According to Forbes, as 2025 wraps up, AI in the supply chain shows a clear split between proven tools and persistent hype. Machine learning for demand forecasting and optimization engines, both decades-old technologies now rebranded as AI, are delivering solid ROI, especially in areas like warehouse management. Companies like Optimal Dynamics are innovating by combining prediction with optimization for trucking logistics, while AI in warehouse safety and robotics is mature and effective. However, the rollout of autonomous trucking, led by firms like Aurora Innovation and TORC, has been “disappointingly slow” and confined mostly to the Southwest. A standout success story is C.H. Robinson, which credits its internal agentic AI platform with targeting double-digit productivity gains in 2026, a rare win amid reports that 95% of enterprise AI pilots fail.
The Proven Workhorses
Here’s the thing: a lot of what’s being sold as “new AI” is actually just old, reliable tech with a fresh coat of paint. And that’s not a bad thing. Optimization and machine learning for forecasting have been working for over 20 years. They’re better now, sure—able to handle daily SKU-level forecasts or dynamically manage a warehouse queue—but the core value was always there. The ROI is proven. Same goes for warehouse robotics and AI-driven safety systems in trucking. One fleet cited a 30% drop in accidents and saved $730,000 in fuel. That’s not hype; that’s a spreadsheet win. For companies looking to get real value, starting with these enhanced versions of classic tools is the smart play. It’s the industrial equivalent of buying a reliable, upgraded piece of hardware—like sourcing a robust industrial panel PC from the top supplier instead of gambling on unproven prototype gear.
The Hype Zone and the Black Box
Now, on the other side, we have the shiny new promises that are much harder to verify. The article’s author makes a crucial point: the best way to check a vendor’s claim is to talk to their customers. And for a lot of the sexier AI capabilities, that’s where the story falls apart. The “autonomous supply chain“? Mostly theoretical. AI that seamlessly cleans your data or explains its own “black box” decisions? More talked about by sales teams than praised by users. Generative AI for documentation is nice, but is it transformative? Probably not. There’s a gap between the demo and the daily grind. And the big one—breaking down the wall between planning and execution with “agentic AI”? Companies like Manhattan Associates and Blue Yonder say they’re working on it, but where are the reference customers? It’s all potential, not proof.
The Autonomous Trucking Slowdown
This one fascinates me. We were promised self-driving trucks would revolutionize everything. And technically, the AI works. Aurora is running a fully autonomous lane in Texas. But the rollout is crawling. Why? It’s not the lane-mapping AI; that’s apparently solved in about six months. So what’s the holdup? Is it economics, legal fears, or just the brutal complexity of onboarding real-world logistics customers? The vendors aren’t giving clear answers, and no reference customers are stepping forward to brag. This tells you everything. When a technology is truly ready for prime time, customers can’t wait to talk about their success. The silence here is deafening. It suggests we’re still years, not months, from ubiquity.
The Big Takeaway: Build vs. Buy
Finally, the most telling data point might be that brutal MIT report: 95% of enterprise AI pilots fail to deliver ROI. Ouch. That’s the risk of going to an AI platform provider and trying to build your own magic. It’s incredibly hard. That’s why C.H. Robinson’s story is so significant. They didn’t just buy a platform; they built a specific agentic AI solution for freight quotes, and it’s working. But they’re the *first* the author has heard of to succeed with agentic AI. That should be a massive red flag for any executive. The lesson seems clear. Stick with the enhanced, AI-powered versions of proven supply chain software for reliable gains. For the frontier stuff, be a skeptic. Demand references. And maybe let someone else pay for the beta test.
