, timeless/classy.”
To finish out this part of the process, I needed to submit my measurements: bust, waist, hip and shoulders.
After I filled out that form, I scheduled another call with Alexander, at which point she showed me inspiration photos from several different Pinterest boards; I told her specific details I liked about certain photos she had selected, which helped her better understand my preferences. On that call,
I remember mentioning how much I like mesh sleeves on a dress…More to come on that.
A few days after our call, Alexander shared a lookbook with me via email, comprising about two dozen dresses—some paired with shoes or accessories—and asked me to “like” the looks I could see myself wearing. I chose a few styles on the first go-around, and once I had selected my favorites, Alexander and I scheduled a third call to discuss why I liked certain items or what I might want to see added to the lookbook.
I told her that, while I loved a few of the looks, others felt a little bit older than I might want to wear. She welcomed the feedback and said she would provide a few additional looks for me to check out based on what I’d told her; within hours, she had done just that. We selected the final looks I would order to try on for our in-person fitting, and—importantly— Alexander recommended which sizes I should order in each item to set me up for success, based on the measurements I had previously submitted to her.
She and I texted a fair bit throughout this process, and communicating with her felt like asking a (very skilled) friend for their opinions on what to wear.
We settled on ordering four dresses. At the outset of our work together, Alexander reminded me that she might pull some looks I wouldn’t necessarily choose for myself, but that I should embrace that and try a few new things. The brown dress and the sparkly green dress in the below photos are indicative of the fact that I took her advice—when Alexander originally asked me to describe my existing style, I used words like “expressive, easy going, casual and classic.”
The brown dress, in particular, is something I would never pull for myself because of fit concerns in the bust. But Alexander knew it’d be flattering. In the end, I almost chose to wear it (the reason I didn’t was because I knew I’d be unlikely to have a reason to wear it repeatedly).
The sparkly green dress was the only dress that didn’t fit at all; it was about two sizes too big, and we had to pin back handfuls of the material for the photos. In the end, I think it would have been too difficult to alter, and even after it was pinned, I didn’t love the shape against my silhouette.
Ultimately, Alexander agreed with that sentiment, so we decided to focus on the other three looks—from the beginning, she told me that ordering multiple looks would safeguard me from any fit issues and from any apprehension about wearing a look I didn’t love.
These kinds of discussions added a personal touch that I simply didn’t find in my experience working with AI-based stylists; Alexander came to my office for the final tryon and styling experience, where the photos you see throughout this story were taken. She brought with her a variety of extra jewelry and accessories, which made the experience feel even more exciting.
THREDUP STYLE CHAT
ThredUp has a tool it calls Style Chat, which allows consumers—in this case, me—to enter a natural-language prompt about an upcoming event or style need. From there, ThredUp’s AI tool offers up categorized suggestions on what to wear, as well as an AI-generated preview of what such an outfit would look like on a person.
I told Style Chat I needed an outfit for a New York City-based gala in April, and that I wanted the final look to be sleek and youthful—the same adjectives I had used with Alexander when I filled out the questionnaire.
G Phylicia B. Alexander and Meghan Hall at a try-on session.
GEORGE CHINSEE (4)
Based on my prompt, ThredUp recommended I wear an emerald-green dress, tan patent leather heels, a black hat, a silver belt and a silver clutch.
I didn’t love the resulting outfit—mostly, the difference in colors felt stark, particularly in comparison to Alexander’s color-matching skills. Tan, with black, with green, with silver made the look feel a little disjointed. It felt clunky, rather than sleek, like I had requested.
I asked three other stylists to rank the looks (without knowing who or what styled them), one through six (one being their favorite, six being their least favorite). Each of them ranked this look fourth or fifth.
Nikki Venus, a business style coach, shared my sentiment about too much mixing and matching.
“While the dress has potential, the styling choices made it feel disconnected. The oversized floppy hat, which leans more vacation-chic, didn’t complement the formal vibe of the gown—it felt out of place,” she said. “On top of that, the metallic belt didn’t do the silhouette any favors; instead of elevating the look, it created a distraction. With more intentional accessories and a cohesive style direction, this dress could really shine.”
Though the mismatched nature of this look wasn’t a favorite, what I’d say about this tool is that it’s a helpful starting point and one of the more accessible tools available to the average, mass-market consumer. That’s because it provides multiple options for each individual item it recommends (and only in sizes that will fit, if you provide details about your preferred sizes, which I did).
As compared with ChatGPT, which provided lots of broken or misdirected links (and didn’t do any favors with trying to help out with sizing), the way the options Style Chat gave were set up felt helpful—even if the options themselves didn’t suit my own style. In stacking ThredUp’s tool against Alexander’s expertise, the most noticeable gaps were the fact that Style Chat had no sense of my preferences, while Alexander did because of the time she spent gathering information from me. She also had a discerning eye for the way colors should pair with one another, which, in this case, the AI stylist lacked.
CHATGPT
Working with ChatGPT was, candidly, one of the more frustrating pieces of working on this story (other than using Amazon’s Rufus, but I’ll get to that later).
I gave it the following prompt: “Act as if you are my stylist. I am attending a formal gala in New York City on April 10 and need a look that revolves around a long dress. I want it to be sleek, youthful and fun. My waist is much smaller than my hips, and I have a large bust, so I don’t like wearing strapless very much. I want to buy only items I can return.”
My main problem with ChatGPT was that, when it offered up a specific style name, it couldn’t source a direct link to that item for me. And, in many cases, the brand no longer sold the style it had recommended. That led to a lot of Google searching, trying to find the closest match to what it had suggested.
You can see here that, when I asked my “stylist” to share the sources for the dresses it had recommended, it struggled to point me to a single product page, instead sending me to sites’ general dress landings or admitting it couldn’t share where its suggestion came from.
The Abercrombie & Fitch dress ChatGPT selected was available, and in response to
my original query, the system recommended I wear the black or green iteration of the dress. I couldn’t find the dress in black, so I ordered it in green.
Though I have had some success using ChatGPT for other, non-fashion-related queries, its efforts in styling me for this event fell flat among the three stylists who ranked the final looks. They each ranked this look as their least favorite.
While I think ChatGPT’s final look (which included gold jewelry and gold shoes) felt more cohesive than ThredUp’s, it didn’t fit my body well—and also felt a bit too informal for the occasion. As style curator Aleisha Bradley put it, “it’s a trendy, go-to dress that doesn’t represent her style and personality. It’s not flattering on her body and lacks luster.”
One thing I did appreciate about ChatGPT was that—like Alexander—it recommended specific hairstyles I could’ve worn with the pieces I selected.
One other note, I completed this experiment before OpenAI upgraded ChatGPT with a more robust shopping function, so my results may differ from how the technology would fare in this trial today.
OTHER EXPERIMENTS
In the process of finding an outfit that would work well for the gala, I tried several other AIpowered tools, with varying degrees of success. Alta, which just inked a partnership with the Council of Fashion Designers of America (CFDA), felt like a strong contender for an AIbased personal stylist. The outfits it suggested to me felt cohesive, even if, at times, not quite my style. The trouble I ran into was that the tool had a difficult time respecting my budget. Amazon Rufus, meanwhile, was extremely difficult to work with in any cohesive way. It suggested items that weren’t in stock, had a hard time adhering to budgetary requirements and suggested dresses that were less “sleek” and more “mother of the bride.”
I felt I got more out of the “styling ideas” tab on individual Amazon product pages than from Rufus itself.
I would note that this experiment took place prior to the release of Amazon’s Nova Act, which supposedly has more agentic capabilities; Rufus is a chatbot meant to field natural-language questions. It’s certainly better suited to handle a question like, “What should I purchase for a Super Bowl party?” than it is to style someone for an important event.
CONCLUSIONS
I think it’s clear that Alexander’s expertise far supersedes that of the current AI stylists I worked with. Each time she and I ran into an issue (out-of-stock undergarments, pieces I wasn’t fond of, a dress that didn’t fit correctly), she had a solution or suggestion. She had a keen understanding of the colors that would suit me best, and had a vision for how certain looks would compliment my figure. A perfect example of that: I thought the dress ChatGPT selected would look great, but it ultimately wasn’t a match for my body type; meanwhile, I thought the brown dress Alexander selected might be a tough look for me, but it ended up fitting my silhouette quite nicely. In addition to that, Alexander sourced items from several sites or brands I’d never heard of (and thus would have been less likely to come across on my own). The dress I ultimately chose came from a brand I’d never shopped
AS MUCH AS I THOUGHT AI STYLING COULD BE A “QUICK FIX,” NEITHER SYSTEM SELECTED A LOOK THAT WAS VIABLE OR APPROPRIATE FOR THE FORMALITY LEVEL OF THE EVENT I ATTENDED.” Meghan Hall
with before. ChatGPT, meanwhile, selected items from mass-market brands I’ve previously shopped at or viewed—it felt like I could have come to a similar conclusion myself with less frustration involved.
