AI is set to transform the world, and supply chains are no exception. But if that’s the case, why aren’t more retailers seeing the results? (So far.)
Blue Yonder research shows that only 17% of retail supply chain leaders believe that AI is already having a meaningful impact on their business. That’s despite nearly twice that number saying that AI should improve planning and predictability.
Faster/better decision-making was the number two strategic priority for retail supply chain leaders for 2026, up from 7th position in the 2025 survey. The rise of agentic AI may be sharpening the minds of retail supply chain leaders to focus efforts on improving decision speed.
So why is it that fewer than one-fifth of retailers were seeing gains by the start of this year? And how much more is there to come for retailers investing in AI?
The technological limitations that prevent AI success
Fragmented, siloed data
Retail tech stacks are typically a patchwork of point solutions, which creates a problem: each solution has its own view of what’s happening and perfectly aligning them all is a near-impossible task. Retailers with this kind of technology approach are likely to lack a single source of truth on which to base decisions. 52% of retail leaders agree that decision-makers often operate in silos, and no wonder the data they base decisions on is siloed too.
Inconsistent, fragmented data is a major barrier to unifying functions like planning to enable faster decision-making. In turn, that means retailers find it harder to respond to the complex array of challenges they’re facing.
Legacy architecture
A new layer on top of the same infrastructure will not deliver transformative change.
There’s a risk that AI pilots and automation projects result in limited progress because systemic inflexibility won’t allow the more valuable transformative effects of AI technologies.
In the age of electrification, simply switching factories over to electrical power didn’t unlock massively higher productivity. The biggest benefits came in the reorganization of layouts, of workflows and of tools that electrification enabled. Without thinking strategically and broadly about how AI can deliver value, planning teams risk automating the structures that hold them back.
The upgrades retail planning leaders can make to maximize the value of AI
Unifying data
Having a single source of truth for retail planning means huge time savings in manual work to export, fix, import, check and utilize data when it’s moving from one system to another. That time comes back to teams to make valuable strategic decisions with the data.
It also gives them the foundation to adopt AI and harness its power to generate much more accurate forecasts, scale localized assortments, optimize replenishment and inventory management, and more.
Cloud-native is a requirement
Breaking free from the constraints of on-premise architecture is essential to maximizing AI’s potential. Being reliant on a constant cycle of maintenance, expensive upgrades, and downtime-inducing updates makes it impossible for retailers to add new functionality and adopt new workflows quickly.
The cost of each improvement in an on-premise model is vastly higher, and comes with much greater operational disruption. Cloud-native architecture enables continuous improvements, infinite scalability, constant security vigilance and upgrades, and ensures that retailers remain always at the cutting edge with new capabilities as they’re released.
The AI opportunity remains for retailers
The messy and challenging realities of retail technology and operations is not an insurmountable barrier to realizing value from AI. Retailers targeting specific business processes and valuable outcomes can make impressive progress in relatively short timeframes. This more focused approach solves real problems while laying the groundwork for wider data unification, a more platform-based approach, and ultimately a stronger foundation for AI adoption.
