Walk into any major retailer’s boardroom today and you’ll hear one word more than any other: margin. After years of pandemic disruption, inflation, and bloated inventory cycles, the retail industry is under relentless pressure to squeeze more profit from every dollar of revenue. A new wave of AI startups — and the tech giants quietly powering them — are stepping in with tools that promise to do exactly that. And investors are paying close attention.
The Margin Crisis Driving AI Adoption in Retail
The numbers tell a stark story. The average U.S. apparel retailer returns rate sits between 20% and 30% of all online orders, according to data from the National Retail Federation. Each returned item costs retailers an estimated $25 to $30 to process, ship back, and restock — or simply write off as damaged. For a mid-size e-commerce brand doing $500 million in annual revenue, that’s a nine-figure drag on profitability each year.
Add in the supply chain whiplash of the past four years — from pandemic overstock to 2026’s energy-driven freight cost spikes tied to Strait of Hormuz disruptions — and it becomes clear why retail CFOs are urgently hunting for technology that can structurally lower operating costs rather than just paper over them.
This urgency has supercharged investment in AI tools targeting two specific pain points: reducing return rates through virtual try-on technology, and cutting inventory waste through AI-driven demand forecasting.
Virtual Try-On: Turning Browsers Into Buyers
Virtual try-on (VTO) technology uses augmented reality (AR) and generative AI to let shoppers see how clothing, eyewear, footwear, and cosmetics look on their own bodies or faces before hitting “Add to Cart.” The premise is straightforward: if a customer is more confident the item will fit and flatter, they’re less likely to return it.
Early results from deployments at major brands suggest the technology delivers. Warby Parker reported that customers who used its virtual glasses try-on feature converted at meaningfully higher rates than those who browsed product photos alone. Several cosmetics brands using AI-powered shade matching tools reported return rates dropping by 15% to 25% in controlled deployments, according to company presentations at the 2025 National Retail Federation Big Show.
The key publicly traded beneficiaries of this shift include:
- Snap Inc. (SNAP) — Snap’s AR shopping platform, built on its Lens technology, powers virtual try-on experiences for hundreds of brands. The company has positioned AR commerce as a core monetization pillar, with over 300 million daily active users providing a massive testing ground for retail AR.
- Amazon.com (AMZN) — Amazon’s “Virtual Try-On for Shoes” and “Room Decorator” AR tools are embedded directly in its shopping app. With its unmatched catalog scale, Amazon is well-positioned to turn VTO from a feature into a competitive moat for fashion and home goods categories.
- Shopify (SHOP) — Through its partnership with AR technology providers and its own AR Quick Look integration, Shopify enables merchants to offer 3D and AR product visualization, making it the infrastructure layer beneath thousands of independent brands’ VTO capabilities.
AI Demand Forecasting: Killing the Inventory Glut
The second major AI application reshaping retail economics is demand forecasting. Traditional retail inventory planning relied on spreadsheets, historical sales data, and human judgment — a system famously ill-suited to the volatility of modern consumer behavior. The result: chronic overstock in some categories and stockouts in others, both of which destroy margin.
Modern AI forecasting systems ingest hundreds of variables simultaneously — weather patterns, social media sentiment, competitor pricing, local event calendars, and real-time point-of-sale data — to generate more accurate demand signals at the SKU and store level.
Walmart has publicly credited its AI demand forecasting investments with meaningfully reducing inventory shrinkage, a key contributor to its consistently strong gross margin performance even in challenging macro environments. Target, in contrast, suffered highly publicized inventory missteps in 2022 that cost the company over $1.5 billion in markdowns — a cautionary tale that accelerated AI adoption across the entire industry.
Key players in the AI-for-retail infrastructure space beyond the retailers themselves include Microsoft (MSFT), whose Azure cloud platform powers AI workloads for numerous retail clients, and Salesforce (CRM), which has embedded AI forecasting capabilities directly into its Commerce Cloud product.
The “Silent Killer” Dynamic: What It Means for Legacy Retailers
CNBC reported this week that AI startups deploying these tools have been described as “silent killers” by industry insiders — a nod to the way the competitive advantage they provide compounds quietly but devastatingly over time. A retailer operating with a 3% gross margin advantage from lower returns and tighter inventory management may not look dramatically different in year one, but over a five-year period, that edge translates into significantly greater capacity to invest in marketing, pricing, and customer acquisition.
This dynamic creates a bifurcated investment thesis. Retailers that successfully integrate AI margin tools may see earnings power grow even in a soft consumer environment. Those that lag — particularly mid-tier department store chains already squeezed by Amazon from above and fast fashion from below — face structural erosion that is difficult to reverse.
Analysts at Morgan Stanley estimated in a late-2025 research note that AI-enabled retailers could see gross margin improvements of 150 to 300 basis points over three to five years, a material difference in an industry where a 30% gross margin is considered healthy and 25% is survival territory.
Risks Worth Watching
The AI retail revolution is not without friction. Consumer privacy concerns around AR and facial recognition technology remain a genuine regulatory risk, particularly in California and the European Union, where legislators have shown appetite for restricting biometric data collection in commercial contexts.
There is also the question of implementation cost. Enterprise-grade AI demand forecasting systems from vendors like Blue Yonder (owned by Panasonic) or o9 Solutions carry significant integration and licensing costs that smaller retailers may struggle to absorb. This could widen the gap between well-capitalized chains and independent merchants — a dynamic worth monitoring for investors in retail-focused ETFs like the SPDR S&P Retail ETF (XRT) or the Amplify Online Retail ETF (IBUY).
Finally, AI technology is only as good as the data fed into it. Retailers with fragmented legacy systems and poor data hygiene may find that deploying AI tools yields disappointingly modest results — a risk that has not been lost on institutional investors who are increasingly asking management teams to demonstrate measurable AI ROI rather than simply announcing AI initiatives.
The Bottom Line
The AI transformation of retail is neither hype nor fully priced in — it is a genuine structural shift that is quietly repricing the competitive landscape. For investors, the clearest near-term opportunities appear to sit in the infrastructure layer: companies like Snap, Shopify, Amazon, and Microsoft that provide the AR and AI platforms retailers are adopting at scale. Longer term, the retailers that most successfully operationalize these tools — and can demonstrate the margin improvement in their income statements — may emerge as the surprise outperformers of the decade.
As with any technology-driven thesis, the timeline matters as much as the direction. The AI retail revolution is real, but execution risk, consumer adoption curves, and regulatory headwinds mean the winners will be separated from the also-rans over years, not quarters.
Disclosure: This article was produced with AI assistance and reviewed before publication. It is for informational purposes only and is not investment advice.