According to recent industry research, nearly 9 out of 10 retailers are actively using or piloting AI, and 87 percent report positive revenue impact from those initiatives (Retail Dive - Technology). However, only about one-quarter of retailers have operationalized AI at scale, with breakdowns most often occurring in stores and distribution centers where devices, connectivity, and data reliability matter most (Retail Dive - Technology).
Retailers are deploying AI across demand forecasting, computer vision, loss prevention, personalization, and associate enablement, but success increasingly hinges on operational foundations rather than algorithms. Inventory distortions driven by poor shelf visibility alone cost the global retail industry an estimated $1.7 trillion annually (Retail Dive - Technology). AI systems designed to address these gaps fail when cameras, mobile devices, or networks are unreliable, forcing teams into reactive support models and limiting ROI from AI investments.
Leading retailers are shifting focus from asking what AI can do to whether their operational environment at the edge—where stores, devices, sensors, and associates intersect—can actually sustain it. Addressing this requires disciplined edge operations: choosing the right devices, proactively managing hardware lifecycles, monitoring performance remotely, and integrating security and data governance into everyday operations.