Unisys executive examines what’s working (and what’s not) with AI in logistics

Supply Chain 24/7: You mentioned that the peak of the “AI hype has run its course.” Can you elaborate on what you're hearing from logistics leaders about AI expectations versus reality?

Sean Tinney: One of the biggest challenges we’re facing is the distributed nature of the supply chain. Many companies imagined AI as a unifying layer — something that could seamlessly connect subcontractors, delivery partners, distribution centers, and so on. However, in reality, that vision hinges on a fully digitized and interconnected ecosystem that doesn’t yet exist at scale.

AI is being used, but mostly in isolated, tactical ways — in customer service, for example, with chat capabilities, order verification, and shipment tracking. What’s not yet happening is end-to-end operational transformation. Initiatives like synchronized planning, dynamic capacity building and real-time visibility across the entire network are still out of reach for most companies.

Why? Because it’s not just about one company’s data environment — it’s about their partners’ and their partners’ partners. Every link in the chain has different levels of digital maturity, infrastructure, and priorities. That makes proper AI integration extremely complex.

Logistics leaders are also grappling with where to begin. Add limited budgets, competing IT initiatives, and real-world constraints like aging assets or manual processes, and it’s easy to see why there’s some skepticism. The promise of AI-driven operations is still there, but getting there requires a lot more foundational work than the early hype suggested.

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