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Smart data capture helps close that gap. It reduces waste by making expiry dates, lot codes, and condition data visible and actionable, and it strengthens traceability at the item and batch level, which is essential for circularity and credible Scope 3 reporting. Just as importantly, capturing information in a structured, machine-readable format at the point of work makes ESG reporting itself faster and more reliable, replacing manual spreadsheets with data you can trust.
Looking ahead but more generally, with your expertise, what do you think is next for the data capture sector? We’ re entering a new phase where data capture becomes far more intelligent, multimodal, and context aware. The sector is moving from simply reading barcodes to understanding the wider physical environment- text, objects, packaging, conditions, and anomalies- all in real time and all on the devices people already use. As AI matures, more intelligence will shift directly onto edge devices, enabling frontline workers and autonomous systems to make accurate, instant decisions without specialized hardware.
We’ ll also see data capture become tightly integrated with workflow automation. Instead of capturing data and acting later, the act of scanning will increasingly drive the next step, such as triggering validations, checks, or instructions immediately. This is part of a broader shift toward what we see as‘ Physical AI’: AI that senses and acts in the real world, not just in digital spaces. The future is software-defined, AI-assisted, and increasingly ubiquitous- where anything with a camera becomes a source of high-quality operational intelligence.
If there was one critical message you’ d like a reader to take from this piece, what would it be? In a world shaped by AI, your output is only as good as your input. You can’ t automate, optimize, or predict anything reliably unless the data coming from the frontline is accurate, timely, and consistent, and workers are empowered with
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