The next phase of AI deployment isn’t happening in the office, and it’s not about grabbing headlines with ambitious claims about the latest model. It’s happening quietly but steadily in the world’s major industries, where edge AI and edge data are connecting the frontline.
It’s where businesses meet the physical world, and where a torrent of valuable data is generated every second. Enterprises that rely on physical operations should focus their vision for AI on the point of work, where small improvements can deliver operational and financial results, and there are key opportunities for transformation.
The real-world environments of manufacturing and logistics are where operational value is won or lost. It’s a key area of value creation and nexus of activity, where goods are produced, assets are tracked, and customer promises are kept. It is here that AI will deliver a deep and durable impact. Zebra’s study with Oxford Economics found that workflow automation can increase productivity by around 20%, with the greatest leverage from better integration between data, IT and operational technology (OT).
A solid foundation for operational success will be laid by those companies that embed real-time intelligence directly into the hands of its frontline workers. Competitive advantage will be unlocked by harnessing data from the edge through on-device and multimodal AI. This practical, secure, and cost-effective reality is already remaking frontline operations.
From the cloud to the edge: A necessary shift
Sending vast amounts of frontline data to the cloud for analysis can be inefficient and, for some real-time applications, unworkable. This can particularly be the case for high-resolution video for quality control or constant streams from sensors. The concept of data gravity comes into play. The sheer volume and velocity of data generated at the edge make it impractical and costly to move.
This approach introduces unacceptable latency for the immediate decisions required in a dynamic operational environment. An operator on a production line, a quality control engineer, or a driver on a delivery route cannot afford the latency of waiting for a response from a centralised cloud server, a delay which is compounded by both physical distance and unpredictable network congestion.
Solving problems, delivering benefits
Edge AI can help overcome these problems by processing data directly where it is created on a mobile computer or vision controller. This on-device approach offers several fundamental benefits:
- Quality and assurance: Among manufacturing leaders who have improved their quality and assurance processes, 58% cited machine learning and 32% cited AI as important factors in their success, according to Zebra’s study with Oxford Economics. These businesses are using AI machine vision running on smart cameras, sensors and vision controllers to elevate their quality inspection processes for the most complex use cases.
- Security and privacy: It keeps sensitive information, like proof-of-delivery images or proprietary manufacturing data, on the device itself. This minimises the attack surface for cyber threats and helps ensure data control and compliance with privacy regulations.
- Cost and reliability: It eliminates the need for constant, high-bandwidth connectivity to the cloud, saving significant data transmission costs and ensuring critical operations can continue uninterrupted, even in areas with poor or no signal.
- Speed and performance: It delivers the instantaneous analysis required for in-the-moment decision-making. Sub-second responses are essential for high-throughput picking and sorting environments.
Intelligence in action: Real examples
On-device multimodal AI creates a genuine breakthrough of intelligence in action. It is best understood as the fusion of multiple data inputs and outputs, like sight, sound, and text, on a single device. It gives the frontline worker the context-aware intelligence to perceive their environment, creating a powerful, real-time feedback loop between the digital and physical worlds.
A delivery driver taking a picture for proof of delivery, manually checking for barcodes, and typing in notes can now take a single photo with an AI-enabled mobile computer. With an AI blueprint for proof of delivery, multimodal AI instantly reads QR and barcodes, automatically blurs personal information to ensure privacy and compliance in photos, and confirms the package was left at the correct location.
An operator in a manufacturing warehouse tasked with picking hundreds of items and boxes per day can point their mobile device at a shelf and look at the augmented reality and AI-enabled screen to know what needs to be picked, what’s inside each box, and whether it needs special handling. Workflows that involved multiple steps become a single, instantaneous action.
Proof of transformation
The era of intelligent operations has arrived, and the path there starts by identifying concrete frontline workflows where intelligent automation can deliver immediate, measurable impact. Unlocking this potential demands a focus on robust data management and embedding intelligence into every action a frontline team performs, empowering them to make better decisions, faster. The organisations that win will be those that make work better every day with the transformative and practical power of data and AI at the edge.
Learn more here.
About the author
Stephan Pottel, Manufacturing Strategy Director EMEA, Zebra Technologies.
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