As much as I thought AI styling could be a “quick fix,” neither system selected a look that was viable or appropriate for the formality level of the event I attended. And, unlike working with a human, asking AI to alter its preliminary results yielded seemingly random suggestions, not tailored to my own style.
What I’d also note is that the process of working with Alexander positively influenced other pieces of this experiment; I had never properly measured myself, so while she used those measurements to help me select sizes for the pieces she recommended, I also used those measurements to ensure I picked the best size for the garments the AI systems recommended. Similarly, Alexander asking about my goals for the look helped me better define what I sought when working with the AI models.
WHAT I WORE
In the days following my final styling session with Alexander, it came time to make the final choice, which proved a difficult decision because I loved three of the four looks she had styled. Alexander checked in with me to ask what I’d selected and offer up any last-minute accessory ideas I might need.
I ultimately selected the look I felt most confident in—a look I knew I’d wear more than once: the black, mesh-sleeve dress.
Coincidentally, that dress was also the favorite among the stylists who ranked the looks; it came in first with all three. Tami Harrigan, a style coach who helped rank the looks, explained why the black dress was her favorite—and I agree.
“The dress fits her body perfectly, it’s flattering, and she looks comfortable and confident in it,” Harrigan said. “The style of the dress is classic, while incorporating modern/ trending elements with the sheer over the arms. It also compliments her body and style.”
E Styled by ThredUp Style Chat.
E Styled by Phylicia B. Alexander.
HOW THE AI REVOLUTION IS TRANSFORMING TRANSPORTATION
AAS ARTIFICIAL INTELLIGENCE (AI) continues to reshape the fashion industry, Penske Logistics is leveraging cutting-edge technology to help its clients enhance supply chain run smoother—all while reducing shipping costs. The Reading, Pennsylvania-based company’s new platform Catalyst AI™ is leading the charge, supporting more datadriven decision-making and performance tracking.
Here, Ann Walsh, senior vice president of digital and customer data at Penske Logistics, discusses how the transportation company uses AI to build resilient supply chains, enhance efficiency and transform fleet operations.
SOURCING JOURNAL: How is AI changing the way fleet operators approach management and asset utilization in the supply chain?
Ann Walsh: With the rise of AI and advanced machine learning algorithms, it’s now possible to analyze vast amounts of data to uncover new efficiencies and opportunities for improvement. This technology is transforming how fleet managers and operators make decisions and run their businesses, revolutionizing the way we view transportation. The ability to integrate data across an entire ecosystem is incredibly powerful. Gaining visibility into operations allows you to pinpoint areas ripe for improvement and quickly identify outliers that need attention— insights that are truly invaluable. Whether it’s optimizing routes by factoring in traffic, weather and delivery windows or reducing fuel consumption and improving delivery times, the possibilities are endless.
“RELYING ON GUT INSTINCT IS NO LONGER ENOUGH—DATA-DRIVEN DECISION-MAKING IS ESSENTIAL FOR SUCCESS IN MODERN FLEET AND SUPPLY CHAIN OPERATIONS.”
ANN WALSH senior vice president of digital and customer data, Penske Logistics
How do you see AI shaping the future of fleet optimization?
A.W.: Integrating AI technology into daily fleet operations brings a wide range of benefits—and as these systems continue to evolve, their precision only improves. One area where AI is already making a major impact is proactive maintenance. At Penske, we leverage AI to analyze vast amounts of data, including engine performance and fault codes, to predict when specific components might fail based on vehicle type. This allows us to schedule repairs before issues arise, helping prevent breakdowns. In fact, our teams complete 90,000 proactive repairs each year, significantly reducing unplanned downtime. We’re especially excited about the future of our Catalyst AI™ decision engine. It provides fleet and transportation managers with
insights into key performance factors like fuel efficiency, vehicle utilization and maintenance trends. In today’s competitive landscape, relying on gut instinct is no longer enough—data-driven decision-making is essential for success in modern fleet and supply chain operations.
You just mentioned Catalyst AI™, Penske’s new tech offering. How does this tool help fleet managers position themselves for the future while improving resiliency and operational performance across the supply chain?
A.W.: Catalyst AI™ answers the critical question: “How am I performing?”
In the past, fleet performance was measured using static forecasts and generalized metrics, with limited ability to create benchmarks tailored to the unique
characteristics of your fleet. As a result, finding a clear answer to that question was often difficult, manual and sometimes impossible.
Now, with AI and Penske’s robust data set, Catalyst AI™ offers an industry-first platform that automates the benchmarking process and delivers actionable insights. It processes over 100 billion data points annually, with new data added daily. Thousands of variables are synthesized into digestible key performance indicators (KPIs) and diagnostic metrics that help customers pinpoint the root causes of performance trends. More than 300 models run simultaneously to surface insights and recommend meaningful actions that drive measurable improvement. Take fuel management as a practical example. Catalyst AI™ can track fuel consumption, identify patterns or anomalies and highlight inefficiencies or potential issues—enabling smarter decisions that lead to cost savings. With its recent launch, Catalyst AI™ now empowers customers with performance comparisons to similar and best-in-class fleets, along with prioritized, datadriven recommendations for improvement. Whether it’s fuel efficiency or vehicle utilization, Catalyst AI™ helps customers focus their efforts where they’ll have the greatest impact. ■
GAIN AN EDGE GAIN GROUND
Penske’s industry-first Catalyst AITM uses advanced algorithms and billions of data points to compare your fleet to similar fleets. This provides your business with unique benchmarks across key metrics, like fuel efficiency, fleet utilization and maintenance, giving you a personalized game plan for improvement.
In an era of cost-cutting and saving, industry creatives fear the peril of AI wannabes. by Meghan Hall
DIGITAL
DISPLACEMENT
Elle Dawson
digital doubles of real-life models have come onto the scene— and though they’re a newer example of the myriad ways generative AI systems can be leveraged for e-commerce and retail, they’re already making a splash. c H&M conjured mixed feelings from the industry when it announced earlier this year that it had created digital twins of real-life models to be used for product imagery.
c The Swedish fast-fashion company isn’t the first to leverage that type of technology; WHP Global’s Anne Klein has done the same for more than a year. Still, now that a mass-market household name has publicly let on that it plans to use tech in this way, it has caused a stir among consumers and industry professionals alike.
But what happens to the creatives who have long been part of capturing brand images when those same companies begin using AI for photoshoots? According to those behind the scenes of today’s brand photography, jobs dwindling remains a major fear.
Sara Ziff, founder and executive director of Model Alliance, an advocacy group focused on protecting models’ rights, said that for those working a variety of jobs related to fashion and apparel campaigns and imagery, the prospect of replacement looms large.
“There’s a concern about job replacement, not only for models, but also for photographers, stylists, hair and makeup artists, among others,” she said.
Lindsay Kastuk, a makeup artist who works on brand and editorial-style shoots, said the rise of digitally generated product and campaign photos worries her.
“A lot of us have poured so much into this specific industry, and [if we] see it all disappear over saving some money, it would be truly heartbreaking,” Kastuk said.
Kastuk said that, while she has not, in her work, encountered a brand planning to replace creative workers with AI, she feels it could be on the way. To the extent she is financially able, she would avoid working with brands leveraging AI to replace creative teams.
“It’s really difficult for me to stand by and support a company that’s, for a lack of a better word, screwing over colleagues,” Kastuk said.
“It’s already a really tough industry postpandemic, and the economy right now is not great—things have slowed down a bit for a lot of people. [AI] is just another hit—you’re like,
‘I just want to do my job. I just want to enjoy it, and I just want to make sure I can keep a roof over my head.’”
Kastuk has not yet put language around AI into her standing contracts, since so much of what happens on a set is out of her control once the model gets in front of the camera, she said. She doesn’t own the images that come out of the shoot, even though her work is featured within them; if those images are fed to AI models generating digital doubles, those systems could learn from hair and makeup artists’ efforts.
Still, Kastuk hopes that the unique looks that she and her colleagues can provide continue to capture brands’ attention as they consider how to proceed with product imagery. Kastuk said that, if jobs become fewer and further between because of the rise of AI, she would have to pivot into other areas of the makeup industry, like wedding looks, which vary significantly from the editorialized style she’s used to.
