Why operational configurability is emerging as a critical factor in moving from AI pilots to real-world outcomes.
Logistics providers are being sold an AI project and calling it a strategy.
The goal was always to run a better operation. Faster quotes. Faster answers. Better carrier performance. Higher margins. Better customer experiences.
I know that goal from the inside. I ran and consulted the world’s best LSP for many years before I built technology for the industry. Depending on who sat in the operator’s seat, the same load could produce a different experience for the customer and a different result for margin. That experience shaped what we set out to build in 2014, when the work still ran on phone calls, faxes, and EDI.
Today I see many AI deployments start the same way. Pick a workflow. Build an agent. Connect it to the TMS, carrier portals, and inbox. Test it. Fix what breaks. Repeat.
Logistics changes every day.
Customers update routing guides. Carriers change processes. Facilities adjust operating hours. Capacity shifts. Weather disrupts networks.
An AI workflow built for a fixed set of assumptions begins aging the moment it goes live.
That is one of the biggest obstacles to AI adoption in logistics. The models are smart enough. The automation works. The obstacle is rigidity.
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The First Half of 2026: What’s Changed, What’s Worked, and What’s Next
The first half of 2026 has been anything but predictable. Tariffs, geopolitical tensions, AI advancements, changing transportation market conditions, and other developments have created new challenges — and new opportunities — for supply chain organizations. As we reach the midpoint of the year, we’d like to understand what has had the greatest impact on your organization, what has surprised you, where you’ve made the most progress, and what priorities will shape the months ahead.
Latest Talking Logistics Episode:
Predictive Safety: What Sets the Best Fleets Apart
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