AI is quickly becoming central to logistics and the running of warehouses, predicting breakdowns long before they occur, and routing trucks before the traffic hits. But this all depends on having good data. Automated sorting and smart forecasting are very much downstream from the tracking of assets. Physical assets need to be tracked reliably, starting by capturing asset data at the point of movement. There and then, and systems need to reconfigure in real-time.
How AI is changing supply chain operations
AI as an LLM, as it stands, is undoubtedly suffering from its own hype. But what isn’t underperforming is the use of AI where there’s lots of data, and the output is predictive. Things like social media algorithms. Logistics benefit from having a vast number of data points, along with existing data like maps, which makes route deliveries and stock re-ordering so accurate.
Warehouses are placing supply orders at the right time by being able to predict spikes in demand and monitoring inventory levels.
This is what’s therefore meant supply chain digital transformation. It layers intelligent software on top of physical operations so that decisions are much faster and sharper. The WEF has pointed out that AI is one of the clearest opportunities for improving supply chain visibility heading into a period of ongoing disruption.
The risk of data quality
If you put garbage in, you get garbage out. If a barcode is smudged, a scan gets missed or it goes as unread. The algorithm doesn’t know any better, it will just run the numbers on bad information and spit out a confident but wrong answer.
Predictive maintenance models can flag the wrong machine, while forecasting tools may overstock or understock based on incorrect inventory counts. This is where RFID and AI become inseparable because RFID gives the AI a continuous stream of location and status data rather than relying on someone remembering to scan a pallet at 4pm on a Friday.
A 2025 study published in Issues in Information Systems found that data quality and the interpretability of AI models are some of the key challenges that businesses need to overcome before AI can deliver on its promises.
Why automated data capture matters for smart warehouses
If you think of a warehouse running AGVs, robotic pickers, automated conveyors, which many do in 2026, it becomes obvious that the system wouldn’t rely on a human with a barcode scanner to keep it updated. The data must update itself, and do some continuously.
This is the essence behind automated data capture for smart warehouses. Tags get read automatically as goods move through doors and onto shelves and into trucks. No gaps or delays.
When that data feed is solid, only then is the AI layered on top When it’s not, you get phantom stock or missed shipments. If you’re training your AI on poor data, it may not been effective when the data is eventually improved.
Maintaining industrial edge with data integrity at scale
Industrial environments are very messy, with metal racking, extreme temperatures, dust, forklifts flying fast. None of it is kind to sensors or scanners, yet these are what we are feeding to the AI day in, day out.
Industrial edge data integrity is to have accurate, consistent data that is captured at the point where things actually happen, before it reaches a dashboard. This is what the success of the AI will hinge on.
PwC’s 2025 Digital Trends in Operations survey covered over 600 operations executives and found that integration with existing systems and data quality issues were among the most commonly cited barriers to scaling AI. For a lot of businesses, that’s the whole ballgame right now.
Real-time visibility through smart edge tracking solutions
The fix, other than having great barcodes, scanners, and reliable IoT, is to ensure it’s real-time. Real-time asset-level tracking must be fed into the AI systems continuously, as they arise, not in batches (e.g., not at the end of a shift).
Smart RFID solutions for real-time edge asset tracking reduces the gap between the physical warehouse floor and the digital systems. For example, Brady builds RFID readers designed specifically for this kind of continuous data capture, and their industrial nature makes it less likely to break despite the hostile conditions.
If you’re trying to work out which tools actually suit your operation, the Supply Chain Visibility guide can be used before committing to any platform. Visibility software is only ever as good as the data flowing into it.
AI is changing supply chains both directly and indirectly. It’s encouraging companies to extract even more out of their potential data – not just the quantity and range but the quality. To do this, industrial-grade hardware, like scanners and IoT, are the keys to getting good quality data and lots of it. Real-time data flows are also needed for many operational use cases.