The level of concern over job stability in certain sectors of the creative industry seems to differ a bit.
Chelsea Loren, who owns her own freelance photography business focused on brand lifestyle shoots, said that, while she has heard some concern floating about in the industry, she remains confident that her skills will continue to be necessary in the coming months and years, even if some brands begin using AI for product photos.
That, she contends, is because, if a wide swath of brands use AI for their product images, they’ll begin to be indistinguishable from one another.
“So many brands are going to veer into using AI, and then everything’s going to start looking the same,” Loren said. “I think people are going to want to have that human touch to it.”
Creating digitally generated product imagery can often save brands time and money,
particularly if they would have otherwise shot the photos in a destination location. Loren said that, if brands choose to use AI to create product images or campaigns, she knows it could alter their sense of what budget for human-led shoots look like in the future, which concerns her more than fullon job loss.
To help safeguard her work, Loren said she has updated her standard contract to include specific provisions related to AI. Her newer contracts include language that prevents companies from using her work to train AI models and from using AI to alter or create derivatives of her images without written consent.
Both Loren and Elle Dawson, a professional model with 18 years of experience, said part of the issue with AI-generated images is that the products in the images are also digitally generated, which could mean that the way the item fits or how the material looks could be far different from actuality.
“At the end of the day, especially because I’m working with products and consumers and businesses, you don’t want to give [consumers] a false representation of what the product is,” Loren said.
Dawson said she believes that, if this trend continues, consumers are likely to find that products don’t fit the way they’re advertised. If that’s the case, it could see brands fielding higher return rates, in turn damaging profit margins or dashing progress toward sustainability goals.
“I think [brands] are using more AI models because they can make [garments] fit on every single body, versus whenever you put a product on me, you have to fake it in different ways,” she said.
Diversity has also been a core issue surrounding AI-generated product content; in 2023, Levi’s said it would partner with Lalaland to create digitally generated models— albeit ones not directly based on real-life models’ likenesses—to introduce more diversity into campaigns and product photos. The denim brand was quickly met with immense backlash, in part because of that diversity push.
Ziff said she feels it’s a further extension of the fashion and apparel industry’s lack of representation to generate digital imagery of diverse models.
“In an industry that’s historically been discriminatory, creating digital representations of models of different races, ethnicities and so on, rather than hiring and paying a diversity of real models, is concerning,” she told Sourcing Journal.
Dawson agreed with Ziff and said the modeling industry is already exploitative, which concerns her because she believes AI could be yet another way models—particularly women—face manipulation.
“The things that models think are OK to deal with are so extreme. Whenever it comes to such a big problem like [AI]...the only way I can trust that the industry won’t go that direction is by being the person who doesn’t let it,” Dawson said. “There has never been a more important time for platforms to be given to the models who actively use their voice to call things out.”
It remains unclear how much models who have licensed their likeness earn from AIgenerated photoshoots, and how that rate compares to what those same models would make if they were on a physical set.
Dawson noted that she would only license her digital likeness if a brand offered her “millions” to do so—alongside the ability to speak out on how she’d use that money for good.
“If you’re going to change the world for the negative, you need to make sure you’re getting the resources and planning to do something that makes up for every job you just got rid of,” she said.
As of yet, though, Dawson has not consented to a digitally generated version of her likeness to be used. She believes her physical presence is far more valuable than a digital double of her body, and she said she doesn’t want to further perpetuate impossible beauty standards by allowing a non-real version of herself to be featured in photos promoting a brand or product.
“The one thing I can guarantee to people is that, when you see an image of me, it is me. I was there. I put in the work. It’s my energy— that can’t be replicated,” she said.
Dawson said she isn’t concerned about being replaced—but only because she has so much experience and a reputation that precedes her in the industry. Other models, who are newer, lack that same assurance.
THERE’S A CONCERN ABOUT JOB REPLACEMENT, NOT ONLY FOR MODELS, BUT ALSO FOR PHOTOGRAPHERS, STYLISTS, HAIR AND MAKEUP ARTISTS, AMONG OTHERS.”
“I’m lucky that I get hired based off of my reputation…but I’ve already seen it affect a lot of models, saying their regular jobs are gone,” Dawson told Sourcing Journal.
⊳
▼ An example of an AI-generated
Dawson advocates for models on a slew of issues, sometimes in partnership with Ziff and Model Alliance, in an effort to ensure fair working agreements and safe conditions. Model Alliance worked on the New York Fashion Workers Act, which has been passed by the state’s legislature and signed into law by Governor Kathy Hochul. The law, which requires greater transparency around models’ contracts and rights, has provisions in it about the requirements for using models’ digital likenesses.
“Beginning on June 19, the [New York] Fashion Workers Act is going to require that modeling agencies and brands that want to create or use a model’s digital replica obtain the model’s clear, written consent, and [that] modeling agencies will no longer be allowed to hold power of attorney over a model’s digital replica, which gives the model more control over how their image is used in connection with generative AI,” Ziff said.
The Fashion Workers Act is a first-ofits-kind piece of legislation, and while Model Alliance hopes to see other states and countries begin to adopt similar tenets, Ziff said she knows it doesn’t even begin to address another important issue: compensation.
She hopes that, as other states and jurisdictions begin to evaluate workers’ rights as they relate to AI, models and other impacted groups have a seat at the table for the discussions. To her, that perspective supersedes any well-intentioned efforts by legislators and companies to speak for models.
“We have tried to take the approach that we’re not anti tech, we’re anti exploitation. We hope that generative AI could be used in a way that is augmenting rather than replacing jobs, but that’s predicated on workers having leverage to negotiate how it’s used, with meaningful protections in place,” Ziff said. “The workers really need to shape the direction of the industry’s adoption of AI before that path is decided for them.”
▲ Sara Ziff, founder and executive director of Model Alliance, speaks about the New York Fashion Workers Act with supporters.
Makeup by Lindsay Kastuk.
model.
Sarah Ziff, Model Alliance
DATA RUSH
While information on the consumer has been plentiful, new tech has taken analytics to another level. by Meghan Hall
customer loyalty has long been a metric brands and retailers chase, but artificial intelligence has redefined what’s possible with personalization. Brands have sought out additional information about their customers’ tendencies, interests and demographics. That’s likely because, in order to power the type of AI systems companies use to personalize experiences, they need a trove of data on their consumers.
As AI has continued to advance, it has also started to influence a new path for legacy technologies and programs. That includes loyalty programs, which provide an influx of information about a brand’s core customers— often at a low cost to the brand or retailer,
when considering the return on investment.
Companies have started using their loyalty programs to ingest data about their consumers while offering them some benefit, Quynh Mai, founder and CEO of Qulture, a digital-first creative agency, said.
“To create meaningful customer experiences with AI, [brands] need user data and are finding ways to mine customer behavior and preferences using discounts and early access from their loyalty programs,” she explained. “Long term, the data will be more important than the discount given, because this knowledge will help them convert new customers and drive business growth.”
Mai’s instincts are backed up by data.
Information from consultancy McKinsey &
Co. shows that loyal consumers are 64 percent more likely to purchase more often and 31 percent more willing to pay a premium for a product sold by a brand they’re loyal to.
And data from Yotpo, a loyalty provider for e-commerce, shows that eight in 10 consumers will make a repeat purchase when incentivized by a loyalty program.
With that in mind, experts recommend personalizing shoppers’ experiences as much as possible to provide stronger brand affinity. When companies ingest consumer data through loyalty programs, they can use AI-powered systems to offer hyperspecific discounts and incentives to customers based on their purchase history and behavior.
Kelly Pedersen, partner, global retail for
PwC, said personalization only continues to get better; as AI continues to develop, and companies move past testing periods into full-on implementation, their customers are likely to see the benefit.
To power those experiences, though, brands and retailers need quality data. Before retailers and brands began implementing systems like these at scale, Pedersen said they were often overwhelmed with information— meaning that they had strong insights about their consumers, but no real way to turn that data into action. But now, he said, they want all they can get.
“In general, retailers are going to want as much [data] as possible,” Pedersen said. “I have yet to see a case where they say, ‘That’s too much.’”
Part of the reason brands and retailers are now able to ingest data en masse is because AI systems can analyze and procure results on individual consumers or buckets of consumers based on the inputs.
“[Companies] can use customer data so much more efficiently than they could in the past, and really drive more personalized experiences than they could in the past,” he said. “The opportunity is there for them
to capture that [consumer] and make them sticky with a brand.”
He cautioned that brands—particularly those closer to the start of their personalization journey—should only collect data they can handle securely and carefully.
