We are syndicating this article from Global Trade Magazine.
Written by Tim Jay. Attributed to Ashley Burkle, Director, Sales and Business Development at Identiv
Why is high-quality, real-time data becoming so critical for AI to deliver meaningful results in supply chains?
Historically, AI applications in supply chains have relied primarily on retrospective datasets and predictive modeling. While these approaches remain valuable for forecasting and planning, they are inherently limited to retrospective analysis and probabilistic forecasting.
Today’s operating environments require a more immediate and dependable foundation. Technologies such as RAIN RFID, BLE-enabled sensing, and secure IoT infrastructure form the backbone of trusted data capture, using authenticated tags and inlays to ensure that each product or asset carries a verifiable digital identity as it moves through the supply chain networks.
As AI becomes embedded within core transportation, warehouse, and visibility systems, the integrity of the physical identity foundation becomes a determining factor in performance. Reliable, authenticated asset data enables organizations to transition from retrospective analysis to real-time operational intelligence to support faster decisions and greater resilience across complex supply networks.
How are IoT and digital identification technologies changing the type of data AI models can access and learn from?
IoT and digital identification technologies like BLE and RFID are fundamentally expanding the type of data available to AI systems. Instead of relying solely on ERP entries or scanned checkpoints, organizations can now capture real-time signals directly from products and assets.
That includes location, dwell time, environmental condition, tamper events, and verified chain-of-custody data. Each asset carries a digital identity that travels with it, generating a continuous stream of ground-truth information.
As a result, AI models are no longer limited to periodic updates, but instead can analyze persistent, real-world asset data as events unfold. This changes the AI’s role from forecasting likely outcomes to interpreting and potentially problem solving live operational patterns – identifying disruptions, bottlenecks, and risk as they emerge, rather than after they’ve already affected performance.
What does “Physical AI” mean in the context of supply chains?
In supply chains, “Physical AI” represents a shift to AI systems that learn from and respond to the physical world in real time.
Supply chains are particularly well suited for Physical AI because they involve constant physical interaction – storage in varied environments, transfer of custody between partners, and exposure to temperature, humidity, or handling risk. When those interactions are captured through persistent digital identity and condition-aware sensing, teams can act on authenticated physical-world signals in the moment, rather than reconciling discrepancies days later through manual audits or customer complaints.
In this model, AI becomes an operational layer, continuously interpreting verified asset behavior and enabling decisions that reflect actual conditions across the network.
What measurable operational advantages do companies gain when AI models are trained on real-time physical-world data rather than historical records alone?
Measurable advantages appear in the performance indicators supply chain leaders already monitor. For example, improving real-time inventory accuracy by even one percentage point can translate into over $1M+ in annual financial value by lower carrying costs associated with excess inventory and avoided lost revenue from stockouts for mid-sized operations.
Organizations are increasingly recognizing that achieving those outcomes requires shifting investment away from simply adding more platforms or predictive models and toward strengthening ground-truth data at the physical layer.
For example, BLE-enabled sensing and RFID-based digital identification provide persistent, real-time sensing of location and environmental conditions, effectively transforming physical assets into continuous data sources. In cold chain food movement and storage, real-time visibility into temperature conditions and asset location enables immediate response to excursions, which can reduce spoilage, limit waste, and protect revenue that would otherwise be lost to compromised goods and downstream disruptions.
On a broader level, when AI operates on authenticated, real-time physical signals, the result is a supply chain that is not only more visible, but measurably more responsive, accountable, and resilient.
How should organizations think about BLE and RFID applications when building a data foundation for AI? Are they competing or complementary technologies?
BLE and RFID are best understood as complementary technologies that together form a durable identity layer for AI-driven supply chains. Each serves a distinct purpose within a broader, interoperable data architecture.
Together, RFID, BLE, and secure IoT infrastructure create the foundational data layer that AI systems depend on. Authenticated tags and inlays ensure that products and assets carry persistent, verifiable digital identities as they move through the network. That persistent identity allows information to remain consistent across ERP, WMS, TMS, yard management, labor systems, and IoT platforms.
This layered approach reflects how these technologies are deployed across real environments. RFID is typically used at the item level, enabling scalable identification of inventory, while BLE is applied to assets or shipments where continuous location and condition monitoring is required.
In a warehouse setting, inventory may be tagged with RFID for high-volume tracking, while equipment or transport assets are equipped with BLE to provide real-time visibility. Cold chain scenarios, for example, require individual items such as seafood to carry RFID tags for identity and traceability, while the containing crate is fitted with BLE to monitor temperature and environmental conditions throughout transit. Unified data models rely on these persistent digital identifiers to maintain consistency across systems and lifecycle stages.
Integrated properly, both technologies feed AI systems with authenticated, persistent data streams that improves model reliability, and reduces blind spots.
What should supply chain leaders prioritize now to ensure their AI investments actually deliver long-term value?
As AI shifts from experimental tools to embedded operating infrastructure, organizations need to examine the integrity of the data feeding those systems.
For many applications, that means strengthening the physical identity layer by ensuring assets are consistently identified, authenticated, and tracked with persistent digital identities from origin to destination. It also means designing infrastructure that captures real-time movement and condition data in a way that is scalable, secure, and interoperable across enterprise platforms.
AI can only optimize what it can reliably see. Leaders who focus on reinforcing data foundations in addition to factors like expanding algorithmic capability will be better positioned to deploy automation confidently.
In the end, the competitive advantage won’t come as a result of having the most sophisticated models; it will come from having the most trustworthy data available at the point of decision.
