Here on Industrial IoT News (and on IoT Evolution + Future of Work News), we’ve dedicated a ton of coverage to the steep proliferation of AI, especially since early last year. As you’d expect, a sweeping amount of it has spot-lit massive spikes in AI adoption rates, sprawling cross-industry use cases, partnerships that have led to specific GenAI integrations, updates on calls for transparent AI regulations; the list goes on.
Of course, we’ve also covered many gaps in our increasingly AI-everything world; inherent trust issues (i.e. ethics concerns, AI training/prompting biases, misinformation dangers, etc.), as well as various AI risk profiles and smarter actionable insights that are gainable when users and organizations alike are making better-informed AI decisions.
The long-story-short of it? If we optimize further, we close more gaps.
So, let’s talk about them for a minute.
According to a recent study conducted by HERE Technologies (developed in part with AWS and YouGov), there is a “significant gap in the adoption of basic data analytics and AI, along with a lack of sustainability mindfulness and progress toward achieving real-time supply chain visibility.” The multi-country study (i.e. the U.S., the U.K. and Germany) surveyed 901 transportation and logistics (T&L) professionals – roughly 300 from each country – and explored AI trends, implementation barriers, sustainable goals and more.
By the numbers:
- Nearly three out of four surveyed professionals in the U.S., the U.K. and Germany (ranging between 68-72% of them) believe their company is “making some progress toward achieving real-time supply chain visibility,” but less than one in four of them believe they’ve made “significant progress” (even without factoring in additional automatable processes).
- “Approximately 50%,” per the study, “of T&L professionals state their organizations utilize basic data analytics in their operations” and only 25% claim they’re leveraging AI. This underscores untapped potential; from ML-supported analytics advancements to fleet routing, predictive maintenance and better decisioning optimized by AI.
- The most common barriers stopping organizations from implementing AI for supply chain optimization included costs (23%), being wary of disruption to existing/functioning services (12%) and a lack of internal expertise (11%).
Per Remco Timmer, Vice President of Product Management at HERE Technologies:
“On one hand, this study shows the progress being made by companies towards increasing their supply chain visibility. But on the other hand, it’s clear the industry currently lacks the contextual data, AI capabilities and tools needed to optimize fleet deployments, routing, and appropriate mode switching. As a result, we’re seeing increased demand for location data and services that enable logistics companies to overcome disruptions in real-time while reducing emissions and improving employee safety in the process.”
Learn more about here.
Edited by
Greg Tavarez