SAP Emarsys data shows that three in 10 consumers noted that they would be put off by companies using data irresponsibly.
In the future, Pedersen said he foresees data collected by loyalty programs being used to influence other portions of the business. Today, AI capabilities exist in silos at many companies—that is to say, a retailer might have one system for loyalty, one system for demand forecasting and another for pricing. But in the future, Pedersen expects those capabilities to be interlinked, which means that brands and retailers will be able to better build business expectations around the behaviors and desires of their most loyal customers.
“The connection between those [functions] and how they interact, that’s the next frontier that we’re definitely talking to our clients about right now,” he said, noting that the largest retailers could be deploying systems like that within months, in particular because of the rise of agentic AI.
WILL CONSUMERS GO FOR THE DATA MINING?
While consumers can sometimes prove wary to share their information with brands and retailers, Mai and Pedersen project that the uncertain economic environment playing out in the U.S. today could benefit retailers and brands when it comes to collecting data.
Part of that, Mai said, is attributable to the fact that consumers equate loyalty programs with discounts and value, two important considerations with inflation fears and poor sentiment plaguing many U.S. consumers.
According to SAP Emarsys data, six in 10 consumers said they switch brands because of cost—a consideration that is likely on the rise with a gun shy consumer.
“With inflation coupled with lower inventory, customers have warmed to loyalty programs that offer discounts and early access to the most coveted looks,” Mai said.
“In return, tech-savvy brands are gathering important customer data that will help them scale and refine their AI customer service and shopping agents for the near future.”
SAP Emarsys data shows that an increased number of consumers have, indeed, shown greater interest in loyalty programs; in 2024, loyalty program usage increased by 28 percent. Fashion and apparel have the potential to reap the benefits of that in a big way. Fifty-four percent of consumers indicated they have loyalty to a specific fashion or apparel brand, higher than any other industry included in the survey.
Mai said when considering who to recruit for loyalty programs, brands and retailers should turn to the customers already driving high revenue for them.
“There is this known fact in retail that 20 percent of your customer base makes up 80 percent of your sales. That said, brands have found better success creating loyalty programs for that shopper now that the price of acquiring new customers is sky high,” she said. “Discounts, access to early arrivals and sneak peeks at brand launches have enticed younger consumers who are price conscious
IN GENERAL, RETAILERS ARE GOING TO WANT AS MUCH [DATA] AS POSSIBLE. I HAVE YET TO SEE A CASE WHERE THEY SAY, ‘THAT’S TOO MUCH.’” Kelly Pedersen, PwC
and want brag-worthy experiences.”
Data from Bain & Company shows that by increasing customer retention by 5 percent, retailers and brands have the capability to unlock at least 25 percent more revenue annually.
Pedersen said even the most loyal consumers have to remain value conscious right now. He expects that, if a loyalty program offers the right benefits, consumers won’t hesitate to share additional information about themselves and their purchasing habits—and proving the value of a loyalty program could be easier among customers that already have an affinity for the brand or retailer in question.
“Consumers start to forget about the risks of sharing data when they get something they want,” Pedersen said. “If it’s used in an effective way where consumers are happy with their experience, they’re going to be more willing to share that data.”
UNLOCKING OPERATIONAL EFFICIENCIES WITH INDUSTRY 4.0
TTO NAVIGATE volatile markets, tariffs, uncertain economies and shifting consumer habits, apparel brands and manufacturers must shift from reactive to predictive strategies to manage potential risks related to costs, sourcing and delivery timelines. Especially as sourcing strategies expand to involve more facilities, key focus areas include real-time visibility across global operations, cost modeling and optimization, and enhancing collaboration between manufacturing locations. Here, Leonard Marano, president, Americas at Lectra, explains the crucial role of technology amid today’s uncertainties.
SOURCING JOURNAL: How is technology reshaping the fashion industry’s sourcing strategies, and what advancements has Lectra introduced?
Leonard Marano: At Lectra, we leverage Industry 4.0 principles to meet the unique needs of each fashion business. Industry 4.0 represents a transformative leap in manufacturing, driven by the integration of advanced technologies such as cloud applications, IoT, AI, and big data. The benefits are substantial: faster production speeds, reduced costs, enhanced product quality and a smaller environmental footprint. Cloud-based solutions such as Valia Fashion, Lectra’s Industry 4.0 intelligent cloud-based solution, provide a centralized database of materials, orders and production resources, connecting the dots from product development to order to cutting room. This enables complete control and real-time visibility over processes, enhances collaboration across teams and locations, and facilitates quick decision-making related to production, deadlines and costs. Additionally, employing pricing strategies through tools like Retviews offers brands competitive pricing intelligence to help position themselves in the marketplace.
How does Lectra combine automation with AI to unlock supply chain efficiencies?
L.M.: Valia Fashion automates complex tasks while optimizing fabric usage and minimizing downtime. AI-driven platforms like Valia deliver real-time insights to enhance production planning accuracy, enabling customers to reduce lead times, lower costs, and reallocate labor to highervalue tasks.
On the factory floor, Lectra’s cutting-room solutions are equipped with smart sensors and connected services that support predictive maintenance. By analyzing machine data and usage patterns, we help manufacturers anticipate failures before they occur, avoid costly interruptions, and maintain peak operational efficiency. Lectra’s expert teams ensure our customers benefit from optimal machine performance, uptime and responsiveness across their operations.
How can brands and retailers navigate tariff uncertainties, create competitive value propositions, and transform disruption into opportunity?
L.M.: Lectra’s end-to-end personalization empowers brands to respond quickly and strategically. By enabling localized, on-demand production through 2D/3D design, advanced patternmaking, AI-powered planning, and automated cutting, our solutions reduce dependency on long, cost-sensitive global supply chains.
Tools such as Modaris and Gerber Accumark streamline patternmaking and virtual prototyping capabilities for faster timelines (with greater speed and accuracy). Valia Fashion connects planning to cutting operations, optimizing fabric usage, improving decisionmaking, and ensuring clear visibility on performance and cost control.
Lectra also builds regional flexibility into operations. Brands can swiftly adapt to sourcing constraints, adjust product availability by market, and safeguard margins against tariff-related cost pressures. Tools like Retviews and Neteven provide actionable insights to refine pricing, assortment and distribution strategies.
“REAL-TIME ANALYTICS FURTHER SUPPORT SMARTER
How is Lectra supporting sustainable practices across the supply chain?
L.M.: Sustainability is a core aspect of Lectra’s technology portfolio, from concept design to manufacturing. Valia Fashion enables brands to purchase only the fabric needed, reducing waste and cutting costs. Real-time analytics further support smarter decision-making, helping track key sustainability indicators like material usage and cutting waste.
In the cutting room, Lectra’s Vector solutions ensure precise fabric yield optimization while reducing idle machine time and energy consumption. Features like energy-efficient turbines contribute to up to 40 percent energy savings.
Lectra promotes transparency with TextileGenesis, which traces the origin of raw materials across supply chains and helps brands meet sustainability regulations and consumer expectations around ethical sourcing. Retviews provides realtime competitor benchmarking for responsible pricing and sourcing strategies, while Kubix Link connects teams via a cloudbased platform, streamlining workflows to minimize errors and resource usage.
By integrating these tools, Lectra’s solutions empower businesses to balance profitability with environmental stewardship at every stage of the value chain, enabling fashion brands to align sustainability with operational performance. ■
LEONARD MARANO president, Americas, Lectra
THE CONNECTING THREAD, FROM CONCEPT TO STORE.
Weave success into the fabric of your business, with solutions that support every phase of the garment lifecycle.
Create better, manufacture better and market better, empowered by real-time insights, seamless collaboration and complete traceability. An interconnected approach ensures fashion data fl ows freely through every stage of the process, supported by Industry 4.0 technologies, notably artifi cial intelligence. Making your path to growth, profi tability and sustainability clear.
We light the way, so you can build your success story.
WOMEN TO
artificial intelligence has myriad use cases, particularly in industries as complex as fashion, apparel and retail, where many processes have remained manual despite technology’s proliferation. c Startups have long partnered with brands and retailers to improve the efficiency of operations, delight consumers and solve core problems—and emerging technologies have only accelerated the pace of possibility. c Female founders, though a significant part of the startup ecosystem, face a large disparity when it comes to fundraising; in 2024, companies co-founded by men and women grabbed 20.9 percent of all VC dollars, but companies started by women-only founders saw just 2.1 percent of all VC money, according to PitchBook data. c Still, the fashion industry relies on women as consumers; data from the U.S. Bureau of Labor Statistics shows that, in 2023, the average U.S. household spent $655 on women’s clothes and $406 on men’s clothes. c Despite the industry-agnostic funding gap, female founders continue to build meaningful technologies that move the needle within the fashion, apparel and retail industries—especially as AI continues to proliferate. c Sourcing Journal identified five companies, with six female founders, to watch in the latter half of 2025; their goals run the gamut—from sourcing, to consumer styling, to sortation for recycling, to resale and product design.
