How AI is shaping retail media, ecommerce and manufacturing workflows

AI in retail and manufacturing is moving from pilot to practice. In the past year, companies across commerce and industry have launched targeted capabilities — from retail media integrations on social platforms to generative try-on tools and mobile shop-floor apps. Therefore, leaders must understand what these moves mean for marketing, customer experience, and operations. This post breaks down five recent developments, explains practical impacts, and offers a short view of what comes next.

## Retail media meets social platforms: AI in retail and manufacturing

Retail media is evolving fast, and this shift matters for both brand owners and retailers. Instacart announced a direct integration with TikTok that lets advertisers target and measure campaigns inside TikTok’s ad platform. This is notable because retail media has traditionally lived inside a retailer’s walled garden. However, by linking Instacart’s retail media data to TikTok ads, brands can more directly connect social creative to grocery purchases. Therefore, marketers get clearer signals about which social content drives conversion.

This integration also signals a new model for partnerships. Instead of separate systems for social ads and retail measurement, the workflow becomes more unified. Additionally, it lowers friction for brands that want to test creative on TikTok and see downstream sales without stitching together multiple data sources. For retailers, the move protects their advertising inventory while opening new avenues for ad revenue. Looking ahead, expect more retail platforms to seek deep links with major social ad ecosystems. That will push advertisers to measure performance across screens and touchpoints. In short, integrated retail media on social platforms sharpens targeting and simplifies measurement — and it raises the bar for marketing teams that want to show clear business outcomes.

Source: Marketing Dive

Programmatic speed and efficiency: AI in retail and manufacturing

Programmatic advertising is getting an AI upgrade, and the benefits are practical. PubMatic reported that its partnership with Nvidia has improved automated programmatic buying, delivering results up to five times faster than older systems. That improvement is not just about speed. Faster decision cycles mean ad buys can react more quickly to market signals, inventory shifts, and creative tweaks. Therefore, advertisers can optimize performance with fewer delays and less manual intervention.

For enterprise marketers, speed translates into efficiency and cost control. Additionally, faster programmatic systems can support more sophisticated bidding strategies and higher-frequency testing. This matters for retail brands running time-sensitive promotions or inventory-driven campaigns. However, speed alone is not enough. Teams must pair faster systems with clear measurement and governance. Otherwise, rapid decisions can amplify mistakes. Furthermore, publishers and platforms need to ensure that faster processing does not sacrifice transparency or data privacy.

Looking forward, expect other ad-tech vendors to highlight AI-driven throughput and latency improvements. Meanwhile, marketers should prioritize use cases where quick programmatic reactions add clear value—such as flash sales, dynamic pricing hooks, or real-time creative optimization. In short, AI-driven programmatic speed helps bridge creative testing and measurable sales, while requiring disciplined oversight.

Source: Marketing Dive

Generative try-on and personalization: Stitch Fix Vision’s impact

Generative AI is reshaping how customers preview products online. Stitch Fix introduced Stitch Fix Vision, a tool that lets shoppers preview curated head-to-toe outfits. The feature blends the retailer’s customer data with generative AI to create realistic try-on experiences. Therefore, shoppers can see ensemble recommendations rather than single items, which helps with confidence and reduces friction in purchase decisions.

This move highlights two important trends. First, personalization is moving beyond recommendations to visual proof. When customers can imagine themselves in an outfit, they are likelier to convert. Second, retailers with deep customer data have an advantage. Stitch Fix combines styling expertise and user history with AI generation, which helps keep recommendations relevant and on-brand. Additionally, generative try-ons can reduce returns by setting clearer expectations about fit and look.

However, execution matters. Retailers must manage privacy and accuracy. Also, they should integrate try-on features into catalog and inventory systems so experiences reflect what’s actually available. Looking ahead, generative try-on will expand from clothes to accessories and even room-scale merchandising for home goods. For brands, the practical win is clearer: better visualization drives engagement, and engagement can become measurable sales when tied to inventory-aware systems.

Source: Digital Commerce 360

AI across the enterprise: Lessons from Nike

Large brands are treating AI as a cross-functional tool, and Nike provides a useful example. The company is applying AI across customer service, brand representation, and product optimization as it works to improve digital sales. Therefore, AI is not a single project at Nike; it’s a capability that touches marketing, product, and operations. This approach helps align investments with measurable outcomes, such as improved service response or better product-market fit.

For other enterprises, Nike’s approach suggests a practical playbook. First, prioritize use cases that impact key business metrics. Second, centralize learning so teams can reuse models, data practices, and governance. Additionally, brand representation via AI — from creative generation to chat — must be monitored to protect identity and voice. Nike’s moves show that AI can support both front-end customer experiences and back-end optimization. However, success requires a balance: speed and innovation alongside controls for quality and brand safety.

As companies scale AI, expect more emphasis on operationalizing models and connecting them to commerce systems. For managers, the lesson is clear: treat AI as an operating capability. Do not silo it. Instead, embed it into workflows where it can move metrics. That way, AI becomes a lever for recovery and growth, not just a technology experiment.

Source: Digital Commerce 360

Operations and mobile tools for manufacturers

AI and digital platforms also matter on the shop floor. Xometry launched a mobile app that extends its workcenter platform, helping suppliers manage production and job orders from their phones. This is practical, not flashy: manufacturers often need quick access to orders, updates, and job details while on the line. Therefore, a mobile-first tool reduces delays and keeps teams aligned across shifts.

Digitally connecting work orders to mobile devices speeds communication and can cut downtime. Additionally, when mobile apps integrate with scheduling and inventory systems, they help manufacturers react faster to changes. For small and mid-sized suppliers, a mobile app lowers the barrier to adopt digital workflows. However, success depends on usability and integration. Teams must ensure that data from mobile interactions flows back into planning and billing systems. Otherwise, the app becomes a silo.

Looking ahead, mobile platforms could pair with AI to recommend tooling, predict delays, or auto-route jobs based on capacity. For now, Xometry’s release shows that digital marketplaces and manufacturers are focusing on practical tools that simplify daily work. That improves responsiveness and, ultimately, customer satisfaction.

Source: Digital Commerce 360

Final Reflection: The future of AI in retail and manufacturing

AI in retail and manufacturing is no longer a promise — it’s becoming operational. From Instacart’s retail media link to TikTok to PubMatic’s faster programmatic buys, the pattern is clear: companies are connecting AI capabilities to measurable business workflows. Additionally, generative tools like Stitch Fix Vision show how visualization can close the gap between interest and purchase. Meanwhile, enterprise examples such as Nike demonstrate the value of a cross-functional AI strategy, and Xometry’s mobile app highlights the importance of practical tools on the shop floor.

Therefore, leaders should focus on three priorities. First, connect AI features to real metrics — sales, returns, cycle time. Second, partner across platforms to maintain measurement and control. Third, scale thoughtfully with governance to protect brand and data. With those pillars, AI can deliver tangible gains in marketing, customer experience, and operations. The near-term future will be iterative: small wins will compound into larger advantages as systems and teams learn together. Overall, the companies taking pragmatic steps today will be best positioned to turn AI experiments into durable value.