On National Logistics Day, Zebra Technologies highlights a logistics industry ‘ambition versus action’ gap when it comes to improving customer experience and operational improvements for deliveries.
According to Zebra’s Impact of Intelligent Operations study with Oxford Economics, while logistics leaders are eager to adopt AI to improve operational efficiency (48%) and customer experience (34%), there’s a significant gap between ambition and achievement. A striking 62% of these leaders admit that improvements are critically needed in delivery and field operations, where they have yet to make meaningful progress.
“Bridging this gap is an opportunity for logistics leaders to elevate the role of the frontline, where customer transactions and trust are often won or lost,” said Phil Sambrook, Transport and Logistics Strategy Director EMEA, Zebra Technologies.
From visibility to intelligence: how frontline AI prevents losses
Everything from lost, late, and misplaced parcels to the highly publicised theft of twelve tonnes of chocolate bars in Italy shows the need for advanced inventory management and delivery operations improvements, as supply chain complexity and e-commerce grows.
“The chocolate manufacturer could trace every single stolen bar because of unique codes on each package, effectively turning every retail scanner into a security checkpoint,” said Sambrook. “Advanced data capture is an essential foundation for frontline AI.”
A torrent of data is generated by digitising the physical inventory, machines, and working spaces layer with technologies like RFID and machine vision. On-device multimodal AI then interprets this data in real time, fusing inputs from scanners, cameras, and sensors to create ambient intelligence. These systems continuously detect, interpret, and surface actionable insights, elevating loss prevention from a reactive process to one of true, proactive resiliency.
Bridging the gap
Sambrook cited the need for a strategic approach where technology-driven transformation and improvement is enabled with the right culture, learning investments and senior leader support.
“Implementing AI is more than an IT upgrade. It requires changes to workplace culture and learning resources to ensure adoption and success.”
The strategic shift toward Edge AI follows a series of high profile on-device AI launches this year, including Zebra’s Frontline AI Suite of Enablers, Blueprints, and Companion AI agents. The company counts a growing number of leading industry customers in logistics already using or piloting the Suite this year.
Advances in on-device AI models, device security, and hardware equipped with the latest CPUs and NPUs designed for Edge AI, bring its capabilities to deskless workers who interact with customers on a daily basis.
“By delegating resource-heavy tasks to on-device AI, warehouse and delivery jobs can be made less of a grind and more of a craft,” said Sambrook. “This elevates productivity and helps employees feel valued by their leaders and engaged in their work for customers.”
A bridge between middle and last mile
Turning to the middle mile, Sambrook pointed to logistics leaders in Europe solving longstanding problems with AI-enabled machine vision inspection solutions fitted over conveyor systems. The system can distinguish true jams from false alerts, eliminating costly stoppages, increasing throughput, and saving time so shoppers get their orders on time.
Other AI machine vision solutions inspect package integrity in real time, checking for damage, leaks, or missing labels before an item proceeds further down the supply chain or as part of returns management.
Beyond packages and conveyors, frontline workers are using AI-enabled mobile computers to capture multiple barcodes on a pallet or shelf in a single scan. Computer vision and augmented reality guide picking tasks by overlaying information on the device screen, instantly showing which item to pick next and even revealing the contents of a sealed box.
Outside the four walls, AI picture proof of delivery automates the entire chain of custody, by capturing and validating drop-off photos, reading barcodes, and redacting personal data for privacy, in a single action. In customer environments, this has been shown to accelerate the proof of delivery workflow by 55% per stop and has contributed to a 10 to 30% reduction in annual claims costs.