Female founders continue to build meaningful technologies that move the needle within the fashion, apparel and retail industries
—especially as AI continues to proliferate. by Meghan
SARIKA BAJAJ
CEO and co-founder, Refiberd
TUSHITA
GUPTA
chief technology officer and co-founder, Refiberd
Stage: Seed
Funding total: $3.7 million
Sarika Bajaj and Tushita Gupta founded Refiberd with the goal of making sortation easier at the outset of the textile recycling process. The company uses hyperspectral cameras to detect the materials in a product.
In recent months, Bajaj and Gupta have expanded their remit, and have updated the company’s main goal: to enable material accuracy across the supply chain to aid circularity efforts throughout the industry.
“We think our technology is critical for textile sortation, but it’s also important for high-value authentication for resale. It’s also useful for brand product development and design, for circularity and even for customs checks—all of these different moments where there is a need for fast, non-destructive, accurate testing,” Bajaj told Sourcing Journal, noting that many of these processes currently use chemical testing, which takes longer and can damage products or samples.
Bajaj and Gupta are both technical founders of Refiberd, but Bajaj has shifted toward the business side of the house. Both women said one of the most difficult parts of starting a company as female founders has been facing doubt from investors who believe Refiberd has set out to solve a problem that requires only simple technology.
“That’s really the issue with a lot of women being in a technical field—there are just assumptions about intelligence or capabilities,” Gupta said. “That’s something we actively have to deal with every day…so we try to make sure that we’re not building an internal culture that is based on assumptions.”
And Bajaj and Gupta have worked hard to ensure they can secure adequate funding and meaningful partnership for their burgeoning business; Refiberd has partnered on a pilot with several companies, including Ganni, and recently won eBay’s Circular Fashion Innovator of the Year Award, which came along with a $300,000 investment from the marketplace’s venture capital arm.
Bajaj said, throughout the remainder of 2025, she and Gupta will continue to test and push the boundaries of the technology they have built.
“We’re at the point where a lot of pilots have completed successfully, and we’re able to feel good about…our technology, so 2025 is about in-person deployments, it’s about conversions and also about exploring the new
market opportunities we [have],” Bajaj said. “The way people are approaching working with us is so much more sophisticated than it was last year, in previous years…so you can see a much greater forward momentum; pilots are well designed.”
KATHLEEN CHAN
CEO and founder, Calico
Stage: Seed
Funding total: $5 million
Kathleen Chan owned a clothing brand before she started Calico, which leverages AI to shorten lead times and ease the burden of sourcing and manufacturing. She knew based on prior experiences that brands and retailers continue to face an ageold problem: long lead times amidst a rapidly changing trend cycle. She decided the industry could stand to have a different standard: weeks, instead of months.
“Production can take anywhere between six and eight months. It’s not unusual to hear people planning for 10 months out…and that is because the current way of working is incredibly manual, and it takes a long time,” she said. “We’re able to take that six-to-eightmonth [timeline] and compress that down to about six to eight weeks.”
The company accomplishes that by handling several main tasks for brands and retailers: creation of tech packs and patterns, matchmaking brands’ orders with capable, verified suppliers in Calico’s network and seeing those orders through until they’re ready to be transported to the client.
Calico currently has about 150 manufacturing partners, primarily in regions outside of China, including Vietnam, Turkey and Peru, among other Southeast Asian and Latin American countries. Throughout the rest of the year, Chan hopes to scale Calico’s manufacturing partner list to 250.
“We’ll continue to invest outside of the regions that we are currently in and double down on the regions that we [already] are,” Chan told Sourcing Journal, noting that tariff
Hall
H Tushita Gupta and Sarika Bajaj
G Kathleen Chan
strategies and the news cycle will influence the decisions the company makes about the best locations to further expand into.
The Calico team vets every supplier it brings into its network for compliance and quality, and suppliers are selected based on their specialties and proximity to raw materials, among other considerations.
As the company continues to recruit brand and retail customers, another 2025 goal for Calico is to constantly iterate on the technology side of its business.
And as for her experience as a female founder, Chan, like others, said the highest hurdle has been fundraising.
“We all have that story of the male VC that didn’t really see what you’re looking at, or diminishes what you’re doing,” Chan said.
But the doubt that has been cast upon her company has only encouraged her to ensure other women and people of diverse backgrounds have a chance to enter the fashion world.
“When it comes to leading the company, we’re incredibly diverse. We’re split gender. I never had to think about the DEI piece behind it, because it was naturally built into how Calico was started,” she said.
EMILY GITTINS
CEO and co-founder, Archive
Stage: Series B
Funding total: $54 million
Emily Gittins founded branded resale platform Archive with a mission on her mind: reduce excess waste onslaught by the fashion industry.
“I spent a lot of time looking at new materials and supply chain innovation, but ultimately became convinced that the crux of it is just the sheer volume of new stuff that gets produced every year,” she said. “Fundamentally, that’s driven by brands having their only incentive being to produce revenue from new items, and their business model being 100 percent tied to new production.”
For that reason, she focused Archive on the utilization of existing garments in the apparel and fashion supply chains.
The company, which boasts clients like Lululemon, New Balance and The North Face, uses technology to help brands coordinate their resale business. Its proprietary tools include
I SPENT A LOT OF TIME LOOKING AT NEW MATERIALS AND SUPPLY CHAIN INNOVATION, BUT ULTIMATELY BECAME CONVINCED THAT THE CRUX OF IT IS JUST THE SHEER VOLUME OF NEW STUFF THAT GETS PRODUCED EVERY YEAR.” Emily Gittins, Archive
insight into secondhand inventory, warehouse management software that allows brands to resell damaged returns, and intelligent routing to ensure all items that enter a warehouse can be resold profitably and more.
Gittins didn’t grow up in fashion, and she said that being a founder can be a difficult task. While she doesn’t necessarily notice the difference in being a female founder, she said funding can sometimes be difficult to come by in an industry where women are the primary consumers.
When Gittins looks at the roadmap ahead for Archive, she sees further technical refinement. In addition to the technology Archive has already deployed, the team plans to use the $30 million Series B round it secured this year to further dive into dynamic pricing technology and further scale its existing solutions.
Hardcore technical prowess paired with an uncertain economic environment could further encourage brands and retailers to use resale as a cog in their revenue-driving efforts.
“People aren’t going to do sustainable things for the sake of it—we can only get so far out of the goodness of people’s hearts,” she said. “What we need to do is make this make sense purely from a business perspective, which is what we’ve shown in the last few years, and why I have a lot of excitement about the future.”
JENNY WANG
CEO and founder, Alta
Stage: Seed
Funding total: $10 million
Jenny Wang founded styling app Alta to help users solve an age-old problem: what to wear. Its algorithms are able to help a user style items they already have and suggest new products to purchase based on a user’s natural-language prompt.
The startup recently launched a virtual try-on function that allows users to upload a headshot, creating an AI-generated avatar they can use to see how specific items look on them, based on their style preferences.
Wang said the app helps users solve a classic problem while simultaneously curbing overconsumption by encouraging new types of wear for items users already have in their closets.
“I think of Alta as both utilitarian and also as just fun. It’s utilitarian in the sense that we’re helping you optimize your close utilization, make smarter shopping decisions… but it’s also fun in that it’s a form of selfexpression,” she said.
Alta, which recently minted a partnership
E Emily Gittins
with the Council of Fashion Designers of America (CFDA), uses affiliate links to earn commission off purchases users make through the app; Wang said that, today, Alta has items from more than 4,000 brands on its platform.
Wang said that while AI can prove a maledominated industry and being a solo female technical founder is a challenge, she continues to focus on the goal ahead: building technology to positively impact consumers’ daily lives.
“Our AI has improved so much in the last two years, but I never feel like it’s good enough,” she said. “You could put [a] query into Google, and I don’t think Google would be able to do it, but we shouldn’t benchmark ourselves against the technology that exists. We should always try to push for better, and that’s what I’m excited about.”
As she thinks about the company’s future, she plans to continue building out a technologically savvy team. As AI further develops, she noted, she has her eye on what she calls “multiplayer consumer AI,” which would allow consumers to collaborate and interact with one another on platforms like Alta.
