How AI is transforming supply chains

Artificial Intelligence (AI) has evolved from a novelty to a necessity in supply chains.

Companies like LVMH are embedding AI across their entire operations, while fast fashion players use it to accelerate everything from forecasting to logistics. Every day more brands are turning to AI to optimize production planning, predict equipment maintenance and streamline fulfillment processes.

As we’re seeing across thousands of supply chains in our network, AI isn’t just about gaining a competitive advantage anymore; today it’s table stakes.

According to Gartner’s 2025 Supply Chain Symposium, 74% of CEOs believe AI will have the most significant impact on their businesses over the next three years. But it’s critical to understand: your AI is only as good as the data you’re feeding it.

What’s working with AI today?

Every day we’re hearing about new uses of AI in the marketplace:

• Generative AI for scenario planning: Cutting edge retailers are using AI to run “what if” scenarios—simulating demand spikes, supplier issues and logistics constraints through digital twins. Instead of being stuck in reactive mode, they’re testing their responses before disruptions hit.
• Turning waste into wins: Companies are applying AI to optimize returns, cut packaging waste and forecast more precisely to avoid overproduction. Traditional cost centers of waste management and sustainability are becoming margin protection tools.
• Connected automation: From smart warehouses to exception automation, retailers are using AI to reduce manual work and simplify operations. Gartner calls out “connected supply chains” and workflow automation as must-haves for 2025.

How’s your data? A reality check

When it comes to where we see AI working in supply chains, the companies winning with AI aren’t the ones with the fanciest algorithms—they’re the ones with the cleanest, most standardized trading partner data.

And here’s why:

• Your AI may build beautiful supplier disruption models, but if the lead time data is inconsistent, its recommendations are worthless when real problems hit.
• Optimizing returns with AI should work, but without accurate item data from trading partners, AI can’t tell the difference between defects and customer preferences.
• While your customers expect flawless execution, your AI can only deliver if your partner data is consistently accurate across every single relationship.

What do you need for a better data foundation?

Across our retail supply chain network, we see that the companies who successfully apply AI are using standardized, real-time partner data. Without it, AI can’t deliver.

The foundations required for AI implementations include:

• Clean EDI data: AI systems need consistent product info, order acknowledgments and shipment notifications. When this varies across trading partners, your AI models produce unreliable outputs.
• Standardized communications: Exception automation requires partners to communicate disruptions in standard formats. Manual, inconsistent communications break AI workflows every time.
• Real-time visibility: AI lives on current information. You need up-to-the-minute partner feeds, but across diverse trading relationships, most companies can’t maintain them.

When AI ideals meet reality

While new technology is always part of the discussion in modern business, what we’re hearing about AI usage across customers is consistent: Companies start excited about the possibilities of what AI can do for them but quickly realize it won’t work without first standardizing their data.

The dilemma:

• The most sophisticated AI fails if trading partners can’t feed it accurate, timely information.
• Manual exception handling is getting replaced by automated workflows, but only when the underlying data triggers actually work.

Where are we heading?

The future of supply chains may actually be written by AI.

We’re moving toward autonomous systems that respond to disruptions without human intervention: connected ecosystems, with AI orchestrating workflows across all trading partners, and sustainable applications optimizing resource usage.

But how well this works (or not) will depend on if there’s standardized, reliable partner data.

Build your foundation now

The companies who’ll win with AI understand that AI transformation begins with better data. They’re investing in standardized trading partner data formats, real-time partner performance visibility and automated workflows that eliminate manual errors.

AI has incredible potential to transform retail supply chains. But you must have a foundation of clean, standardized and real-time supplier data.

Want to see how leading retailers are preparing their supply chains for the future? Explore our latest insights.

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Matt Brolsma

Sr. Mgr. Product Marketing at SPS Commerce

Matt is a Senior Product Marketing Manager at SPS Commerce, where he excels in aligning product development with market needs. Recognized for his analytical prowess and creative storytelling, Matt drives effective go-to-market strategies that enhance how SPS shows up to retailers, distributors and grocers. Over the last decade, Matt has taken on various roles at SPS, but has remained focused on solving customers' most difficult challenges across the retail supply chain.

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