Risk has always been part of the supply chain. But now it’s moving faster, hitting harder, and leaving less room for error. Simultaneously, AI, real-time data, and advanced sensors are giving logistics professionals unprecedented visibility into how (and why) things go wrong.
Most supply chains already have the hardware. GPS. Telematics. IoT devices. But operational clarity still lags. In a 2024 survey by Vinturas, according to Logistics Manager, 97 percent of respondents said they experienced blind spots in their operations. Nearly 40 percent linked those blind spots to poor data quality or weak interpretation. Sensors can tell you where a shipment is. They can tell you if it was too hot, arrived too late, or took a different route. But without the proper context, those facts don’t translate into insight, and they don’t reduce risk.
Where the impact is most visible is in the insurance industry. Claims and underwriting are becoming faster, more precise, and harder to dispute. AI models built on cross-loss histories and real-time telemetry can reconstruct incidents with the same level of detail we didn’t have even five years ago, such as vessel speed, environmental conditions, battery levels, and cargo temperatures. This changes what’s possible. A claims team no longer has to wait for a report. An underwriter no longer has to price based on averages. Risk is becoming traceable.
What these tools require, beyond an endless stream of investment, is fluency. Not just in using the dashboards, but in understanding how the data is generated, where it comes from, and what it means legally and operationally. Claims professionals, risk managers, and underwriters are expected to operate across functions. They need to collaborate with IT teams to verify the sources. They need to question anomalies, not just flag them. A 2025 GARP report found that 71 percent of logistics firms now require data analysis skills for roles that didn’t involve tech five years ago.
As data becomes central to logistics decision-making, with new legislation like the EU Data Act and expanding regulatory pressure in the U.S, consequently, the governance gaps are becoming harder to ignore. Who owns the telematics data in a subcontracted fleet? What happens when a sensor contradicts a manual report? What rights do clients have to audit that data?
The goal isn’t to eliminate risk because that’s not possible. The goal is to know more, act faster, and respond with clarity. That only happens when data systems and human judgment work together.
AI isn’t replacing risk professionals, but it’s giving them better material to work with. But only if they’re equipped to handle it. AI systems now surface real-time insights that once took days or weeks to uncover: patterns in claims data, deviations in transit behavior, subtle indicators of system failure. Insight, though, is only valuable if the person interpreting it knows how to validate it, apply it, and recognize its limits.
So, this is where the real shift is occurring, not in the tech stack, but in the expectations placed on the human side of the system. Risk professionals must evolve from case managers to data-informed strategists. They need to be fluent not only in operational workflows, but in how the data that underpins them is structured, governed, and modeled.