“A lot of consumer AI products right now are single player—like ChatGPT, Midjourney, etc.,” she said. “I’m excited to see what multiplayer consumer AI looks like—how it affects people’s behavior, the way they buy things, the way trends get shared.”
CHERYL LIU
CEO and founder, Raspberry AI
Stage: Series A
Funding total: $28.5 million
Cheryl Liu founded Raspberry AI to bridge the gap between slow design cycles and consumer demand.
The startup uses generative AI to increase design teams’ agility; its platform can help creatives make digital renderings of designs based on sketches that are in early phases, but it also allows teams to enter a written prompt that shares what they’re looking for, then see a digitally rendered asset reflecting their request. Liu said these capabilities help brands shorten lead times and lower costs associated with samples and overstocks on out-of-trend items.
Liu’s company has a variety of clients, including H&M, Revolve, Ramy Brook and Crocs. The company recently expanded its technical solutions to include a tool that allows marketing teams to use generative AI to create imagery for product description pages, social media and other marketing tasks.
Liu said the expansion felt like the natural next step for the business, which already has built the technology needed to render complex images for design and product developmentrelated use cases.
WE SHOULDN’T BENCHMARK OURSELVES AGAINST THE TECHNOLOGY THAT EXISTS. WE SHOULD ALWAYS TRY TO PUSH FOR BETTER.”
Jenny Wang, Alta
“We started in design, and we still believe that’s our core business, but AI can also generate amazing marketing content, and take your designs and put them in the best light possible and make them very appealing, and also help streamline a lot of the photo shooting processes,” Liu told Sourcing Journal.
In 2025, Liu said Raspberry will focus on improving the technology behind its solutions so that its offerings continue to be tailored directly to professionals inside the fashion and apparel industries. To make that happen, the company is planning to leverage some of the $24 million in Series A funding it received to hire across its engineering, product, marketing and design teams.
“There are unique challenges to being a female founder. The most prominent, in my mind, is just the small number of female entrepreneurs who are venture backed and receive the resources that are necessary to build a really compelling business,” she said, noting that it can also be difficult to find other women to look up to in industries where men are disproportionately represented and funded.
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SUPER
SLEUTHS
Some have turned to artificial intelligence to combat theft and fraud both in-store and online. by Meghan Hall
retail’s most frequently cited problems— theft and fraud—have started garnering attention from companies peddling artificial intelligence solutions. c By tackling those issues for brands and retailers, whether in the store or online, service providers said they can help employees feel safer and more confident, preserve brands and retailers’ bottom lines and keep companies in loyal consumers’ good graces. c Six in 10 retail employees have seen theft in their stores, according to December 2024 data from Verkada and the Loss Prevention Research Council. That same survey showed that nearly 70 percent of retail workers worry about theft when entering their workplace. c What’s more, about one-fifth of retail workers have noted they have considered looking for another job because they have concerns over their personal safety in stores.
Experts said implementing AI-powered systems to curb retail theft instances could help associates feel more comfortable at work.
Pedro Ramos, chief revenue officer at Appriss Retail, which uses AI to help clients curb fraud and theft, said AI is well suited to identify patterns, as well as how those patterns evolve over time, whether in stores or online. That, he noted, is because AI can ingest and sort through large amounts of data that a human cannot do with the same speed—and can then analyze and interlink similar instances.
“These applications can create predictive patterns so that the retailer is more capable of taking proactive measures to stop either the safety issue, the loss prevention issue or the retail fraud issue,” Ramos said. “In the past, that required people with detailed analytical skills sitting around, mining through mountains of data, and you couldn’t get two consistent views on the same data.
Ramos said flagging patterns can help retailers better understand how to be proactive and reactive in stores. For instance, if data analyzed by AI shows that crimesters in a specific area are keen to steal luxury handbags, a retailer might lock certain styles up or limit the inventory they put out on the floor. Alternatively, if AI systems can identify that criminals tend to linger in a specific area inside the store, retailers can place security guards in those areas to deter theft.
He anticipates that, in the future, multiple AI systems can join up to communicate about those needs in real time.
“With the right tools in place, one can theoretically envision a future where an AI engine analyzing reams of data and all the activity…could alert a store associate: ‘Please remove 80 percent of the following SKUs off the shelf until we notify you otherwise,’” he said. “That’s hardening the target against outside thefts, and it affects associate behavior.”
Ramos said all the technical capabilities are in place for that kind of change to happen, but noted that the correct systems may not yet be interlinked at many retailers. That could soon happen, though, he said.
With the knowledge that retail employees worry about adverse effects from crime, that kind of alarm system could also extend to safety incidents outside of the store. For instance, an AI system could be set up to scan for weather events, civil unrest or other noteworthy happenings in an area, giving retailers the chance to respond to any issues, even beyond retail crime, in real time.
“Giving those employees the best possible environment is critical,” Ramos said.
The store is far from retail crimesters’ only arena for fraud, though. Increasingly, those perpetuating schemes against retailers have turned to e-commerce. According to Juniper
WITH THE RIGHT TOOLS IN PLACE, ONE CAN THEORETICALLY ENVISION A FUTURE WHERE AN AI ENGINE ANALYZING REAMS OF DATA AND ALL THE ACTIVITY…COULD ALERT A STORE ASSOCIATE.”
Pedro Ramos, Appriss Retail
Research, fraud accounted for $44.3 billion in lost revenue for retailers in 2024, with that figure expected to more than double by 2029.
One of the age-old arguments about retail theft and crime is that perpetrators’ actions negatively impact the at-large consumer by making goods more expensive. Jordan Shamir, co-founder and CEO of startup Yofi, works to help retailers and brands defeat bad actors, digitally and in stores.
He said that the company has started to see an influx of interest from retailers and brands, partly onslaught by the economic uncertainty associated with U.S. President Donald Trump’s strategy on tariffs.
“We’re seeing a lot more interest because this notion of retail shrinkage is not just a small percentage where I can say, ‘Hey, I’m going to hide it in my margin.’ Everyone is struggling—consumers are struggling; retailers are struggling,” Shamir said.
While personalization has long been talked about as a way for brands and retailers to better connect with their customers, it can also be used as a vehicle for discouraging potentially fraudulent transactions or blocking repeat bad actors. Yofi’s technology is set up to help brands and retailers identify and prevent such instances.
The company, which counts JD Sports Canada and footwear company Koio among its clients, has seen a rise in returns fraud, fraudulent customer service claims and more since it set up shop. Shamir said as brands and retailers fret over their economic situation, Yofi has further built out the technological infrastructure necessary to segment customers based on behavior.
“We’ve been diving deeper into bad actor detection and thinking about, what is the profitability of individuals? Not every customer is the same, so not every customer deserves the same benefits, and not every customer deserves the same friction.”
That means that, while a loyal customer who needs to return an item or two may receive instant refunds and free returns, a customer flagged as a fraudster may be required to return an item in store or to wait until the item has been physically inspected at a return center to receive a refund.
That, Shamir said, helps protect retailers’ margins and inventory, but it also provides a stronger experience for responsible shoppers— what Shamir calls “good actors.”
He also said that Yofi can identify legitimate customers who still aren’t causing the brand to gain revenue—that might be, for instance, a shopper who bought six dresses to try on for a wedding and ultimately returned five. In those cases, he said, Yofi might recommend continuing to allow the consumer to buy as normal but ceasing paid advertising to that particular customer.
“Brand experience is becoming way more important, and that’s where the consumer loyalty and the consumer trust is going to start coming in,” he said.
Consumers aren’t the only bad actors that Yofi has successfully identified; in some instances, the company has been able to single out employees breaking company policy or reaping discounts for their own monetary gain. While those incidents are fewer and further between, they aren’t completely unheard of.
On the aggregate, though, Shamir noted that the tools Yofi provides are, by and large, helpful to customer associates trying to do their jobs. With some customers, Yofi has started using technology to route consumer inquiries received through customer service.
The idea is that consumers are assigned a risk score, which can help customer service determine which course of action needs to be taken. Shamir said customer service employees’ roles have, to date, been positively impacted by the technological assist, particularly because fraud can be difficult to detect.
“Low risk will talk to an AI agent; high risk will talk to a human,” he said. “It’s really helpful to the agent and to the consumer. The nice thing is, you can actually now have a ‘we believe you by default’ policy, rather than some brands moving to ‘we don’t believe you by default’ policy. That’s a huge impact for consumers because with this macroeconomic cycle, $10 is a lot more meaningful now than it was a year and a half ago.”
FINANCED FIVE
AI-focused startups continue to ink deals with venture capital firms and angel investors, and many have a pool of retail-focused clients eager to test, assess and deploy the emerging technology. by Meghan Hall
ai funding smashed records in the first quarter of the year, attracting $66 billion, according to data from CB Insights. While that seems like a gargantuan figure, more than $45 billion of that funding went to major players like Anthropic, OpenAI and Safe Superintelligence.
Still, deal count remained somewhat steady, falling about 7 percent quarter over quarter. CB Insights speculates that could mean that deals are increasing in size, even if not increasing in number.
And as AI-focused startups continue to ink deals with venture capital firms and angel investors, many have a pool of retail-focused clients eager to test, assess and deploy the emerging technology. According to Nvidia data
from March, focused on retail and CPG companies, 47 percent of organizations are actively assessing use cases and AI implementation, while 42 percent actively use AI in their operations.
As these tests and deployments take place, companies have dedicated funds to the transformation; Nvidia’s study notes that 97 percent of retail and CPG executives indicated that their companies would increase spending on AI during the 2025 fiscal year.
AI has myriad uses in retail, fashion and apparel—particularly on back-end processes and logistics management. Sourcing Journal actively tracks the fundraising slated to impact industry players. Here are five companies that have bagged cash in the first half of 2025:
1
AUGMENT
San Francisco-headquartered startup Augment has secured a $25 million seed round, led by 8VC, it announced in March.
The startup aims to use AI to solve inefficiencies in the logistics landscape. Its marquis product, which it calls Augie, is an AI-powered assistant that helps freight industry operators automate time-consuming, mundane tasks for greater efficiency and accuracy.
Augie can place and take calls, or send
texts and emails, related to shipment location, issues with deliveries and other related issues. It can also partially automate workflows for truckload (FTL), less-than-truckload (LTL) and drayage shipments. It interacts with human operators via a dashboard and via employees’ own communication tools, like Slack.
Harish Abbott, co-founder and CEO of Augment, said the fundraising round emphasizes investors’ belief in the transformative impact AI could have on the at-large logistics industry when deployed against meaningful use cases.
“We are applying AI to logistics, one of the largest and most complex industries, to drive transformative
change,” said Abbott. “Augie is like an assistant to every operator in the freight industry, Augie performs the tedious and mundane tasks so the operators can focus on the important and urgent.
The startup, which has offices in San Francisco, Chicago and Toronto, plans to use the funds to further build out its logistics-focused platform and to increase headcount on its engineering and customer success teams.
Arrive Logistics, a brokerage, is one of Augment’s first customers. Matt Pyatt, founder and CEO of Arrive, said the technology will help his company continue to provide strong service to its own clients.
“We partnered with Augment to build a multi-functional AI assistant, giving our team another tool to spend more
time on value-added parts of their jobs and delivering a better experience to our partners,” Pyatt said in a statement.
“The Augment team has exceeded our expectations as a partner, shadowing our reps in house, learning the business and building solutions that make sense for our operation.”
2
CONTORO ROBOTICS
Austin, Tex.-based Contoro Robotics announced in March that it had scored a $12 million Series A round for its AI-powered robots that have the capability to unload parcels situated inside trucks and containers.
Investors included Doosan, Coupang, Amazon Industrial Innovation Fund, IMM, SV Investment, KB Investment, Kakao Ventures and Future Play. The company had previously raised $10 million.
Contoro uses sensors and cameras to help guide its robotic arms, which have special gripping capabilities that enable them to suction boxes from the sides, rather than only the top. That, it notes on its site, is particularly useful for moving goods out of containers, where the tops of boxes are typically inaccessible to begin with.
The company plans to use its capital injection to expand into new markets, launch a palletization system and scale its unloading robot fleet. Customers pay a per-container fee to Contoro, rather than a flat rate.
Contoro believes its robotics can help tackle cost and labor challenges in warehouses, distribution centers and fulfillment centers. The robots take a human-in-the-loop approach, which it contends yields higher accuracy and safety.
Mok Yun, CEO and founder of Contoro, said he believes the company’s technology has the power to change workers’ lives for the better.
“Unloading trailers is one of the most physically demanding jobs in the warehouse, yet it remains largely manual,” Yun said in a statement.
“We’re bringing AI-powered automation that enhances reliability, safety and efficiency—allowing warehouse teams to shift from hazardous, repetitive tasks to more strategic and value-added roles.”
THE INDUSTRY HAS REALLY GOTTEN USED TO WHAT THEY SEE AS AN ACCEPTABLE LEVEL OF PAIN. I THINK AI IS GOING TO BLOW THAT UP—NO MORE PAIN.” Mariah Chase, Ekyam
the information. From there, it shares sustainability recommendations for future action, especially related to compliance. The company noted it works with a wide variety of companies, including many mid-sized apparel brands. One of its clients is Reformation, which has consistently worked with startups hawking emerging technologies.
Carrie Freiman Parry, senior director of sustainability at Reformation, said the startup has already helped the brand streamline the mundane pieces of her team’s work.
“Elm AI has been instrumental in transforming our responsible sourcing process, streamlining cumbersome administrative tasks and giving brands more time to focus on what matters most—building programs and management systems that benefit workers by ensuring safe, healthy, and equitable working conditions,” she said in a statement.
Elm AI plans to use its latest funding to upgrade the technology behind its systems and to onboard additional customers off its “extensive waitlist.” As it continues to build, the company hopes to become “the definitive platform for managing comprehensive supplier performance and risk data at global scale.”
4
EKYAM
Eloquii’s founder has been busy cooking up her next big business—and it bagged a $2 million pre-seed round, led by the Female Founders Fund, in March.
The startup aims to provide “a single source of truth for inventory” by leveraging AI-powered systems to bridge gaps in companies’ data streams.
Mariah Chase, co-founder and CEO of Ekyam, said her time spent entrenched in the retail industry showed her that retailers have simply given in to the inevitability of inventory mismatch and lagging data.
But, she countered, it doesn’t have to be that way.
“The industry has really gotten used to what they see as an acceptable level of pain,” she told Sourcing Journal. “I think AI is going to blow that up—no more pain.”
24 hours—all systems can access realtime data on inventory.
The system can be used to inform inventory decisions for e-commerce and in-store operations, depending on which of its systems a retailer gives Ekyam access to interconnect. Ekyam also has an agentic offering, like so many companies vying to grab contracts with brands and retailers today.
“We have AI agents that…can do things like predictive forecasting, order routing, looking for inventory mismatches, doing price comparison, creating business intelligence and reporting dashboards,” she said.
“There’s a lot the agents can do once the data is in a great place and it’s synced on a real-time basis.”
The startup will use the funding to add to its engineering and technology teams, as well as its sales and marketing teams.
5
SEREACT
Sereact, a German startup building AI software for logistics robotics, announced in January that it had raised a €25 million ($26.2 million) Series A round, led by Creandum. The round also saw participation from firms Point Nine and Air Street Capital and angel investors like Mehdi Ghissassi, formerly of Google DeepMind; Rubin Ritter, formerly of Zalando and others.
Sereact’s software is hardware agnostic, which means it should have the ability to integrate with clients’ existing robots and systems. The technology is designed to enable robots to understand, process and adapt to their environments in real time, with little human direction or intervention.
Ralf Gulde, CEO and co-founder of Sereact, said that differentiation sets the company’s technology apart from other providers.
“With our technology, robots act situationally rather than following rigidly programmed sequences. They adapt to dynamic tasks in real-time, enabling an unprecedented level of autonomy,” Gulde said in a statement.
Current clients include Daimler Truck and e-commerce company Bol.
Elm AI, a startup that spun out of Cornell University, announced in April that it had secured $2 million in funding.
Beta Boom Fund and Working Capital Fund led the round, with further support from Boro Capital Partners, Very Serious Ventures, Gorges Ventures, The Bond Collective and Textbook Ventures.
Elm uses AI to ingest supplier documentation, like audits, and analyze
Ekyam’s main goal is to solve that problem with middleware, which connects disjointed, siloed technology systems in a company’s stack and allows data to flow through in real time. That could change the game when it comes to inventory management because it effectively means that the systems can talk to each other with greater ease; rather than receiving updated data every hour—or even every
The company plans to use the funds to support additional types of robots, like humanoids and mobile robots. It will also work to develop AI-based solutions that extend outside the logistics and manufacturing industries.
While much of its business is currently done in Europe, Sereact also plans to use the money to expand further into the U.S. and grow its headcount, though it did not specify by how much or in which business units.
READY TO
as retail supply chains expand and more inventory funnels into warehousing ecosystems, robotics and automation will inevitably take on larger roles in optimizing efficiency, increasing fulfillment accuracy and cutting labor costs. c But despite the benefits, warehouse robotics adoption has been on somewhat of a slow path, with industry leaders carrying the burden of implementing and scaling the technologies across their businesses. c Global warehouse automation orders slowed last year, with a decline of 3 percent in 2024, according to Interact Analysis. c While the technologies keep improving and major players are catching on, economic, political and market-specific factors spurred the decline. This includes high interest rates in many territories, and residual impacts from the oversupply of warehouses built during the Covid-19 pandemic. c Despite the decline, growth is expected to pick up in 2025, which is anticipated to mark a year of slow recovery for the sector. Pre-pandemic growth levels are projected to return in 2026, while longterm expansion is forecast at an 8 percent compound annual growth rate (CAGR) between 2024 and 2030, the firm said. c “We anticipate that warehouse automation investments in the U.S. will increase in 2026 as end-customers settle into the new president’s policies, resulting in an uptick in revenue in 2027,” said Rowan Stott, senior research analyst at Interact Analysis. c Across North America, the demand had been better compared to global counterparts, but essentially flat, according to data released in May by the Association for Advancing Automation (A3). Gains were still slow in the first quarter of 2025, with companies purchasing 9,064 units valued at $580.7 million. c Compared to the year prior, unit orders increased 0.4 percent on a 15 percent rise in order value, signaling an increased investment in higher-value automation systems.
Industry leaders like Amazon, DHL and UPS are driving next-gen automation. by Glenn Taylor
DEPLOY
AMAZON’S ROBOTICS POTENTIAL COULD SAVE
$10 BILLION A YEAR
Amazon unveiled in May that it has developed, produced and deployed more than 750,000 robots across its logistics network, sending a clear message as it cuts supply chain costs that the technology is here to stay, even if it isn’t seen yet.
At this point, Amazon may be the prime example of how retail and supply chain businesses have been able to scale different types of robotics systems across its warehouse operations.
In recent years, the company debuted several new technologies including the Sequoia containerized inventory storage system, Agility Robotics’ Digit humanoid robot and AI-powered robotic arms including Sparrow, Robin and Cardinal.
“Thanks to advancements in AI, these technologies integrate seamlessly, and will help us drive an estimated 25 percent productivity improvement at next-generation fulfillment facilities,” says Scott Dresser, vice president of Amazon Robotics.
The tech titan generated more hype with the unveiling of the Vulcan robot, which Amazon says is its first machine to “have a sense of touch.”
The robot is designed to help employees pick and stow parcels, items and packages at the highest and lowest level of inventory pods, and has a nine-foot-tall reach. The technology could grab items up to five pounds and 14 inches in length to better fit them into onesquare-foot compartments.
Vulcan is built to manipulate objects within those compartments to make room for whatever it’s stowing, because it knows when it makes contact and how much force it’s applying and can stop short of doing any damage.
The technology can pick and stow approximately 75 percent of all various types of items stored at Amazon’s fulfillment centers, and enables employees to work more efficiently in their ergonomic power zone instead of climbing up a ladder or crouching down.
Vulcan and the other robotics technologies are expected to pay further dividends in cutting the price to operate its supply chain.
The e-commerce giant stands to save as much as $10 billion annually if 30 percent to 40 percent of U.S. orders are fulfilled through next-generation robotics facilities by 2030, according to Morgan Stanley estimates.
A May 14 report from Business Insider indicated that the company is angling to soften its reliance on human labor with the mass deployments.
The report cited an internal document indicating the tech is “critical to flattening Amazon’s hiring curve over the next ten years.”
DHL BOLSTERS CASEPICKING IN SCALING 1,000 STRETCH ROBOTS
DHL recently cut a new deal with partner Boston Dynamics to deploy more than 1,000 Stretch robot units across its arsenal of warehouses.
The Stretch robot, initially used by DHL to automate container unloading from the back of trailers to a conveyor, is expected to cover additional use cases with the expansion, including case picking. DHL was the first commercial customer for the Stretch robot.
The robot’s custom-designed, lightweight arm has seven degrees-of-freedom, built for length and flexibility to reach cases throughout
THANKS TO ADVANCEMENTS IN AI, THESE TECHNOLOGIES INTEGRATE SEAMLESSLY, AND WILL HELP US DRIVE AN ESTIMATED 25 PERCENT PRODUCTIVITY IMPROVEMENT AT NEXT-GENERATION FULFILLMENT FACILITIES.” Scott Dresser, Amazon Robotics
the trailer or container. Sensing capabilities enable it to handle a variety of package types and sizes while maximizing pick rates, with the robot capable of working autonomously through situations like mixed stacking configurations and recovering fallen boxes.
The move comes as the company’s contract logistics division, DHL Supply Chain, has invested more than 1 billion euros ($1.1 billion) over the past three years in automation projects as it takes on more outsourcing from other customers.
DHL Supply Chain first began using the Stretch arm in 2023 in North America to help discharge parcels and boxes for customers, with the robot able to unload at rates of up to 700 cases per hour.
Patrick Kelleher, CEO of DHL Supply Chain, told Sourcing Journal in February that the businesses has more than 1,600 technology pilots.
“We take a funneled approach,” Kelleher said, regarding the automation scaling process. “That could be robotics or AI, whatever the case may be, and the ability to scale it. We’re especially good at integrating how the technology works with the individuals who work in our business, so combining the technology element with the human element to deliver and maximize an expected result. If we can see an ROI there, and that comes from confirmation from our people, that a green light to move the pace, to roll technologies out.”
DHL Supply Chain says case picking is one of the most labor-intensive activities within its business, with the segment claiming that Stretch contributes to higher employee satisfaction by reducing the need for physically demanding work.
Across all of DHL, the company uses more than 7,500 robots.
UPS FLIRTS WITH HUMANOID ROBOTS AS IT GOES ‘FULL THROTTLE’ ON AUTOMATION PUSH
UPS has made a concerted effort to move more of its packages through automated warehouses with its Network of the Future reconfiguration, already having leveraged multiple robotics
systems across its logistics network.
The company uses pick-and-place technologies powered by Dexterity, Fortna and Plus One Robotics to help employees sort small packages, as well as technologies from Pickle Robot to make unloading trailers less physically demanding.
Additionally, the company is using autonomous guided vehicles (AGVs) powered by Dane Technologies, Geek+, Locus Robotics, Crown Lift Trucks and Toyota-Raymond, to help warehouse workers more safely and easily move small packages and irregular-sized shipments through facilities.
In April, a report from Bloomberg tied UPS to discussions with robotics startup Figure AI to use humanoid robots within its warehousing network.
The AI-powered humanoid robot operates in the way a human worker would in a warehouse, with Figure AI touting on its website that it is “poised to solve labor shortages.”
For warehouse operators, the humanoid robots are programed for logistics package handling and sorting via the Helix visionlanguage-action model. This enables the robot to track the dynamic flow of numerous packages on a moving conveyor, while maintaining a high throughput.
The system is designed to determine the optimal moment and method for grasping a moving object and reorienting each package to expose its shipping label, which needs to be correctly oriented before scanning.
The company’s latest model, Figure 02, can lift and maneuver up to 55 pounds.
Neither UPS nor rival FedEx have implemented humanoid robots.
UPS wouldn’t confirm the Figure AI talks in its earnings call, Nando Cesaron, president U.S. at UPS, said AI applications would help the company “take costs out of the network.”
“The end result will be a much more efficient operation with less dependency on labor.” Cesaron said candidly, with the logistics provider committing to 20,000 job cuts for 2025.
COULD HUMANOID ROBOTS BE THE FUTURE FOR SUPPLY CHAINS?
Humanoid robots aren’t expected to flood the market rapidly, so don’t expect retail supply chain jobs to evaporate overnight. The robots are likely to have limited uptake in the shortto mid-term, according to Interact Analysis.
The technology market research firm forecasts a baseline scenario for a market size for humanoid robots of $2 billion by 2032, equating to just over 40,000 units shipped in that year. To put this into perspective, just under 200,000 mobile robots in total were shipped in 2024, reaching a market size of $5 billion.
“While the market will grow quickly, market penetration will be very low,” said Marco Wang, research analyst at Interact Research. “Manufacturing and service represent the largest total addressable market, although warehousing will see the greatest penetration given the suitability of workflows.”
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David Wang, robotic software engineer at Amazon, works on a robot prior to a demonstration.
